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import json
from typing import Any, Dict, List, Union
from swarms.utils.lazy_loader import lazy_import_decorator
from pydantic import BaseModel
from swarms.tools.logits_processor import (
NumberStoppingCriteria,
OutputNumbersTokens,
StringStoppingCriteria,
)
from swarm_models.base_llm import BaseLLM
from swarms.utils.auto_download_check_packages import (
auto_check_and_download_package,
)
try:
import transformers
except ImportError:
auto_check_and_download_package(
"transformers", package_manager="pip"
)
import transformers
GENERATION_MARKER = "|GENERATION|"
@lazy_import_decorator
class Jsonformer:
"""
Initializes the FormatTools class.
Args:
model (PreTrainedModel): The pre-trained model.
tokenizer (PreTrainedTokenizer): The tokenizer for the model.
json_schema (Dict[str, Any]): The JSON schema.
prompt (str): The prompt for generation.
Keyword Args:
debug (bool, optional): Whether to enable debug mode. Defaults to False.
max_array_length (int, optional): The maximum length of an array. Defaults to 10.
max_number_tokens (int, optional): The maximum number of tokens for numbers. Defaults to 6.
temperature (float, optional): The temperature for generation. Defaults to 1.0.
max_string_token_length (int, optional): The maximum length of a string token. Defaults to 10.
"""
value: Dict[str, Any] = {}
def __init__(
self,
model: transformers.PreTrainedModel = None, # type: ignore
tokenizer: transformers.PreTrainedTokenizer = None, # type: ignore
json_schema: Union[Dict[str, Any], BaseModel] = None,
schemas: List[Union[Dict[str, Any], BaseModel]] = [],
prompt: str = None,
*,
debug: bool = False,
max_array_length: int = 10,
max_number_tokens: int = 6,
temperature: float = 1.0,
max_string_token_length: int = 10,
llm: BaseLLM = None,
):
self.model = model
self.tokenizer = tokenizer
self.json_schema = json_schema
self.prompt = prompt
self.llm = llm
self.schemas = schemas
self.number_logit_processor = OutputNumbersTokens(
self.tokenizer, self.prompt
)
self.generation_marker = "|GENERATION|"
self.debug_on = debug
self.max_array_length = max_array_length
self.max_number_tokens = max_number_tokens
self.temperature = temperature
self.max_string_token_length = max_string_token_length
def generate_number(
self, temperature: Union[float, None] = None, iterations=0
):
"""
Generates a number based on the given prompt.
Args:
temperature (float, optional): The temperature value for number generation. Defaults to None.
iterations (int, optional): The number of iterations for generating a valid number. Defaults to 0.
Returns:
float: The generated number.
Raises:
ValueError: If a valid number cannot be generated after 3 iterations.
"""
if self.model:
prompt = self.get_prompt()
self.debug("[generate_number]", prompt, is_prompt=True)
input_tokens = self.tokenizer.encode(
prompt, return_tensors="pt"
).to(self.model.device)
response = self.model.generate(
input_tokens,
max_new_tokens=self.max_number_tokens,
num_return_sequences=1,
logits_processor=[self.number_logit_processor],
stopping_criteria=[
NumberStoppingCriteria(
self.tokenizer, len(input_tokens[0])
)
],
temperature=temperature or self.temperature,
pad_token_id=self.tokenizer.eos_token_id,
)
response = self.tokenizer.decode(
response[0], skip_special_tokens=True
)
response = response[len(prompt) :]
response = response.strip().rstrip(".")
self.debug("[generate_number]", response)
try:
return float(response)
except ValueError:
if iterations > 3:
raise ValueError(
"Failed to generate a valid number"
)
return self.generate_number(
temperature=self.temperature * 1.3,
iterations=iterations + 1,
)
elif self.llm:
prompt = self.get_prompt()
self.debug("[generate_number]", prompt, is_prompt=True)
response = self.llm(prompt)
response = response[len(prompt) :]
response = response.strip().rstrip(".")
self.debug("[generate_number]", response)
try:
return float(response)
except ValueError:
if iterations > 3:
raise ValueError(
"Failed to generate a valid number"
)
return self.generate_number(
temperature=self.temperature * 1.3,
iterations=iterations + 1,
)
elif self.llm and self.model:
raise ValueError("Both LLM and model cannot be None")
def generate_boolean(self) -> bool:
"""
Generates a boolean value based on the given prompt.
Returns:
bool: The generated boolean value.
"""
if self.model:
prompt = self.get_prompt()
self.debug("[generate_boolean]", prompt, is_prompt=True)
input_tensor = self.tokenizer.encode(
prompt, return_tensors="pt"
)
output = self.model.forward(
input_tensor.to(self.model.device)
)
logits = output.logits[0, -1]
# todo: this assumes that "true" and "false" are both tokenized to a single token
# this is probably not true for all tokenizers
# this can be fixed by looking at only the first token of both "true" and "false"
true_token_id = self.tokenizer.convert_tokens_to_ids(
"true"
)
false_token_id = self.tokenizer.convert_tokens_to_ids(
"false"
)
result = logits[true_token_id] > logits[false_token_id]
self.debug("[generate_boolean]", result)
return result.item()
elif self.llm:
prompt = self.get_prompt()
self.debug("[generate_boolean]", prompt, is_prompt=True)
output = self.llm(prompt)
return output if output == "true" or "false" else None
else:
raise ValueError("Both LLM and model cannot be None")
def generate_string(self) -> str:
if self.model:
prompt = self.get_prompt() + '"'
self.debug("[generate_string]", prompt, is_prompt=True)
input_tokens = self.tokenizer.encode(
prompt, return_tensors="pt"
).to(self.model.device)
response = self.model.generate(
input_tokens,
max_new_tokens=self.max_string_token_length,
num_return_sequences=1,
temperature=self.temperature,
stopping_criteria=[
StringStoppingCriteria(
self.tokenizer, len(input_tokens[0])
)
],
pad_token_id=self.tokenizer.eos_token_id,
)
# Some models output the prompt as part of the response
# This removes the prompt from the response if it is present
if (
len(response[0]) >= len(input_tokens[0])
and (
response[0][: len(input_tokens[0])]
== input_tokens
).all()
):
response = response[0][len(input_tokens[0]) :]
if response.shape[0] == 1:
response = response[0]
response = self.tokenizer.decode(
response, skip_special_tokens=True
)
self.debug("[generate_string]", "|" + response + "|")
if response.count('"') < 1:
return response
return response.split('"')[0].strip()
elif self.llm:
prompt = self.get_prompt() + '"'
self.debug("[generate_string]", prompt, is_prompt=True)
response = self.llm(prompt)
# Some models output the prompt as part of the response
# This removes the prompt from the response if it is present
if (
len(response[0]) >= len(input_tokens[0])
and (
response[0][: len(input_tokens[0])]
== input_tokens
).all()
):
response = response[0][len(input_tokens[0]) :]
if response.shape[0] == 1:
response = response[0]
self.debug("[generate_string]", "|" + response + "|")
if response.count('"') < 1:
return response
return response.split('"')[0].strip()
else:
raise ValueError("Both LLM and model cannot be None")
def generate_object(
self, properties: Dict[str, Any], obj: Dict[str, Any]
) -> Dict[str, Any]:
for key, schema in properties.items():
self.debug("[generate_object] generating value for", key)
obj[key] = self.generate_value(schema, obj, key)
return obj
def generate_value(
self,
schema: Dict[str, Any],
obj: Union[Dict[str, Any], List[Any]],
key: Union[str, None] = None,
) -> Any:
schema_type = schema["type"]
if schema_type == "number":
if key:
obj[key] = self.generation_marker
else:
obj.append(self.generation_marker)
return self.generate_number()
elif schema_type == "boolean":
if key:
obj[key] = self.generation_marker
else:
obj.append(self.generation_marker)
return self.generate_boolean()
elif schema_type == "string":
if key:
obj[key] = self.generation_marker
else:
obj.append(self.generation_marker)
return self.generate_string()
elif schema_type == "array":
new_array = []
obj[key] = new_array
return self.generate_array(schema["items"], new_array)
elif schema_type == "object":
new_obj = {}
if key:
obj[key] = new_obj
else:
obj.append(new_obj)
return self.generate_object(schema["properties"], new_obj)
else:
raise ValueError(
f"Unsupported schema type: {schema_type}"
)
def generate_array(
self, item_schema: Dict[str, Any], obj: Dict[str, Any]
) -> list:
if self.model:
for _ in range(self.max_array_length):
# forces array to have at least one element
element = self.generate_value(item_schema, obj)
obj[-1] = element
obj.append(self.generation_marker)
input_prompt = self.get_prompt()
obj.pop()
input_tensor = self.tokenizer.encode(
input_prompt, return_tensors="pt"
)
output = self.model.forward(
input_tensor.to(self.model.device)
)
logits = output.logits[0, -1]
top_indices = logits.topk(30).indices
sorted_token_ids = top_indices[
logits[top_indices].argsort(descending=True)
]
found_comma = False
found_close_bracket = False
for token_id in sorted_token_ids:
decoded_token = self.tokenizer.decode(token_id)
if "," in decoded_token:
found_comma = True
break
if "]" in decoded_token:
found_close_bracket = True
break
if found_close_bracket or not found_comma:
break
return obj
elif self.llm:
for _ in range(self.max_array_length):
# forces array to have at least one element
element = self.generate_value(item_schema, obj)
obj[-1] = element
obj.append(self.generation_marker)
input_prompt = self.get_prompt()
obj.pop()
output = self.llm(input_prompt)
found_comma = False
found_close_bracket = False
for token_id in output:
decoded_token = str(token_id)
if "," in decoded_token:
found_comma = True
break
if "]" in decoded_token:
found_close_bracket = True
break
if found_close_bracket or not found_comma:
break
return obj
def get_prompt(self):
template = """{prompt}\nOutput result in the following JSON schema format:\n{schema}\nResult: {progress}"""
progress = json.dumps(self.value)
gen_marker_index = progress.find(
f'"{self.generation_marker}"'
)
if gen_marker_index != -1:
progress = progress[:gen_marker_index]
else:
raise ValueError("Failed to find generation marker")
prompt = template.format(
prompt=self.prompt,
schema=json.dumps(self.json_schema),
progress=progress,
)
return prompt
def __call__(self) -> Dict[str, Any]:
self.value = {}
generated_data = self.generate_object(
self.json_schema["properties"], self.value
)
return generated_data
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