




'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


A_			      =		logging.get_logger(__name__)

A_			      =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A_			      =		{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

A_			      =		{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			VOCAB_FILES_NAMES
     A_											=			PRETRAINED_VOCAB_FILES_MAP
     A_											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_											=			["input_ids", "attention_mask"]
     A_											=			GPTaTokenizer





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]=None     ,							__lowerCamelCase     :					List[str]=None     ,							__lowerCamelCase     :					Union[str, Any]=None     ,							__lowerCamelCase     :					Any="<|endoftext|>"     ,							__lowerCamelCase     :					str="<|endoftext|>"     ,							__lowerCamelCase     :					Any="<|endoftext|>"     ,							__lowerCamelCase     :					Optional[Any]=False     ,							**__lowerCamelCase     :					Dict     ,							):
           super().__init__(
               __lowerCamelCase     ,							__lowerCamelCase     ,							tokenizer_file=__lowerCamelCase     ,							unk_token=__lowerCamelCase     ,							bos_token=__lowerCamelCase     ,							eos_token=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							**__lowerCamelCase     ,							)

           snake_case__       :   Tuple	      =    kwargs.pop('add_bos_token'     ,							__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                 snake_case__       :   List[str]	      =    getattr(__lowerCamelCase     ,							pre_tok_state.pop('type'			)			)
                 snake_case__       :   int	      =    add_prefix_space
                 snake_case__       :   Dict	      =    pre_tok_class(**__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    add_prefix_space





     def 			_lowerCAmelCase      (     self     :					int     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					int			):
           snake_case__       :   str	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)
           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					Tuple			):
           snake_case__       :   int	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str] = None			):
           snake_case__       :   List[Any]	      =    self._tokenizer.model.save(__lowerCamelCase     ,							name=__lowerCamelCase			)
           return tuple(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					"Conversation"			):
           snake_case__       :   Optional[int]	      =    []
           for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			) + [self.eos_token_id]			)

           if len(__lowerCamelCase			) > self.model_max_length:
                 snake_case__       :   List[str]	      =    input_ids[-self.model_max_length :]
           return input_ids






'''simple docstring'''






import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_			      =		"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
A_			      =		[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

      for i in range(1				,      opset + 1		):
            onnx_ops.extend(onnx_opsets[str(__SCREAMING_SNAKE_CASE		)]		)

      with open(__SCREAMING_SNAKE_CASE				,      'rb'		) as f:
            saved_model.ParseFromString(f.read()		)

      snake_case__       :   List[str]	      =    set()

      # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
      for meta_graph in saved_model.meta_graphs:
            # Add operations in the graph definition
            model_op_names.update(node.op for node in meta_graph.graph_def.node		)

            # Go through the functions in the graph definition
            for func in meta_graph.graph_def.library.function:
                  # Add operations in each function
                  model_op_names.update(node.op for node in func.node_def		)

    # Convert to list, sorted if you want
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

      for op in model_op_names:
            if op not in onnx_ops and op not in INTERNAL_OPS:
                  incompatible_ops.append(__SCREAMING_SNAKE_CASE		)

      if strict and len(__SCREAMING_SNAKE_CASE		) > 0:
            raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops		)
      elif len(__SCREAMING_SNAKE_CASE		) > 0:
            print(f"Found the following incompatible ops for the opset {opset}:"		)
            print(*__SCREAMING_SNAKE_CASE				,      sep='\n'		)
      else:
            print(f"The saved model {saved_model_path} can properly be converted with ONNX."		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
      )
      parser.add_argument(
          "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
      )
      parser.add_argument(
          "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
      )
      A_			      =		parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)
