




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      A_			      =		None


A_			      =		logging.get_logger(__name__)


A_			      =		{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A_			      =		{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

A_			      =		{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
A_			      =		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






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

     A_											=			[]
     A_											=			[]





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					List[Any]="<s>"     ,							__lowerCamelCase     :					str="</s>"     ,							__lowerCamelCase     :					List[Any]="</s>"     ,							__lowerCamelCase     :					List[str]="<s>"     ,							__lowerCamelCase     :					Optional[int]="<unk>"     ,							__lowerCamelCase     :					List[Any]="<pad>"     ,							__lowerCamelCase     :					Optional[int]="<mask>"     ,							__lowerCamelCase     :					Union[str, Any]=None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					str=None     ,							**__lowerCamelCase     :					Union[str, Any]     ,							):
           # Mask token behave like a normal word, i.e. include the space before it
           snake_case__       :   Optional[Any]	      =    AddedToken(__lowerCamelCase     ,							lstrip=__lowerCamelCase     ,							rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     ,							__lowerCamelCase			) else mask_token

           super().__init__(
               vocab_file=__lowerCamelCase     ,							tokenizer_file=__lowerCamelCase     ,							bos_token=__lowerCamelCase     ,							eos_token=__lowerCamelCase     ,							sep_token=__lowerCamelCase     ,							cls_token=__lowerCamelCase     ,							unk_token=__lowerCamelCase     ,							pad_token=__lowerCamelCase     ,							mask_token=__lowerCamelCase     ,							src_lang=__lowerCamelCase     ,							tgt_lang=__lowerCamelCase     ,							additional_special_tokens=__lowerCamelCase     ,							**__lowerCamelCase     ,							)

           snake_case__       :   str	      =    vocab_file
           snake_case__       :   int	      =    False if not self.vocab_file else True

           snake_case__       :   Union[str, Any]	      =    FAIRSEQ_LANGUAGE_CODES.copy()

           if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 _additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

           self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
           snake_case__       :   Optional[Any]	      =    {
               lang_code: self.convert_tokens_to_ids(__lowerCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           snake_case__       :   Tuple	      =    src_lang if src_lang is not None else 'en_XX'
           snake_case__       :   Any	      =    self.convert_tokens_to_ids(self._src_lang			)
           snake_case__       :   str	      =    tgt_lang
           self.set_src_lang_special_tokens(self._src_lang			)





     @property
     def 			_lowerCAmelCase      (     self     :					Any			):
           return self._src_lang





     @src_lang.setter
     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					str			):
           snake_case__       :   Optional[int]	      =    new_src_lang
           self.set_src_lang_special_tokens(self._src_lang			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					List[int]     ,							__lowerCamelCase     :					Optional[List[int]] = None			):
           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					List[int]     ,							__lowerCamelCase     :					Optional[List[int]] = None			):
           snake_case__       :   str	      =    [self.sep_token_id]
           snake_case__       :   Union[str, Any]	      =    [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep			) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str]     ,							__lowerCamelCase     :					Optional[str]     ,							**__lowerCamelCase     :					str			):
           if src_lang is None or tgt_lang is None:
                 raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
           snake_case__       :   Optional[Any]	      =    src_lang
           snake_case__       :   Optional[int]	      =    self(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							return_tensors=__lowerCamelCase     ,							**__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    self.convert_tokens_to_ids(__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    tgt_lang_id
           return inputs





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					str = "en_XX"     ,							__lowerCamelCase     :					Optional[List[str]] = None     ,							__lowerCamelCase     :					str = "ro_RO"     ,							**__lowerCamelCase     :					List[str]     ,							):
           snake_case__       :   int	      =    src_lang
           snake_case__       :   Any	      =    tgt_lang
           return super().prepare_seqaseq_batch(__lowerCamelCase     ,							__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str			):
           return self.set_src_lang_special_tokens(self.src_lang			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           return self.set_tgt_lang_special_tokens(self.tgt_lang			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   List[Any]	      =    self.convert_tokens_to_ids(__lowerCamelCase			)
           snake_case__       :   Any	      =    []
           snake_case__       :   Dict	      =    [self.eos_token_id, self.cur_lang_code]

           snake_case__       :   str	      =    self.convert_ids_to_tokens(self.prefix_tokens			)
           snake_case__       :   Union[str, Any]	      =    self.convert_ids_to_tokens(self.suffix_tokens			)

           snake_case__       :   str	      =    processors.TemplateProcessing(
               single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,							pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,							self.prefix_tokens + self.suffix_tokens			)			)     ,							)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					str			):
           snake_case__       :   str	      =    self.convert_tokens_to_ids(__lowerCamelCase			)
           snake_case__       :   List[Any]	      =    []
           snake_case__       :   str	      =    [self.eos_token_id, self.cur_lang_code]

           snake_case__       :   Any	      =    self.convert_ids_to_tokens(self.prefix_tokens			)
           snake_case__       :   Dict	      =    self.convert_ids_to_tokens(self.suffix_tokens			)

           snake_case__       :   Union[str, Any]	      =    processors.TemplateProcessing(
               single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,							pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,							self.prefix_tokens + self.suffix_tokens			)			)     ,							)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str] = None			):
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                     'tokenizer.'			)

           if not os.path.isdir(__lowerCamelCase			):
                 logger.error(F"Vocabulary path ({save_directory}) should be a directory."			)
                 return
           snake_case__       :   Dict	      =    os.path.join(
               __lowerCamelCase     ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

           if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			):
                 copyfile(self.vocab_file     ,							__lowerCamelCase			)

           return (out_vocab_file,)






'''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)
