




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
							from ...onnx.config import PatchingSpec
							from ...tokenization_utils_base import PreTrainedTokenizerBase


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Dict          =  {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """longformer"""

						def __init__(		self :Optional[int]     ,			__snake_case :Union[List[int], int] = 5_12     ,			__snake_case :int = 2     ,			__snake_case :int = 1     ,			__snake_case :int = 0     ,			__snake_case :int = 2     ,			__snake_case :int = 3_05_22     ,			__snake_case :int = 7_68     ,			__snake_case :int = 12     ,			__snake_case :int = 12     ,			__snake_case :int = 30_72     ,			__snake_case :str = "gelu"     ,			__snake_case :float = 0.1     ,			__snake_case :float = 0.1     ,			__snake_case :int = 5_12     ,			__snake_case :int = 2     ,			__snake_case :float = 0.02     ,			__snake_case :float = 1E-12     ,			__snake_case :bool = False     ,			**__snake_case :List[str]     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			**__snake_case			)

										__magic_name__						:						int										=attention_window
										__magic_name__						:						Dict										=sep_token_id
										__magic_name__						:						Dict										=bos_token_id
										__magic_name__						:						Tuple										=eos_token_id
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						Dict										=hidden_size
										__magic_name__						:						Any										=num_hidden_layers
										__magic_name__						:						Union[str, Any]										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						int										=intermediate_size
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						List[str]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[int]										=max_position_embeddings
										__magic_name__						:						Dict										=type_vocab_size
										__magic_name__						:						int										=initializer_range
										__magic_name__						:						Optional[Any]										=layer_norm_eps
										__magic_name__						:						int										=onnx_export




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :Optional[Any]     ,			__snake_case :"PretrainedConfig"     ,			__snake_case :str = "default"     ,			__snake_case :"List[PatchingSpec]" = None			):
										'''simple docstring'''

										super().__init__(__snake_case     ,			__snake_case     ,			__snake_case			)
										__magic_name__						:						Optional[Any]										=True

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						Dict										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						List[str]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""global_attention_mask""", dynamic_axis),
										    ]			)

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=super().outputs
										if self.task == "default":
														__magic_name__						:						List[str]										={0: """batch"""}
										return outputs

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 1E-4

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return max(super().default_onnx_opset     ,			14			)


						def      A__    (		self :int     ,			__snake_case :"PreTrainedTokenizerBase"     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=super().generate_dummy_inputs(
										    preprocessor=__snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)
										import torch

										# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
										# makes the export fail randomly
										__magic_name__						:						List[Any]										=torch.zeros_like(inputs["""input_ids"""]			)
										# make every second token global
										__magic_name__						:						Optional[int]										=1

										return inputs









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_      :      Optional[int]    =     '.'

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






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

 with open(os.path.join(__UpperCAmelCase							,  '''utils'''							,  '''tf_ops'''							,  '''onnx.json'''       )       ) as f:
  a__												= json.load(__UpperCAmelCase       )['''opsets''']

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

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

 a__												= 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
 a__												= sorted(__UpperCAmelCase       )
 a__												= []

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

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


if __name__ == "__main__":
     a_      :      Dict    =     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_      :      str    =     parser.parse_args()

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





