





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
lowerCAmelCase							:       Dict					 =	'.'

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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Union[str, Any]					)      ->    str:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   SavedModel()
						__SCREAMING_SNAKE_CASE:				int           =   []

						with open(os.path.join(__UpperCAmelCase       ,			'''utils'''       ,			'''tf_ops'''       ,			'''onnx.json'''					)					) as f:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   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()					)

						__SCREAMING_SNAKE_CASE:				int           =   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
						__SCREAMING_SNAKE_CASE:				List[Any]           =   sorted(__UpperCAmelCase					)
						__SCREAMING_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(__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__":
							lowerCAmelCase							:       List[Any]					 =	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)"""
							)
							lowerCAmelCase							:       str					 =	parser.parse_args()

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









import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs