






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       @register_to_config
       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = False    ,     ):
         super().__init__()

         UpperCamelCase_:   Optional[int]        =							nn.Embedding(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							nn.Embedding(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							False

         UpperCamelCase_:   Optional[int]        =							nn.Dropout(p=_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							TaConfig(
             vocab_size=_lowerCamelCase    ,     d_model=_lowerCamelCase    ,     num_heads=_lowerCamelCase    ,     d_kv=_lowerCamelCase    ,     d_ff=_lowerCamelCase    ,     dropout_rate=_lowerCamelCase    ,     feed_forward_proj=_lowerCamelCase    ,     is_decoder=_lowerCamelCase    ,     is_encoder_decoder=_lowerCamelCase    ,     )

         UpperCamelCase_:   List[str]        =							nn.ModuleList()
         for lyr_num in range(_lowerCamelCase				):
           UpperCamelCase_:   Tuple        =							TaBlock(_lowerCamelCase				)
           self.encoders.append(_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							TaLayerNorm(_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							nn.Dropout(p=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[Any]        =							self.token_embedder(_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							encoder_input_tokens.shape[1]
         UpperCamelCase_:   Optional[Any]        =							torch.arange(_lowerCamelCase    ,     device=encoder_input_tokens.device				)
         x += self.position_encoding(_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							self.dropout_pre(_lowerCamelCase				)

         # inverted the attention mask
         UpperCamelCase_:   Dict        =							encoder_input_tokens.size()
         UpperCamelCase_:   str        =							self.get_extended_attention_mask(_lowerCamelCase    ,     _lowerCamelCase				)

         for lyr in self.encoders:
           UpperCamelCase_:   Optional[Any]        =							lyr(_lowerCamelCase    ,     _lowerCamelCase				)[0]
         UpperCamelCase_:   Union[str, Any]        =							self.layer_norm(_lowerCamelCase				)

         return self.dropout_post(_lowerCamelCase				), encoder_inputs_mask





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
__UpperCamelCase   :			Tuple          = "."

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

	with open(os.path.join(__lowerCamelCase				,     """utils"""				,     """tf_ops"""				,     """onnx.json"""       )       ) as f:
		a									=				json.load(__lowerCamelCase       )["""opsets"""]

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

	with open(__lowerCamelCase				,     """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(__lowerCamelCase       )
	a									=				[]

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

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


if __name__ == "__main__":
		__UpperCamelCase   :			Optional[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)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

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

