




import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.albert.modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		)

class 						__lowerCAmelCase      (      unittest.TestCase					):
							def __init__(		self		:str						, __magic_name__		:str						, __magic_name__		:Dict=13						, __magic_name__		:Any=7						, __magic_name__		:int=True						, __magic_name__		:List[Any]=True						, __magic_name__		:List[str]=True						, __magic_name__		:str=True						, __magic_name__		:str=99						, __magic_name__		:Any=32						, __magic_name__		:str=5						, __magic_name__		:List[Any]=4						, __magic_name__		:int=37						, __magic_name__		:List[str]="gelu"						, __magic_name__		:Any=0.1						, __magic_name__		:Tuple=0.1						, __magic_name__		:List[str]=512						, __magic_name__		:Optional[Any]=16						, __magic_name__		:str=2						, __magic_name__		:str=0.02						, __magic_name__		:str=4						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				seq_length
								a									=				is_training
								a									=				use_attention_mask
								a									=				use_token_type_ids
								a									=				use_labels
								a									=				vocab_size
								a									=				hidden_size
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				intermediate_size
								a									=				hidden_act
								a									=				hidden_dropout_prob
								a									=				attention_probs_dropout_prob
								a									=				max_position_embeddings
								a									=				type_vocab_size
								a									=				type_sequence_label_size
								a									=				initializer_range
								a									=				num_choices
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size							)

								a									=				None
								if self.use_attention_mask:
									a									=				random_attention_mask([self.batch_size, self.seq_length]							)

								a									=				None
								if self.use_token_type_ids:
									a									=				ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size							)

								a									=				AlbertConfig(
								    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, is_decoder=__magic_name__						, initializer_range=self.initializer_range						, )

								return config, input_ids, token_type_ids, attention_mask





							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				self.prepare_config_and_inputs()
								a							,					a							,					a							,					a									=				config_and_inputs
								a									=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict







@require_flax
class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    (
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				FlaxAlbertModelTester(self							)





							@slow
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								for model_class_name in self.all_model_classes:
									a									=				model_class_name.from_pretrained("""albert-base-v2"""							)
									a									=				model(np.ones((1, 1)							)							)
									self.assertIsNotNone(__magic_name__							)







@require_flax
class 						__lowerCAmelCase      (      unittest.TestCase					):
							@slow
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				FlaxAlbertModel.from_pretrained("""albert-base-v2"""							)
								a									=				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
								a									=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
								a									=				model(__magic_name__						, attention_mask=__magic_name__							)[0]
								a									=				(1, 11, 768)
								self.assertEqual(output.shape						, __magic_name__							)
								a									=				np.array(
								    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]							)

								self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						, __magic_name__						, atol=1E-4							)							)







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)

