






import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=5    ,		_UpperCamelCase=4    ,		_UpperCamelCase=4    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=True    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=3    ,		_UpperCamelCase=4    ,		_UpperCamelCase=None    ,		)     ->							List[str]:
							UpperCAmelCase_   :				str						=						parent
							UpperCAmelCase_   :				Union[str, Any]						=						batch_size
							UpperCAmelCase_   :				List[str]						=						seq_length
							UpperCAmelCase_   :				Optional[Any]						=						is_training
							UpperCAmelCase_   :				Optional[Any]						=						use_input_mask
							UpperCAmelCase_   :				Optional[int]						=						use_token_type_ids
							UpperCAmelCase_   :				Dict						=						use_labels
							UpperCAmelCase_   :				Any						=						vocab_size
							UpperCAmelCase_   :				Dict						=						hidden_size
							UpperCAmelCase_   :				Optional[Any]						=						num_hidden_layers
							UpperCAmelCase_   :				Any						=						num_attention_heads
							UpperCAmelCase_   :				Optional[int]						=						intermediate_multiple_size
							UpperCAmelCase_   :				Union[str, Any]						=						hidden_act
							UpperCAmelCase_   :				Union[str, Any]						=						hidden_dropout
							UpperCAmelCase_   :				Tuple						=						attention_dropout
							UpperCAmelCase_   :				Tuple						=						weight_tying
							UpperCAmelCase_   :				str						=						max_position_embeddings
							UpperCAmelCase_   :				str						=						type_vocab_size
							UpperCAmelCase_   :				Optional[Any]						=						type_sequence_label_size
							UpperCAmelCase_   :				Tuple						=						initializer_range
							UpperCAmelCase_   :				str						=						num_labels
							UpperCAmelCase_   :				List[str]						=						num_choices
							UpperCAmelCase_   :				Optional[int]						=						scope



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size       )

							UpperCAmelCase_   :				List[str]						=						None
							if self.use_input_mask:
												UpperCAmelCase_   :				Union[str, Any]						=						random_attention_mask([self.batch_size, self.seq_length]       )

							UpperCAmelCase_   :				Optional[int]						=						None
							if self.use_labels:
												UpperCAmelCase_   :				Tuple						=						ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels       )

							UpperCAmelCase_   :				Optional[int]						=						self.get_config()

							return config, input_ids, input_mask, token_labels



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return GPTNeoXJapaneseConfig(
							    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_multiple_size=self.intermediate_multiple_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout=self.hidden_dropout    ,		attention_dropout=self.attention_dropout    ,		weight_tying=self.weight_tying    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		is_decoder=_UpperCamelCase    ,		initializer_range=self.initializer_range    ,		)



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self.prepare_config_and_inputs()

							UpperCAmelCase_   :				List[str]						=						True

							return config, input_ids, input_mask, token_labels



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						GPTNeoXJapaneseModel(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				List[Any]						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						model(_UpperCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				str						=						True
							UpperCAmelCase_   :				str						=						GPTNeoXJapaneseModel(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				List[str]						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				Optional[int]						=						GPTNeoXJapaneseForCausalLM(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Dict						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						True
							UpperCAmelCase_   :				Tuple						=						GPTNeoXJapaneseForCausalLM(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()

							# first forward pass
							UpperCAmelCase_   :				Optional[Any]						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		use_cache=_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							UpperCAmelCase_   :				Dict						=						ids_tensor((self.batch_size, 3)    ,		config.vocab_size       )
							UpperCAmelCase_   :				Union[str, Any]						=						ids_tensor((self.batch_size, 3)    ,		vocab_size=2       )

							# append to next input_ids and
							UpperCAmelCase_   :				Optional[Any]						=						torch.cat([input_ids, next_tokens]    ,		dim=-1       )
							UpperCAmelCase_   :				int						=						torch.cat([input_mask, next_mask]    ,		dim=-1       )

							UpperCAmelCase_   :				Optional[int]						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		output_hidden_states=_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						output_from_no_past['hidden_states'][0]
							UpperCAmelCase_   :				List[str]						=						model(
							    _UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		past_key_values=_UpperCamelCase    ,		output_hidden_states=_UpperCamelCase    ,		)['hidden_states'][0]

							# select random slice
							UpperCAmelCase_   :				List[Any]						=						ids_tensor((1,)    ,		output_from_past.shape[-1]       ).item()
							UpperCAmelCase_   :				List[Any]						=						output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCAmelCase_   :				Optional[Any]						=						output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(_UpperCamelCase    ,		_UpperCamelCase    ,		atol=1E-3       )       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Tuple						=						self.prepare_config_and_inputs()
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						config_and_inputs
							UpperCAmelCase_   :				List[Any]						=						{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class   lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
		_snake_case   :   Optional[Any]							=  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
		_snake_case   :   int							=  (
		    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
		    if is_torch_available()
		    else {}
		)
		_snake_case   :   str							=  False
		_snake_case   :   str							=  False
		_snake_case   :   Any							=  False
		_snake_case   :   Optional[Any]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				int						=						GPTNeoXJapaneseModelTester(self       )
							UpperCAmelCase_   :				List[Any]						=						ConfigTester(self    ,		config_class=_UpperCamelCase    ,		hidden_size=3_7       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							self.config_tester.run_common_tests()



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							# This regression test was failing with PyTorch < 1.3
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self.model_tester.prepare_config_and_inputs_for_decoder()

							UpperCAmelCase_   :				List[str]						=						None

							self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				int						=						'abeja/gpt-neox-japanese-2.7b'

							UpperCAmelCase_   :				Any						=						['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

							UpperCAmelCase_   :				str						=						[
							    'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
							    '100年後に必要とされる会社は、「人」が中心の会社です。',
							    'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
							    '国境の長いトンネルを抜けると、そこは雪国だった。',
							    '美味しい日本食といえば、やっぱりお寿司ですよね。',
							]

							UpperCAmelCase_   :				List[Any]						=						GPTNeoXJapaneseTokenizer.from_pretrained(_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						GPTNeoXJapaneseForCausalLM.from_pretrained(_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						[]
							for prompt in prompts:
												UpperCAmelCase_   :				str						=						tokenizer(_UpperCamelCase    ,		return_tensors='pt'       ).input_ids
												UpperCAmelCase_   :				List[str]						=						model.generate(_UpperCamelCase    ,		max_length=5_0       )
												UpperCAmelCase_   :				Any						=						tokenizer.batch_decode(_UpperCamelCase    ,		skip_special_tokens=_UpperCamelCase       )
												predicted_outputs += generated_string
							self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )














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     							=						'.'

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




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

					with open(os.path.join(__snake_case					,	'utils'					,	'tf_ops'					,	'onnx.json'  )  ) as f:
										UpperCAmelCase_   :				Optional[Any]						=						json.load(__snake_case  )['opsets']

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

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

					UpperCAmelCase_   :				List[Any]						=						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
					UpperCAmelCase_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

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

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


if __name__ == "__main__":
		__UpperCAmelCase     							=						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     							=						parser.parse_args()

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






