





import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
							    GPTNeoXForCausalLM,
							    GPTNeoXForQuestionAnswering,
							    GPTNeoXForSequenceClassification,
							    GPTNeoXForTokenClassification,
							    GPTNeoXModel,
							)




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=99		,				_SCREAMING_SNAKE_CASE=64		,				_SCREAMING_SNAKE_CASE=5		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=37		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=None		,				) ->				Any:
								snake_case_				:							Tuple						 =       parent
								snake_case_				:							str						 =       batch_size
								snake_case_				:							Optional[Any]						 =       seq_length
								snake_case_				:							Dict						 =       is_training
								snake_case_				:							Optional[int]						 =       use_input_mask
								snake_case_				:							Any						 =       use_token_type_ids
								snake_case_				:							str						 =       use_labels
								snake_case_				:							List[Any]						 =       vocab_size
								snake_case_				:							Any						 =       hidden_size
								snake_case_				:							Tuple						 =       num_hidden_layers
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							Union[str, Any]						 =       intermediate_size
								snake_case_				:							Optional[int]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       hidden_dropout_prob
								snake_case_				:							Tuple						 =       attention_probs_dropout_prob
								snake_case_				:							int						 =       max_position_embeddings
								snake_case_				:							List[Any]						 =       type_vocab_size
								snake_case_				:							Dict						 =       type_sequence_label_size
								snake_case_				:							Dict						 =       initializer_range
								snake_case_				:							Optional[Any]						 =       num_labels
								snake_case_				:							Any						 =       num_choices
								snake_case_				:							Optional[Any]						 =       scope
								snake_case_				:							Union[str, Any]						 =       vocab_size - 1






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

								snake_case_				:							Dict						 =       None
								if self.use_input_mask:
										snake_case_				:							Optional[int]						 =       random_attention_mask([self.batch_size, self.seq_length]				)

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

								snake_case_				:							str						 =       self.get_config()

								return config, input_ids, input_mask, token_labels






						def       _lowerCAmelCase       (	self				) ->				str:
								return GPTNeoXConfig(
								    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=_SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				pad_token_id=self.pad_token_id		,				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Any						 =       self.prepare_config_and_inputs()

								snake_case_				:							Tuple						 =       True

								return config, input_ids, input_mask, token_labels






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				str:
								snake_case_				:							Tuple						 =       GPTNeoXModel(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							Dict						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       model(_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								snake_case_				:							Any						 =       True
								snake_case_				:							List[Any]						 =       GPTNeoXModel(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							Dict						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							Tuple						 =       GPTNeoXForCausalLM(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							str						 =       self.num_labels
								snake_case_				:							Tuple						 =       GPTNeoXForQuestionAnswering(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.start_logits.shape		,				(self.batch_size, self.seq_length)				)
								self.parent.assertEqual(result.end_logits.shape		,				(self.batch_size, self.seq_length)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Dict						 =       self.num_labels
								snake_case_				:							Dict						 =       GPTNeoXForSequenceClassification(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							int						 =       ids_tensor([self.batch_size]		,				self.type_sequence_label_size				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							int						 =       self.num_labels
								snake_case_				:							Any						 =       GPTNeoXForTokenClassification(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							Optional[int]						 =       True
								snake_case_				:							List[str]						 =       GPTNeoXForCausalLM(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()

								# first forward pass
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				use_cache=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case_				:							int						 =       ids_tensor((self.batch_size, 3)		,				config.vocab_size				)
								snake_case_				:							Tuple						 =       ids_tensor((self.batch_size, 3)		,				vocab_size=2				)

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

								snake_case_				:							Tuple						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Union[str, Any]						 =       output_from_no_past["hidden_states"][0]
								snake_case_				:							Tuple						 =       model(
								    _SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				past_key_values=_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE		,				)["hidden_states"][0]

								# select random slice
								snake_case_				:							List[Any]						 =       ids_tensor((1,)		,				output_from_past.shape[-1]				).item()
								snake_case_				:							List[Any]						 =       output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case_				:							Union[str, 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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Any						 =       self.prepare_config_and_inputs()
								snake_case_				:							Optional[int]						 =       config_and_inputs
								snake_case_				:							Tuple						 =       {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_torch
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[Any]						=					(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						A     :   str						=					(GPTNeoXForCausalLM,) if is_torch_available() else ()
						A     :   List[Any]						=					(
						    {
						        'feature-extraction': GPTNeoXModel,
						        'question-answering': GPTNeoXForQuestionAnswering,
						        'text-classification': GPTNeoXForSequenceClassification,
						        'text-generation': GPTNeoXForCausalLM,
						        'token-classification': GPTNeoXForTokenClassification,
						        'zero-shot': GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						A     :   List[Any]						=					False
						A     :   Union[str, Any]						=					False
						A     :   int						=					False
						A     :   int						=					False






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							int						 =       GPTNeoXModelTester(self				)
								snake_case_				:							List[Any]						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				hidden_size=64		,				num_attention_heads=8				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								self.config_tester.run_common_tests()






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							str						 =       self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								# This regression test was failing with PyTorch < 1.3
								snake_case_				:							Optional[Any]						 =       self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case_				:							Optional[Any]						 =       None

								self.model_tester.create_and_check_model_as_decoder(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Optional[Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Union[str, Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Any						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE				)






						@unittest.skip(reason="Feed forward chunking is not implemented"				)
						def       _lowerCAmelCase       (	self				) ->				Any:
								pass






						@parameterized.expand([("linear",), ("dynamic",)]				)
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				:							Optional[Any]						 =       ids_tensor([1, 10]		,				config.vocab_size				)
								snake_case_				:							str						 =       ids_tensor([1, int(config.max_position_embeddings * 1.5				)]		,				config.vocab_size				)

								set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
								snake_case_				:							List[str]						 =       GPTNeoXModel(_SCREAMING_SNAKE_CASE				)
								original_model.to(_SCREAMING_SNAKE_CASE				)
								original_model.eval()
								snake_case_				:							Dict						 =       original_model(_SCREAMING_SNAKE_CASE				).last_hidden_state
								snake_case_				:							Union[str, Any]						 =       original_model(_SCREAMING_SNAKE_CASE				).last_hidden_state

								set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
								snake_case_				:							int						 =       {"type": scaling_type, "factor": 10.0}
								snake_case_				:							Optional[Any]						 =       GPTNeoXModel(_SCREAMING_SNAKE_CASE				)
								scaled_model.to(_SCREAMING_SNAKE_CASE				)
								scaled_model.eval()
								snake_case_				:							List[Any]						 =       scaled_model(_SCREAMING_SNAKE_CASE				).last_hidden_state
								snake_case_				:							Any						 =       scaled_model(_SCREAMING_SNAKE_CASE				).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-5				)				)
								else:
										self.assertFalse(torch.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-5				)				)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-5				)				)




@require_torch
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						@slow
						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Any						 =       AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"				)
								for checkpointing in [True, False]:
										snake_case_				:							List[Any]						 =       GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"				)

										if checkpointing:
												model.gradient_checkpointing_enable()
										else:
												model.gradient_checkpointing_disable()
										model.to(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							str						 =       tokenizer("My favorite food is"		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)
										# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
										# See: https://github.com/huggingface/transformers/pull/24193
										snake_case_				:							str						 =       "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

										snake_case_				:							Dict						 =       model.generate(**_SCREAMING_SNAKE_CASE		,				do_sample=_SCREAMING_SNAKE_CASE		,				max_new_tokens=20				)
										snake_case_				:							Tuple						 =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)[0]

										self.assertEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)








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
lowercase  :							Tuple			  =    '''.'''

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

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Dict  ,      _a       :			List[str]    ):
		snake_case_				:							Optional[int]						 =       SavedModel()
		snake_case_				:							Any						 =       []

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

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

		with open(_a  ,      "rb"    ) as f:
				saved_model.ParseFromString(f.read()    )

		snake_case_				:							str						 =       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
		snake_case_				:							int						 =       sorted(_a    )
		snake_case_				:							Optional[int]						 =       []

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

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


if __name__ == "__main__":
							lowercase  :							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)'''
							)
							lowercase  :							int			  =    parser.parse_args()

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