





from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.mpnet.modeling_tf_mpnet import (
	    TFMPNetForMaskedLM,
	    TFMPNetForMultipleChoice,
	    TFMPNetForQuestionAnswering,
	    TFMPNetForSequenceClassification,
	    TFMPNetForTokenClassification,
	    TFMPNetModel,
	)




class 					UpperCamelCase_    :


							def __init__(							self      :List[Any]							,			__A      :Optional[int]							,			__A      :List[Any]=13							,			__A      :Optional[int]=7							,			__A      :List[str]=True							,			__A      :str=True							,			__A      :Any=False							,			__A      :Dict=True							,			__A      :Dict=99							,			__A      :Optional[int]=64							,			__A      :Any=2							,			__A      :int=4							,			__A      :List[str]=64							,			__A      :Union[str, Any]="gelu"							,			__A      :Any=0.1							,			__A      :List[str]=0.1							,			__A      :int=512							,			__A      :Any=16							,			__A      :List[str]=2							,			__A      :List[str]=0.0_2							,			__A      :List[Any]=3							,			__A      :Any=4							,			__A      :Any=None							,			)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= parent
									SCREAMING_SNAKE_CASE__								= batch_size
									SCREAMING_SNAKE_CASE__								= seq_length
									SCREAMING_SNAKE_CASE__								= is_training
									SCREAMING_SNAKE_CASE__								= use_input_mask
									SCREAMING_SNAKE_CASE__								= use_token_type_ids
									SCREAMING_SNAKE_CASE__								= use_labels
									SCREAMING_SNAKE_CASE__								= vocab_size
									SCREAMING_SNAKE_CASE__								= hidden_size
									SCREAMING_SNAKE_CASE__								= num_hidden_layers
									SCREAMING_SNAKE_CASE__								= num_attention_heads
									SCREAMING_SNAKE_CASE__								= intermediate_size
									SCREAMING_SNAKE_CASE__								= hidden_act
									SCREAMING_SNAKE_CASE__								= hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								= max_position_embeddings
									SCREAMING_SNAKE_CASE__								= type_vocab_size
									SCREAMING_SNAKE_CASE__								= type_sequence_label_size
									SCREAMING_SNAKE_CASE__								= initializer_range
									SCREAMING_SNAKE_CASE__								= num_labels
									SCREAMING_SNAKE_CASE__								= num_choices
									SCREAMING_SNAKE_CASE__								= scope


							def        _snake_case					(							self      :List[str]						)				->				List[Any]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								= None
									if self.use_input_mask:
											SCREAMING_SNAKE_CASE__								= random_attention_mask([self.batch_size, self.seq_length]						)

									SCREAMING_SNAKE_CASE__								= None
									SCREAMING_SNAKE_CASE__								= None
									SCREAMING_SNAKE_CASE__								= None
									if self.use_labels:
											SCREAMING_SNAKE_CASE__								= ids_tensor([self.batch_size]							,			self.type_sequence_label_size						)
											SCREAMING_SNAKE_CASE__								= ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels						)
											SCREAMING_SNAKE_CASE__								= ids_tensor([self.batch_size]							,			self.num_choices						)

									SCREAMING_SNAKE_CASE__								= MPNetConfig(
									    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							,			initializer_range=self.initializer_range							,			)
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


							def        _snake_case					(							self      :Optional[int]							,			__A      :List[Any]							,			__A      :List[str]							,			__A      :Union[str, Any]							,			__A      :List[str]							,			__A      :List[Any]							,			__A      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= TFMPNetModel(config=__A						)
									SCREAMING_SNAKE_CASE__								= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									SCREAMING_SNAKE_CASE__								= [input_ids, input_mask]
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)						)


							def        _snake_case					(							self      :Optional[int]							,			__A      :List[Any]							,			__A      :List[Any]							,			__A      :List[Any]							,			__A      :Dict							,			__A      :Dict							,			__A      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= TFMPNetForMaskedLM(config=__A						)
									SCREAMING_SNAKE_CASE__								= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)						)


							def        _snake_case					(							self      :Dict							,			__A      :int							,			__A      :Any							,			__A      :int							,			__A      :List[str]							,			__A      :Any							,			__A      :str						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= TFMPNetForQuestionAnswering(config=__A						)
									SCREAMING_SNAKE_CASE__								= {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									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        _snake_case					(							self      :Any							,			__A      :Optional[Any]							,			__A      :Union[str, Any]							,			__A      :int							,			__A      :Tuple							,			__A      :Any							,			__A      :Dict						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.num_labels
									SCREAMING_SNAKE_CASE__								= TFMPNetForSequenceClassification(__A						)
									SCREAMING_SNAKE_CASE__								= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)						)


							def        _snake_case					(							self      :str							,			__A      :Optional[Any]							,			__A      :Tuple							,			__A      :int							,			__A      :Union[str, Any]							,			__A      :Optional[int]							,			__A      :str						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.num_choices
									SCREAMING_SNAKE_CASE__								= TFMPNetForMultipleChoice(__A						)
									SCREAMING_SNAKE_CASE__								= tf.tile(tf.expand_dims(__A							,			1						)							,			(1, self.num_choices, 1)						)
									SCREAMING_SNAKE_CASE__								= tf.tile(tf.expand_dims(__A							,			1						)							,			(1, self.num_choices, 1)						)
									SCREAMING_SNAKE_CASE__								= {
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_choices)						)


							def        _snake_case					(							self      :Any							,			__A      :Dict							,			__A      :Dict							,			__A      :Dict							,			__A      :Optional[int]							,			__A      :List[str]							,			__A      :Union[str, Any]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.num_labels
									SCREAMING_SNAKE_CASE__								= TFMPNetForTokenClassification(__A						)
									SCREAMING_SNAKE_CASE__								= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)						)


							def        _snake_case					(							self      :int						)				->				Optional[Any]:



									"""simple docstring"""



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




@require_tf
class 					UpperCamelCase_    (       UpperCamelCase__			,					UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   (
							    (
							        TFMPNetForMaskedLM,
							        TFMPNetForMultipleChoice,
							        TFMPNetForQuestionAnswering,
							        TFMPNetForSequenceClassification,
							        TFMPNetForTokenClassification,
							        TFMPNetModel,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCamelCase_          =   (
							    {
							        "feature-extraction": TFMPNetModel,
							        "fill-mask": TFMPNetForMaskedLM,
							        "question-answering": TFMPNetForQuestionAnswering,
							        "text-classification": TFMPNetForSequenceClassification,
							        "token-classification": TFMPNetForTokenClassification,
							        "zero-shot": TFMPNetForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase_          =   False
							lowerCamelCase_          =   False


							def        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= TFMPNetModelTester(self						)
									SCREAMING_SNAKE_CASE__								= ConfigTester(self							,			config_class=__A							,			hidden_size=37						)


							def        _snake_case					(							self      :Optional[Any]						)				->				Union[str, Any]:



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def        _snake_case					(							self      :str						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*__A						)


							def        _snake_case					(							self      :List[Any]						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_masked_lm(*__A						)


							def        _snake_case					(							self      :List[str]						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*__A						)


							def        _snake_case					(							self      :Optional[int]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*__A						)


							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*__A						)


							def        _snake_case					(							self      :Union[str, Any]						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*__A						)


							@slow
							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									for model_name in ["microsoft/mpnet-base"]:
											SCREAMING_SNAKE_CASE__								= TFMPNetModel.from_pretrained(__A						)
											self.assertIsNotNone(__A						)




@require_tf
class 					UpperCamelCase_    (       unittest.TestCase      ):


							@slow
							def        _snake_case					(							self      :Optional[int]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""						)
									SCREAMING_SNAKE_CASE__								= tf.constant([[0, 1, 2, 3, 4, 5]]						)
									SCREAMING_SNAKE_CASE__								= model(__A						)[0]

									SCREAMING_SNAKE_CASE__								= [1, 6, 768]
									self.assertEqual(output.shape							,			__A						)

									SCREAMING_SNAKE_CASE__								= tf.constant(
									    [
									        [
									            [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
									            [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
									            [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
									        ]
									    ]						)
									tf.debugging.assert_near(output[:, :3, :3]							,			__A							,			atol=1E-4						)






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
				f.write(config.to_json_string()      )


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)