


from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import (
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
			    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TFSharedEmbeddings,
			)

			if _tf_gpu_memory_limit is not None:
						_lowerCamelCase :       Dict     					=    tf.config.list_physical_devices('''GPU''')
						for gpu in gpus:
									# Restrict TensorFlow to only allocate x GB of memory on the GPUs
									try:
												tf.config.set_logical_device_configuration(
												    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
												)
												_lowerCamelCase :       Dict     					=    tf.config.list_logical_devices('''GPU''')
												print('''Logical GPUs''', logical_gpus)
									except RuntimeError as e:
												# Virtual devices must be set before GPUs have been initialized
												print(e)




@require_tf
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	List[str]					     =      None
	_UpperCAmelCase :	Optional[Any]					     =      ()
	_UpperCAmelCase :	Optional[Any]					     =      ()
	_UpperCAmelCase :	List[str]					     =      True
	_UpperCAmelCase :	Union[str, Any]					     =      True
	_UpperCAmelCase :	Optional[int]					     =      True
	_UpperCAmelCase :	Dict					     =      False



	def 				A							(						self			:					Any    ,						lowercase			:					Any    ,						lowercase			:					Any    ,						lowercase			:					str=False			):


				'''simple docstring'''


				_snake_case						=    copy.deepcopy(lowercase			)

				if model_class in get_values(lowercase			):
							_snake_case						=    {
							    k: tf.tile(tf.expand_dims(lowercase    ,						1			)    ,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)			)
							    if isinstance(lowercase    ,						tf.Tensor			) and v.ndim > 0
							    else v
							    for k, v in inputs_dict.items()
							}

				if return_labels:
							if model_class in get_values(lowercase			):
										_snake_case						=    tf.ones(self.model_tester.batch_size    ,						dtype=tf.intaa			)
							elif model_class in get_values(lowercase			):
										_snake_case						=    tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa			)
										_snake_case						=    tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa			)
							elif model_class in [
							    *get_values(lowercase			),
							    *get_values(lowercase			),
							]:
										_snake_case						=    tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa			)
							elif model_class in get_values(lowercase			):
										_snake_case						=    tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa			)
							elif model_class in [
							    *get_values(lowercase			),
							    *get_values(lowercase			),
							    *get_values(lowercase			),
							    *get_values(lowercase			),
							    *get_values(lowercase			),
							]:
										_snake_case						=    tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)    ,						dtype=tf.intaa			)
				return inputs_dict



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


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							_snake_case						=    self._prepare_for_class(lowercase    ,						lowercase			)
							_snake_case						=    model_class(lowercase			)

							@tf.function
							def run_in_graph_mode():
										return model(lowercase			)

							_snake_case						=    run_in_graph_mode()
							self.assertIsNotNone(lowercase			)



	@slow
	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							_snake_case						=    self._prepare_for_class(lowercase    ,						lowercase			)
							_snake_case						=    model_class(lowercase			)

							@tf.function(experimental_compile=lowercase			)
							def run_in_graph_mode():
										return model(lowercase			)

							_snake_case						=    run_in_graph_mode()
							self.assertIsNotNone(lowercase			)



	@slow
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							_snake_case						=    model_class(lowercase			)
							if getattr(lowercase    ,						'hf_compute_loss'    ,						lowercase			):
										# Test that model correctly compute the loss with kwargs
										_snake_case						=    self._prepare_for_class(inputs_dict.copy()    ,						lowercase    ,						return_labels=lowercase			)
										# Is there a better way to remove these decoder inputs?
										_snake_case						=    {
										    key: val
										    for key, val in prepared_for_class.items()
										    if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
										}

										_snake_case						=    {
										    'labels',
										    'label',
										    'label_ids',
										    'start_positions',
										    'start_position',
										    'end_positions',
										    'end_position',
										    'next_sentence_label',
										}
										_snake_case						=    possible_label_cols.intersection(set(lowercase			)			)
										self.assertGreater(len(lowercase			)    ,						0    ,						msg='No matching label names found!'			)
										_snake_case						=    {key: val for key, val in prepared_for_class.items() if key in label_names}
										_snake_case						=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
										self.assertGreater(len(lowercase			)    ,						0			)

										# Make sure it works with XLA!
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)    ,						jit_compile=lowercase			)
										# Make sure the model fits without crashing regardless of where we pass the labels
										_snake_case						=    model.fit(
										    lowercase    ,						validation_data=lowercase    ,						steps_per_epoch=1    ,						validation_steps=1    ,						shuffle=lowercase    ,						verbose=0    ,						)
										_snake_case						=    history.history['loss'][0]
										self.assertTrue(not isnan(lowercase			)			)
										_snake_case						=    history.history['val_loss'][0]
										self.assertTrue(not isnan(lowercase			)			)

										# Now test it with separate labels, to make sure that path works in XLA too.
										_snake_case						=    model_class(lowercase			)
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)    ,						jit_compile=lowercase			)
										_snake_case						=    model.fit(
										    lowercase    ,						lowercase    ,						validation_data=(inputs_minus_labels, labels)    ,						steps_per_epoch=1    ,						validation_steps=1    ,						shuffle=lowercase    ,						verbose=0    ,						)

										_snake_case						=    history.history['loss'][0]
										self.assertTrue(not isnan(lowercase			)			)
										_snake_case						=    history.history['val_loss'][0]
										self.assertTrue(not isnan(lowercase			)			)



	@slow
	def 				A							(						self			:					int			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				_snake_case						=    True
				_snake_case						=    True

				if hasattr(lowercase    ,						'use_cache'			):
							_snake_case						=    True

				_snake_case						=    getattr(self.model_tester    ,						'encoder_seq_length'    ,						self.model_tester.seq_length			)
				_snake_case						=    getattr(self.model_tester    ,						'key_length'    ,						lowercase			)

				for model_class in self.all_model_classes[:2]:
							_snake_case						=    self._prepare_for_class(lowercase    ,						lowercase			)
							_snake_case						=    model_class(lowercase			)
							model.build()
							_snake_case						=    len(model(lowercase			)			)

							for key in list(class_inputs_dict.keys()			):
										# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
										if key not in model.input_signature:
													del class_inputs_dict[key]
										# Check it's a tensor, in case the inputs dict has some bools in it too
										elif isinstance(class_inputs_dict[key]    ,						tf.Tensor			) and class_inputs_dict[key].dtype.is_integer:
													_snake_case						=    tf.cast(class_inputs_dict[key]    ,						tf.intaa			)

							if set(class_inputs_dict.keys()			) != set(model.input_signature.keys()			):
										continue  # Some models have inputs that the preparation functions don't create, we skip those

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(lowercase    ,						saved_model=lowercase			)
										_snake_case						=    os.path.join(lowercase    ,						'saved_model'    ,						'1'			)
										_snake_case						=    tf.keras.models.load_model(lowercase			)
										_snake_case						=    model(lowercase			)

										if self.is_encoder_decoder:
													_snake_case						=    outputs['encoder_hidden_states']
													_snake_case						=    outputs['encoder_attentions']
										else:
													_snake_case						=    outputs['hidden_states']
													_snake_case						=    outputs['attentions']

										self.assertEqual(len(lowercase			)    ,						lowercase			)

										_snake_case						=    getattr(
										    self.model_tester    ,						'expected_num_hidden_layers'    ,						self.model_tester.num_hidden_layers + 1			)

										self.assertEqual(len(lowercase			)    ,						lowercase			)
										self.assertListEqual(
										    list(output_hidden_states[0].shape[-2:]			)    ,						[self.model_tester.seq_length, self.model_tester.hidden_size]    ,						)

										self.assertEqual(len(lowercase			)    ,						self.model_tester.num_hidden_layers			)
										self.assertListEqual(
										    list(output_attentions[0].shape[-3:]			)    ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,						)



	@slow
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

				# try/finally block to ensure subsequent tests run in float32
				try:
							_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
										_snake_case						=    self._prepare_for_class(lowercase    ,						lowercase			)
										_snake_case						=    model_class(lowercase			)
										_snake_case						=    model(lowercase			)

										self.assertIsNotNone(lowercase			)
				finally:
							tf.keras.mixed_precision.set_global_policy('float32'			)



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


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				# head_mask and decoder_head_mask has different shapes than other input args
				if "head_mask" in inputs_dict:
							del inputs_dict["head_mask"]
				if "decoder_head_mask" in inputs_dict:
							del inputs_dict["decoder_head_mask"]
				if "cross_attn_head_mask" in inputs_dict:
							del inputs_dict["cross_attn_head_mask"]
				_snake_case						=    {
				    module_member
				    for model_class in self.all_model_classes
				    for module in (import_module(model_class.__module__			),)
				    for module_member_name in dir(lowercase			)
				    if module_member_name.endswith('MainLayer'			)
				    for module_member in (getattr(lowercase    ,						lowercase			),)
				    if isinstance(lowercase    ,						lowercase			)
				    and tf.keras.layers.Layer in module_member.__bases__
				    and getattr(lowercase    ,						'_keras_serializable'    ,						lowercase			)
				}

				for main_layer_class in tf_main_layer_classes:
							# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
							if "T5" in main_layer_class.__name__:
										# Take the same values than in TFT5ModelTester for this shared layer
										_snake_case						=    TFSharedEmbeddings(self.model_tester.vocab_size    ,						self.model_tester.hidden_size    ,						name='shared'			)
										_snake_case						=    False
										_snake_case						=    main_layer_class(lowercase    ,						embed_tokens=lowercase			)
							else:
										_snake_case						=    main_layer_class(lowercase			)

							_snake_case						=    {
							    name: tf.keras.Input(tensor.shape[1:]    ,						dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
							}

							if hasattr(self.model_tester    ,						'num_labels'			):
										_snake_case						=    self.model_tester.num_labels
							else:
										_snake_case						=    2

							_snake_case						=    tf.data.Dataset.from_tensor_slices(
							    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)			))			).batch(1			)

							_snake_case						=    main_layer(lowercase			)[0]
							_snake_case						=    tf.keras.layers.Dense(lowercase    ,						activation='softmax'    ,						name='outputs'			)(lowercase			)
							_snake_case						=    tf.keras.models.Model(inputs=lowercase    ,						outputs=[outputs]			)

							model.compile(loss='binary_crossentropy'    ,						optimizer='adam'    ,						metrics=['binary_accuracy']			)
							model.fit(lowercase    ,						epochs=1			)

							with tempfile.TemporaryDirectory() as tmpdirname:
										_snake_case						=    os.path.join(lowercase    ,						'keras_model.h5'			)
										model.save(lowercase			)
										if "T5" in main_layer_class.__name__:
													_snake_case						=    tf.keras.models.load_model(
													    lowercase    ,						custom_objects={
													        main_layer_class.__name__: main_layer_class,
													        'TFSharedEmbeddings': TFSharedEmbeddings,
													    }    ,						)
										else:
													_snake_case						=    tf.keras.models.load_model(
													    lowercase    ,						custom_objects={main_layer_class.__name__: main_layer_class}			)
										assert isinstance(lowercase    ,						tf.keras.Model			)
										model(lowercase			)



	@slow
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes[:2]:
							_snake_case						=    model_class(lowercase			)

							_snake_case						=    copy.deepcopy(lowercase			)

							if not self.is_encoder_decoder:
										_snake_case						=    inputs['input_ids']
										del inputs["input_ids"]
							else:
										_snake_case						=    inputs['input_ids']
										_snake_case						=    inputs.get('decoder_input_ids'    ,						lowercase			)
										del inputs["input_ids"]
										inputs.pop('decoder_input_ids'    ,						lowercase			)

							if not self.is_encoder_decoder:
										_snake_case						=    model.get_input_embeddings()(lowercase			)
							else:
										_snake_case						=    model.get_input_embeddings()(lowercase			)
										_snake_case						=    model.get_input_embeddings()(lowercase			)

							_snake_case						=    self._prepare_for_class(lowercase    ,						lowercase			)

							@tf.function
							def run_in_graph_mode():
										return model(lowercase			)

							_snake_case						=    run_in_graph_mode()
							self.assertIsNotNone(lowercase			)



	def 				A							(						self			:					List[Any]    ,						lowercase			:					Tuple    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    []
				if model.config.bos_token_id is not None:
							special_tokens.append(model.config.bos_token_id			)
				if model.config.pad_token_id is not None:
							special_tokens.append(model.config.pad_token_id			)
				if model.config.eos_token_id is not None:
							special_tokens.append(model.config.eos_token_id			)

				# create random bad tokens that are not special tokens
				_snake_case						=    []
				while len(lowercase			) < num_bad_tokens:
							_snake_case						=    tf.squeeze(ids_tensor((1, 1)    ,						self.model_tester.vocab_size			)    ,						0			).numpy()[0]
							if token not in special_tokens:
										bad_tokens.append(lowercase			)
				return bad_tokens



	def 				A							(						self			:					List[Any]    ,						lowercase			:					List[str]			):


				'''simple docstring'''


				for token_id in output_ids[0].numpy().tolist():
							self.assertGreaterEqual(lowercase    ,						0			)
							self.assertLess(lowercase    ,						self.model_tester.vocab_size			)





	def 				A							(						self			:					List[Any]    ,						lowercase			:					Optional[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				for bad_word_ids in bad_words_ids:
							# for all slices in batch
							for generated_ids_slice in generated_ids:
										# for all word idx
										for i in range(len(lowercase			)    ,						len(lowercase			)			):
													# if tokens match
													if generated_ids_slice[i - len(lowercase			) : i] == bad_word_ids:
																return True
				return False

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  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

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

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


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    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 :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)