





'''simple docstring'''




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
							import tensorflow as tf

							from transformers.models.swin.modeling_tf_swin import (
							    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFSwinForImageClassification,
							    TFSwinForMaskedImageModeling,
							    TFSwinModel,
							)


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	int     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	int=13     ,		lowerCamelCase_							:	Any=32     ,		lowerCamelCase_							:	Optional[int]=2     ,		lowerCamelCase_							:	Dict=3     ,		lowerCamelCase_							:	Union[str, Any]=16     ,		lowerCamelCase_							:	List[Any]=[1, 2, 1]     ,		lowerCamelCase_							:	Dict=[2, 2, 4]     ,		lowerCamelCase_							:	Optional[Any]=2     ,		lowerCamelCase_							:	Union[str, Any]=2.0     ,		lowerCamelCase_							:	Tuple=True     ,		lowerCamelCase_							:	Optional[Any]=0.0     ,		lowerCamelCase_							:	Dict=0.0     ,		lowerCamelCase_							:	Any=0.1     ,		lowerCamelCase_							:	Optional[Any]="gelu"     ,		lowerCamelCase_							:	Any=False     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	Optional[Any]=0.02     ,		lowerCamelCase_							:	List[str]=1e-5     ,		lowerCamelCase_							:	List[Any]=True     ,		lowerCamelCase_							:	Union[str, Any]=None     ,		lowerCamelCase_							:	Optional[int]=True     ,		lowerCamelCase_							:	int=10     ,		lowerCamelCase_							:	int=8     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	parent
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	batch_size
							SCREAMING_SNAKE_CASE				:							Dict						=	image_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	patch_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	num_channels
							SCREAMING_SNAKE_CASE				:							List[Any]						=	embed_dim
							SCREAMING_SNAKE_CASE				:							List[str]						=	depths
							SCREAMING_SNAKE_CASE				:							int						=	num_heads
							SCREAMING_SNAKE_CASE				:							List[str]						=	window_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	mlp_ratio
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	qkv_bias
							SCREAMING_SNAKE_CASE				:							List[str]						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							Tuple						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Dict						=	drop_path_rate
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	hidden_act
							SCREAMING_SNAKE_CASE				:							int						=	use_absolute_embeddings
							SCREAMING_SNAKE_CASE				:							List[Any]						=	patch_norm
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	layer_norm_eps
							SCREAMING_SNAKE_CASE				:							List[str]						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Any						=	is_training
							SCREAMING_SNAKE_CASE				:							List[Any]						=	scope
							SCREAMING_SNAKE_CASE				:							Tuple						=	use_labels
							SCREAMING_SNAKE_CASE				:							Tuple						=	type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							List[str]						=	encoder_stride


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							SCREAMING_SNAKE_CASE				:							str						=	None
							if self.use_labels:
									SCREAMING_SNAKE_CASE				:							int						=	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size		)

							SCREAMING_SNAKE_CASE				:							int						=	self.get_config()

							return config, pixel_values, labels


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							return SwinConfig(
							    image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		depths=self.depths     ,		num_heads=self.num_heads     ,		window_size=self.window_size     ,		mlp_ratio=self.mlp_ratio     ,		qkv_bias=self.qkv_bias     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		drop_path_rate=self.drop_path_rate     ,		hidden_act=self.hidden_act     ,		use_absolute_embeddings=self.use_absolute_embeddings     ,		path_norm=self.patch_norm     ,		layer_norm_eps=self.layer_norm_eps     ,		initializer_range=self.initializer_range     ,		encoder_stride=self.encoder_stride     ,		)


					def lowerCamelCase_	(		self							:	Tuple     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	TFSwinModel(config=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	model(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							str						=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
							SCREAMING_SNAKE_CASE				:							Dict						=	int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

							self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, expected_seq_len, expected_dim)		)


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	TFSwinForMaskedImageModeling(config=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(lowerCamelCase_		)
							self.parent.assertEqual(
							    result.logits.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

							# test greyscale images
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	1
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	TFSwinForMaskedImageModeling(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							List[str]						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)		)


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	self.type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	TFSwinForImageClassification(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model(lowerCamelCase_     ,		labels=lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							SCREAMING_SNAKE_CASE				:							Dict						=	1
							SCREAMING_SNAKE_CASE				:							List[str]						=	TFSwinForImageClassification(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)



					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	config_and_inputs
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_tf
class       UpperCamelCase__			(			lowercase_		,			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					(
					    (
					        TFSwinModel,
					        TFSwinForImageClassification,
					        TFSwinForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE__						=					(
					    {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	TFSwinModelTester(self		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	ConfigTester(self     ,		config_class=lowerCamelCase_     ,		embed_dim=37		)


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							return


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)


					@unittest.skip(reason="""Swin does not use inputs_embeds"""		)
					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							pass


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(lowerCamelCase_		)
									self.assertIsInstance(model.get_input_embeddings()     ,		tf.keras.layers.Layer		)
									SCREAMING_SNAKE_CASE				:							Dict						=	model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowerCamelCase_     ,		tf.keras.layers.Dense		)		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Dict						=	model_class(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							Dict						=	inspect.signature(model.call		)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE				:							int						=	[*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							Any						=	True

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	True
									SCREAMING_SNAKE_CASE				:							int						=	False
									SCREAMING_SNAKE_CASE				:							str						=	True
									SCREAMING_SNAKE_CASE				:							Tuple						=	model_class(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	outputs.attentions
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	len(self.model_tester.depths		)
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
									SCREAMING_SNAKE_CASE				:							int						=	config.window_size**2
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	model_class(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							List[str]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	outputs.attentions
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)
									SCREAMING_SNAKE_CASE				:							str						=	len(lowerCamelCase_		)

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
									SCREAMING_SNAKE_CASE				:							Any						=	True
									SCREAMING_SNAKE_CASE				:							Dict						=	model_class(lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

									if hasattr(self.model_tester     ,		"""num_hidden_states_types"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.num_hidden_states_types
									else:
											# also another +1 for reshaped_hidden_states
											SCREAMING_SNAKE_CASE				:							Dict						=	2
									self.assertEqual(out_len + added_hidden_states     ,		len(lowerCamelCase_		)		)

									SCREAMING_SNAKE_CASE				:							Dict						=	outputs.attentions

									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	model_class(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	outputs.hidden_states

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	getattr(
							    self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths		) + 1		)
							self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

							# Swin has a different seq_length
							SCREAMING_SNAKE_CASE				:							str						=	to_atuple(config.patch_size		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]		)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	outputs.reshaped_hidden_states
							self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	reshaped_hidden_states[0].shape

							SCREAMING_SNAKE_CASE				:							int						=	tf.reshape(reshaped_hidden_states[0]     ,		(batch_size, num_channels, height * width)		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	tf.transpose(lowerCamelCase_     ,		(0, 2, 1)		)

							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]		)     ,		[num_patches, self.model_tester.embed_dim]     ,		)


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	to_atuple(self.model_tester.image_size		)

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	True

									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							Dict						=	3

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	to_atuple(self.model_tester.image_size		)
							SCREAMING_SNAKE_CASE				:							Dict						=	to_atuple(config.patch_size		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							SCREAMING_SNAKE_CASE				:							Any						=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)



					@slow
					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	TFSwinModel.from_pretrained(lowerCamelCase_		)
									self.assertIsNotNone(lowerCamelCase_		)







@require_vision
@require_tf
class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""


					@cached_property
					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
							    if is_vision_available()
							    else None
							)



					@slow
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.default_image_processor

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	image_processor(images=lowerCamelCase_     ,		return_tensors="""tf"""		)

							# forward pass
							SCREAMING_SNAKE_CASE				:							int						=	model(lowerCamelCase_		)

							# verify the logits
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	tf.TensorShape((1, 10_00)		)
							self.assertEqual(outputs.logits.shape     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	tf.constant([-0.0_948, -0.6_454, -0.0_921]		)
							self.assertTrue(np.allclose(outputs.logits[0, :3]     ,		lowerCamelCase_     ,		atol=1e-4		)		)










'''simple docstring'''




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						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

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

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


if __name__ == "__main__":
							__UpperCAmelCase         =   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."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)



