
'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase					:							Optional[Any]   =      logging.get_logger(__name__)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=False			) ->			Tuple:
	_snake_case     		=	[]
	for i in range(config.num_hidden_layers			):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')			)
		rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')			)
		rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')			)
		rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')			)
		rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')			)
		rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')			)
		rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')			)
		rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')			)
		rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')			)
		rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')			)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('cls_token', 'vit.embeddings.cls_token'),
	        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
	        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
	        ('pos_embed', 'vit.embeddings.position_embeddings'),
	    ]			)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		        ('pre_logits.fc.weight', 'pooler.dense.weight'),
		        ('pre_logits.fc.bias', 'pooler.dense.bias'),
		    ]			)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		_snake_case     		=	[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]			)

	return rename_keys







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=False			) ->			List[Any]:
	for i in range(config.num_hidden_layers			):
		if base_model:
			_snake_case     		=	''
		else:
			_snake_case     		=	'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		_snake_case     		=	state_dict.pop(F'blocks.{i}.attn.qkv.weight'			)
		_snake_case     		=	state_dict.pop(F'blocks.{i}.attn.qkv.bias'			)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case     		=	in_proj_weight[
		    : config.hidden_size, :
		]
		_snake_case     		=	in_proj_bias[: config.hidden_size]
		_snake_case     		=	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_snake_case     		=	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		_snake_case     		=	in_proj_weight[
		    -config.hidden_size :, :
		]
		_snake_case     		=	in_proj_bias[-config.hidden_size :]







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__A    ,       __A			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			List[Any]:
	_snake_case     		=	dct.pop(__A			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     ) ->			Dict:
	_snake_case     		=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case     		=	Image.open(requests.get(__A    ,       stream=__A			).raw			)
	return im







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Tuple:
	_snake_case     		=	ViTConfig()
	_snake_case     		=	False
	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	if vit_name[-5:] == "in21k":
		_snake_case     		=	True
		_snake_case     		=	int(vit_name[-12:-10]			)
		_snake_case     		=	int(vit_name[-9:-6]			)
	else:
		_snake_case     		=	1_000
		_snake_case     		=	'huggingface/label-files'
		_snake_case     		=	'imagenet-1k-id2label.json'
		_snake_case     		=	json.load(open(hf_hub_download(__A    ,       __A    ,       repo_type='dataset'			)    ,       'r'			)			)
		_snake_case     		=	{int(__A			): v for k, v in idalabel.items()}
		_snake_case     		=	idalabel
		_snake_case     		=	{v: k for k, v in idalabel.items()}
		_snake_case     		=	int(vit_name[-6:-4]			)
		_snake_case     		=	int(vit_name[-3:]			)
	# size of the architecture
	if "deit" in vit_name:
		if vit_name[9:].startswith('tiny'			):
			_snake_case     		=	192
			_snake_case     		=	768
			_snake_case     		=	12
			_snake_case     		=	3
		elif vit_name[9:].startswith('small'			):
			_snake_case     		=	384
			_snake_case     		=	1_536
			_snake_case     		=	12
			_snake_case     		=	6
		else:
			pass
	else:
		if vit_name[4:].startswith('small'			):
			_snake_case     		=	768
			_snake_case     		=	2_304
			_snake_case     		=	8
			_snake_case     		=	8
		elif vit_name[4:].startswith('base'			):
			pass
		elif vit_name[4:].startswith('large'			):
			_snake_case     		=	1_024
			_snake_case     		=	4_096
			_snake_case     		=	24
			_snake_case     		=	16
		elif vit_name[4:].startswith('huge'			):
			_snake_case     		=	1_280
			_snake_case     		=	5_120
			_snake_case     		=	32
			_snake_case     		=	16

    # load original model from timm
	_snake_case     		=	timm.create_model(__A    ,       pretrained=__A			)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	_snake_case     		=	timm_model.state_dict()
	if base_model:
		remove_classification_head_(__A			)
	_snake_case     		=	create_rename_keys(__A    ,       __A			)
	for src, dest in rename_keys:
		rename_key(__A    ,       __A    ,       __A			)
	read_in_q_k_v(__A    ,       __A    ,       __A			)

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		_snake_case     		=	ViTModel(__A			).eval()
	else:
		_snake_case     		=	ViTForImageClassification(__A			).eval()
	model.load_state_dict(__A			)

	# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
	if "deit" in vit_name:
		_snake_case     		=	DeiTImageProcessor(size=config.image_size			)
	else:
		_snake_case     		=	ViTImageProcessor(size=config.image_size			)
	_snake_case     		=	image_processor(images=prepare_img()    ,       return_tensors='pt'			)
	_snake_case     		=	encoding['pixel_values']
	_snake_case     		=	model(__A			)

	if base_model:
		_snake_case     		=	timm_model.forward_features(__A			)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(__A    ,       outputs.pooler_output    ,       atol=1e-3			)
	else:
		_snake_case     		=	timm_model(__A			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__A    ,       outputs.logits    ,       atol=1e-3			)

	Path(__A			).mkdir(exist_ok=__A			)
	print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'			)
	model.save_pretrained(__A			)
	print(F'Saving image processor to {pytorch_dump_folder_path}'			)
	image_processor.save_pretrained(__A			)


if __name__ == "__main__":
					lowercase					:							Optional[int]   =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--vit_name",
					    default="vit_base_patch16_224",
					    type=str,
					    help="Name of the ViT timm model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)

					lowercase					:							List[str]   =      parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



'''simple docstring'''







import unittest

from transformers import is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=32					,     lowerCAmelCase_=5					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	seq_length
								_snake_case     		=	is_training
								_snake_case     		=	use_token_type_ids
								_snake_case     		=	use_labels
								_snake_case     		=	vocab_size
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	self.vocab_size - 1


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

								_snake_case     		=	None
								if self.use_token_type_ids:
									_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size						)

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

								_snake_case     		=	OpenAIGPTConfig(
								    vocab_size=self.vocab_size					,     n_embd=self.hidden_size					,     n_layer=self.num_hidden_layers					,     n_head=self.num_attention_heads					,     n_positions=self.max_position_embeddings					,     pad_token_id=self.pad_token_id					,     )

								_snake_case     		=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,     2						)

								return (
								    config,
								    input_ids,
								    head_mask,
								    token_type_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     head_mask=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)

								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTDoubleHeadsModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	OpenAIGPTForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				
								)     		=	config_and_inputs
								_snake_case     		=	{
								    'input_ids': input_ids,
								    'token_type_ids': token_type_ids,
								    'head_mask': head_mask,
								}

								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							__lowercase   	= (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							__lowercase   	= (
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
									# tiny config could not be created.
									return True

								return False


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	super()._prepare_for_class(lowerCAmelCase_					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)

								if return_labels:
									if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowerCAmelCase_						)
								return inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     n_embd=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_						)




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	OpenAIGPTModel.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)


@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'						)
								model.to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([[4_81, 47_35, 5_44]]					,     dtype=torch.long					,     device=lowerCAmelCase_						)  # the president is
								_snake_case     		=	[
								    4_81,
								    47_35,
								    5_44,
								    2_46,
								    9_63,
								    8_70,
								    7_62,
								    2_39,
								    2_44,
								    4_04_77,
								    2_44,
								    2_49,
								    7_19,
								    8_81,
								    4_87,
								    5_44,
								    2_40,
								    2_44,
								    6_03,
								    4_81,
								]  # the president is a very good man. " \n " i\'m sure he is, " said the

								_snake_case     		=	model.generate(lowerCAmelCase_					,     do_sample=lowerCAmelCase_						)
								self.assertListEqual(output_ids[0].tolist()					,     lowerCAmelCase_						)

