


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			__UpperCamelCase    (    _A    :				Dict				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
	        F"stage{idx}.patch_embed.proj.weight",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
	        F"stage{idx}.patch_embed.proj.bias",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
	        F"stage{idx}.patch_embed.norm.weight",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
	        F"stage{idx}.patch_embed.norm.bias",
	    )				)
	return embed

def 			__UpperCamelCase    (    _A    :				List[Any]					,   _A    :				Dict				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= []
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
	    )				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")				)
	return attention_weights

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= []
	token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token')				)
	return token

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= []
	head.append(('layernorm.weight', 'norm.weight')				)
	head.append(('layernorm.bias', 'norm.bias')				)
	head.append(('classifier.weight', 'head.weight')				)
	head.append(('classifier.bias', 'head.bias')				)
	return head

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Optional[Any]					,   _A    :				Dict					,   _A    :				str				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Tuple      							= 10_00

	lowerCAmelCase					:    str      							= 'huggingface/label-files'
	lowerCAmelCase					:    List[Any]      							= num_labels
	lowerCAmelCase					:    Any      							= json.load(open(cached_download(hf_hub_url(_A					,   _A					,   repo_type='dataset'				)				)					,   'r'				)				)
	lowerCAmelCase					:    List[str]      							= {int(_A				): v for k, v in idalabel.items()}

	lowerCAmelCase					:    List[str]      							= idalabel
	lowerCAmelCase					:    str      							= {v: k for k, v in idalabel.items()}

	lowerCAmelCase					:    int      							= CvtConfig(num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit('/'					,   1				)[-1][4:6] == "13":
		lowerCAmelCase					:    List[str]      							= [1, 2, 10]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit('/'					,   1				)[-1][4:6] == "21":
		lowerCAmelCase					:    Tuple      							= [1, 4, 16]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		lowerCAmelCase					:    Any      							= [2, 2, 20]
		lowerCAmelCase					:    List[str]      							= [3, 12, 16]
		lowerCAmelCase					:    List[Any]      							= [1_92, 7_68, 10_24]

	lowerCAmelCase					:    Union[str, Any]      							= CvtForImageClassification(_A				)
	lowerCAmelCase					:    str      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
	lowerCAmelCase					:    Optional[Any]      							= image_size
	lowerCAmelCase					:    List[Any]      							= torch.load(_A					,   map_location=torch.device('cpu'				)				)

	lowerCAmelCase					:    str      							= OrderedDict()
	lowerCAmelCase					:    int      							= []

	for idx in range(len(config.depth				)				):
		if config.cls_token[idx]:
			lowerCAmelCase					:    List[str]      							= list_of_state_dict + cls_token(_A				)
		lowerCAmelCase					:    Optional[Any]      							= list_of_state_dict + embeddings(_A				)
		for cnt in range(config.depth[idx]				):
			lowerCAmelCase					:    List[Any]      							= list_of_state_dict + attention(_A					,   _A				)

	lowerCAmelCase					:    List[str]      							= list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_A				)
	for i in range(len(_A				)				):
		lowerCAmelCase					:    Tuple      							= original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_A				)
	model.save_pretrained(_A				)
	image_processor.save_pretrained(_A				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[Any]		     =	argparse.ArgumentParser()
				parser.add_argument(
				    '--cvt_model',
				    default='cvt-w24',
				    type=str,
				    help='Name of the cvt model you\'d like to convert.',
				)
				parser.add_argument(
				    '--image_size',
				    default=384,
				    type=int,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--cvt_file_name',
				    default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
				    type=str,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				_lowerCAmelCase  :		str		     =	parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




'''simple docstring'''




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Tuple			      = GPTSwaTokenizer
	_lowerCamelCase   :					str			      = False
	_lowerCamelCase   :					Dict			      = True
	_lowerCamelCase   :					Optional[Any]			      = False



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    Tuple      							= GPTSwaTokenizer(snake_case__ ,					eos_token='<unk>' ,					bos_token='<unk>' ,					pad_token='<unk>'    )

		tokenizer.save_pretrained(self.tmpdirname    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		return input_text, output_text



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= '<s>'
		lowerCAmelCase					:    Optional[int]      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<unk>'    )
		self.assertEqual(vocab_keys[1] ,					'<s>'    )
		self.assertEqual(vocab_keys[-1] ,					'j'    )
		self.assertEqual(len(snake_case__    ) ,					2000    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					2000    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= GPTSwaTokenizer(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.tokenize('This is a test'    )
		self.assertListEqual(snake_case__ ,					['▁This', '▁is', '▁a', '▁t', 'est']    )

		self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    ) ,					[465, 287, 265, 631, 842]    )

		lowerCAmelCase					:    Tuple      							= tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] ,					)
		# fmt: on

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.convert_tokens_to_ids(snake_case__    )
		self.assertListEqual(
		    snake_case__ ,					[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,					)

		lowerCAmelCase					:    int      							= tokenizer.convert_ids_to_tokens(snake_case__    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    )
		# fmt: on



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= GPTSwaTokenizer(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= ['This is a test', 'I was born in 92000, and this is falsé.']
		lowerCAmelCase					:    Tuple      							= [
		    [465, 287, 265, 631, 842],
		    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
		]

		# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
		for text, expected_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertListEqual(tokenizer.encode_fast(snake_case__    ) ,					snake_case__    )

		# Test that decode_fast returns the input text
		for text, token_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertEqual(tokenizer.decode_fast(snake_case__    ) ,					snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= [
		    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
		    'Hey there, how are you doing this fine day?',
		    'This is a text with a trailing spaces followed by a dot     .',
		    'Häj sväjs lillebrör! =)',
		    'Det är inget fel på Mr. Cool',
		]

		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
		# fmt: on
		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='AI-Sweden/gpt-sw3-126m' ,					sequences=snake_case__ ,					)
