
"""simple docstring"""



import unittest

from transformers import LiltConfig, 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 (
	    LiltForQuestionAnswering,
	    LiltForSequenceClassification,
	    LiltForTokenClassification,
	    LiltModel,
	)
	from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Any=13				,     UpperCAmelCase_	:			Optional[int]=7				,     UpperCAmelCase_	:			List[str]=True				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			Dict=True				,     UpperCAmelCase_	:			List[str]=True				,     UpperCAmelCase_	:			Union[str, Any]=99				,     UpperCAmelCase_	:			str=24				,     UpperCAmelCase_	:			Optional[int]=2				,     UpperCAmelCase_	:			Optional[Any]=6				,     UpperCAmelCase_	:			str=37				,     UpperCAmelCase_	:			Any="gelu"				,     UpperCAmelCase_	:			Optional[int]=0.1				,     UpperCAmelCase_	:			Union[str, Any]=0.1				,     UpperCAmelCase_	:			List[Any]=512				,     UpperCAmelCase_	:			List[str]=16				,     UpperCAmelCase_	:			int=2				,     UpperCAmelCase_	:			Optional[int]=0.02				,     UpperCAmelCase_	:			List[str]=3				,     UpperCAmelCase_	:			Optional[int]=None				,     UpperCAmelCase_	:			Optional[int]=1_000				,     )		->  int:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Tuple:







											"""simple docstring"""




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

											_lowerCAmelCase  	=			ids_tensor([self.batch_size, self.seq_length, 4]				,     self.range_bbox  )
											# Ensure that bbox is legal
											for i in range(bbox.shape[0]  ):
																		for j in range(bbox.shape[1]  ):
																									if bbox[i, j, 3] < bbox[i, j, 1]:
																																_lowerCAmelCase  	=			bbox[i, j, 3]
																																_lowerCAmelCase  	=			bbox[i, j, 1]
																																_lowerCAmelCase  	=			t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																_lowerCAmelCase  	=			bbox[i, j, 2]
																																_lowerCAmelCase  	=			bbox[i, j, 0]
																																_lowerCAmelCase  	=			t

											_lowerCAmelCase  	=			None
											if self.use_input_mask:
																		_lowerCAmelCase  	=			ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

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

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

											_lowerCAmelCase  	=			self.get_config()

											return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


				def 				__lowerCamelCase	(					self	:			int  )		->  Any:







											"""simple docstring"""




											return LiltConfig(
											    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				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			LiltModel(config=UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     bbox=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_  )
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     bbox=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_  )
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     bbox=UpperCAmelCase_  )

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


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int				,     )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.num_labels
											_lowerCAmelCase  	=			LiltForTokenClassification(config=UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()
											_lowerCAmelCase  	=			model(
											    UpperCAmelCase_				,     bbox=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_				,     labels=UpperCAmelCase_  )
											self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Union[str, Any]				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			LiltForQuestionAnswering(config=UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()
											_lowerCAmelCase  	=			model(
											    UpperCAmelCase_				,     bbox=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_				,     start_positions=UpperCAmelCase_				,     end_positions=UpperCAmelCase_				,     )
											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 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											(
											    (
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		
											)  	=			config_and_inputs
											_lowerCAmelCase  	=			{
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict






@require_torch
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Any							=					(
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_:							Dict							=					(
				    {
				        "feature-extraction": LiltModel,
				        "question-answering": LiltForQuestionAnswering,
				        "text-classification": LiltForSequenceClassification,
				        "token-classification": LiltForTokenClassification,
				        "zero-shot": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_:							Tuple							=					False
				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					False


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			List[str]  )		->  Union[str, Any]:







											"""simple docstring"""




											return True


				def 				__lowerCamelCase	(					self	:			int  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			LiltModelTester(self  )
											_lowerCAmelCase  	=			ConfigTester(self				,     config_class=UpperCAmelCase_				,     hidden_size=37  )


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







											"""simple docstring"""




											self.config_tester.run_common_tests()


				def 				__lowerCamelCase	(					self	:			int  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		_lowerCAmelCase  	=			type
																		self.model_tester.create_and_check_model(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_  )







				@slow
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  str:







											"""simple docstring"""




											for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  	=			LiltModel.from_pretrained(UpperCAmelCase_  )
																		self.assertIsNotNone(UpperCAmelCase_  )






@require_torch
@slow
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



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







											"""simple docstring"""




											_lowerCAmelCase  	=			LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'  ).to(UpperCAmelCase_  )

											_lowerCAmelCase  	=			torch.tensor([[1, 2]]				,     device=UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,     device=UpperCAmelCase_  )

											# forward pass
											with torch.no_grad():
																		_lowerCAmelCase  	=			model(input_ids=UpperCAmelCase_				,     bbox=UpperCAmelCase_  )

											_lowerCAmelCase  	=			torch.Size([1, 2, 768]  )
											_lowerCAmelCase  	=			torch.tensor(
											    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]				,     device=UpperCAmelCase_				,     )

											self.assertTrue(outputs.last_hidden_state.shape				,     UpperCAmelCase_  )
											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,     UpperCAmelCase_				,     atol=1E-3  )  )








"""simple docstring"""



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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')						)
							return token






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

							_lowerCAmelCase  	=			'huggingface/label-files'
							_lowerCAmelCase  	=			num_labels
							_lowerCAmelCase  	=			json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  repo_type='dataset'						)						)     ,  'r'						)						)
							_lowerCAmelCase  	=			{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

							_lowerCAmelCase  	=			idalabel
							_lowerCAmelCase  	=			{v: k for k, v in idalabel.items()}

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

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

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

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														_lowerCAmelCase  	=			[2, 2, 20]
														_lowerCAmelCase  	=			[3, 12, 16]
														_lowerCAmelCase  	=			[192, 768, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location=torch.device('cpu'						)						)

							_lowerCAmelCase  	=			OrderedDict()
							_lowerCAmelCase  	=			[]

							for idx in range(len(config.depth						)						):
														if config.cls_token[idx]:
																					_lowerCAmelCase  	=			list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE						)
							for i in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
	_snake_case          =						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=3_8_4,
	    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.'''
	)

	_snake_case          =						parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






