


'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.distilbert.modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertModel,
				)



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					):
		lowerCAmelCase					:    Optional[int]      							= parent
		lowerCAmelCase					:    Tuple      							= 13
		lowerCAmelCase					:    List[Any]      							= 7
		lowerCAmelCase					:    str      							= True
		lowerCAmelCase					:    Optional[Any]      							= True
		lowerCAmelCase					:    Tuple      							= False
		lowerCAmelCase					:    int      							= True
		lowerCAmelCase					:    str      							= 99
		lowerCAmelCase					:    str      							= 32
		lowerCAmelCase					:    Tuple      							= 2
		lowerCAmelCase					:    List[Any]      							= 4
		lowerCAmelCase					:    Dict      							= 37
		lowerCAmelCase					:    int      							= 'gelu'
		lowerCAmelCase					:    Dict      							= 0.1
		lowerCAmelCase					:    Dict      							= 0.1
		lowerCAmelCase					:    str      							= 512
		lowerCAmelCase					:    Optional[int]      							= 16
		lowerCAmelCase					:    Tuple      							= 2
		lowerCAmelCase					:    Tuple      							= 0.0_2
		lowerCAmelCase					:    Tuple      							= 3
		lowerCAmelCase					:    Optional[int]      							= 4
		lowerCAmelCase					:    Union[str, Any]      							= None



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

		lowerCAmelCase					:    Tuple      							= None
		if self.use_input_mask:
			lowerCAmelCase					:    Tuple      							= random_attention_mask([self.batch_size, self.seq_length]    )

		lowerCAmelCase					:    Union[str, Any]      							= None
		lowerCAmelCase					:    Optional[int]      							= None
		lowerCAmelCase					:    List[Any]      							= None
		if self.use_labels:
			lowerCAmelCase					:    List[str]      							= ids_tensor([self.batch_size] ,					self.type_sequence_label_size    )
			lowerCAmelCase					:    List[Any]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels    )
			lowerCAmelCase					:    Dict      							= ids_tensor([self.batch_size] ,					self.num_choices    )

		lowerCAmelCase					:    List[str]      							= DistilBertConfig(
		    vocab_size=self.vocab_size ,					dim=self.hidden_size ,					n_layers=self.num_hidden_layers ,					n_heads=self.num_attention_heads ,					hidden_dim=self.intermediate_size ,					hidden_act=self.hidden_act ,					dropout=self.hidden_dropout_prob ,					attention_dropout=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					)

		return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= TFDistilBertModel(config=snake_case__    )
		lowerCAmelCase					:    List[Any]      							= {'input_ids': input_ids, 'attention_mask': input_mask}

		lowerCAmelCase					:    List[Any]      							= model(snake_case__    )

		lowerCAmelCase					:    List[str]      							= [input_ids, input_mask]

		lowerCAmelCase					:    Optional[int]      							= model(snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= TFDistilBertForMaskedLM(config=snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= {'input_ids': input_ids, 'attention_mask': input_mask}
		lowerCAmelCase					:    int      							= model(snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= TFDistilBertForQuestionAnswering(config=snake_case__    )
		lowerCAmelCase					:    Dict      							= {
		    'input_ids': input_ids,
		    'attention_mask': input_mask,
		}
		lowerCAmelCase					:    Optional[int]      							= model(snake_case__    )
		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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.num_labels
		lowerCAmelCase					:    List[str]      							= TFDistilBertForSequenceClassification(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= {'input_ids': input_ids, 'attention_mask': input_mask}
		lowerCAmelCase					:    Dict      							= model(snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[str]      							= self.num_choices
		lowerCAmelCase					:    Optional[Any]      							= TFDistilBertForMultipleChoice(snake_case__    )
		lowerCAmelCase					:    Dict      							= tf.tile(tf.expand_dims(snake_case__ ,					1    ) ,					(1, self.num_choices, 1)    )
		lowerCAmelCase					:    List[Any]      							= tf.tile(tf.expand_dims(snake_case__ ,					1    ) ,					(1, self.num_choices, 1)    )
		lowerCAmelCase					:    Any      							= {
		    'input_ids': multiple_choice_inputs_ids,
		    'attention_mask': multiple_choice_input_mask,
		}
		lowerCAmelCase					:    Union[str, Any]      							= model(snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= self.num_labels
		lowerCAmelCase					:    str      							= TFDistilBertForTokenClassification(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= {'input_ids': input_ids, 'attention_mask': input_mask}
		lowerCAmelCase					:    List[str]      							= model(snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.prepare_config_and_inputs()
		((lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase))					:    Tuple      							= config_and_inputs
		lowerCAmelCase					:    Union[str, Any]      							= {'input_ids': input_ids, 'attention_mask': input_mask}
		return config, inputs_dict



@require_tf
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Union[str, Any]			      = (
	    (
	        TFDistilBertModel,
	        TFDistilBertForMaskedLM,
	        TFDistilBertForQuestionAnswering,
	        TFDistilBertForSequenceClassification,
	        TFDistilBertForTokenClassification,
	        TFDistilBertForMultipleChoice,
	    )
	    if is_tf_available()
	    else None
	)
	_lowerCamelCase   :					Optional[int]			      = (
	    {
	        """feature-extraction""": TFDistilBertModel,
	        """fill-mask""": TFDistilBertForMaskedLM,
	        """question-answering""": TFDistilBertForQuestionAnswering,
	        """text-classification""": TFDistilBertForSequenceClassification,
	        """token-classification""": TFDistilBertForTokenClassification,
	        """zero-shot""": TFDistilBertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	_lowerCamelCase   :					Union[str, Any]			      = False
	_lowerCamelCase   :					int			      = False



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= TFDistilBertModelTester(self    )
		lowerCAmelCase					:    Any      							= ConfigTester(self ,					config_class=snake_case__ ,					dim=37    )



	def 				lowercase     (					self    ):
		self.config_tester.run_common_tests()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_for_masked_lm(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_for_question_answering(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_for_sequence_classification(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_for_multiple_choice(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_distilbert_for_token_classification(*snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]    ):
			lowerCAmelCase					:    Any      							= TFDistilBertModel.from_pretrained(snake_case__    )
			self.assertIsNotNone(snake_case__    )



@require_tf
class  lowerCAmelCase				(				unittest.TestCase							):



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= TFDistilBertModel.from_pretrained('distilbert-base-uncased'    )
		lowerCAmelCase					:    List[str]      							= tf.constant([[0, 1, 2, 3, 4, 5]]    )
		lowerCAmelCase					:    Any      							= model(snake_case__    )[0]

		lowerCAmelCase					:    List[str]      							= [1, 6, 768]
		self.assertEqual(output.shape ,					snake_case__    )

		lowerCAmelCase					:    Union[str, Any]      							= tf.constant(
		    [
		        [
		            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
		            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
		            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
		        ]
		    ]    )
		tf.debugging.assert_near(output[:, :3, :3] ,					snake_case__ ,					atol=1e-4    )




'''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 			__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)
