



"""simple docstring"""





import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    MegatronBertForCausalLM,
					    MegatronBertForMaskedLM,
					    MegatronBertForMultipleChoice,
					    MegatronBertForNextSentencePrediction,
					    MegatronBertForPreTraining,
					    MegatronBertForQuestionAnswering,
					    MegatronBertForSequenceClassification,
					    MegatronBertForTokenClassification,
					    MegatronBertModel,
					)



class    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=6_4							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=3							,    _lowercase=4							,    _lowercase=None							,    ) ->						str:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			parent
								snake_case_   : Union[str, Any]						=			batch_size
								snake_case_   : Any						=			seq_length
								snake_case_   : str						=			is_training
								snake_case_   : Any						=			use_input_mask
								snake_case_   : Tuple						=			use_token_type_ids
								snake_case_   : Any						=			use_labels
								snake_case_   : Dict						=			vocab_size
								snake_case_   : str						=			hidden_size
								snake_case_   : Optional[int]						=			embedding_size
								snake_case_   : int						=			num_hidden_layers
								snake_case_   : int						=			num_attention_heads
								snake_case_   : Tuple						=			intermediate_size
								snake_case_   : Optional[int]						=			hidden_act
								snake_case_   : Optional[Any]						=			hidden_dropout_prob
								snake_case_   : List[str]						=			attention_probs_dropout_prob
								snake_case_   : List[Any]						=			max_position_embeddings
								snake_case_   : Union[str, Any]						=			type_vocab_size
								snake_case_   : Union[str, Any]						=			type_sequence_label_size
								snake_case_   : Optional[int]						=			initializer_range
								snake_case_   : Optional[int]						=			num_labels
								snake_case_   : List[str]						=			num_choices
								snake_case_   : Optional[Any]						=			scope





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : List[Any]						=			None
								if self.use_input_mask:
														snake_case_   : Optional[int]						=			random_attention_mask([self.batch_size, self.seq_length]   )

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

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

								snake_case_   : Any						=			self.get_config()

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





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								return MegatronBertConfig(
								    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							,    embedding_size=self.embedding_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							,    is_decoder=__A							,    initializer_range=self.initializer_range							,    )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Any						=			MegatronBertModel(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : List[Any]						=			model(__A							,    attention_mask=__A							,    token_type_ids=__A   )
								snake_case_   : Tuple						=			model(__A							,    token_type_ids=__A   )
								snake_case_   : str						=			model(__A   )

								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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			MegatronBertForMaskedLM(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : int						=			model(__A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						int:







								'''simple docstring'''

								snake_case_   : Tuple						=			MegatronBertForCausalLM(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : List[str]						=			model(__A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			MegatronBertForNextSentencePrediction(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : str						=			model(
								    __A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A							,    )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, 2)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			MegatronBertForPreTraining(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : Tuple						=			model(
								    __A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A							,    next_sentence_label=__A							,    )
								self.parent.assertEqual(result.prediction_logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)   )
								self.parent.assertEqual(result.seq_relationship_logits.shape							,    (self.batch_size, 2)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : str						=			MegatronBertForQuestionAnswering(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : Union[str, Any]						=			model(
								    __A							,    attention_mask=__A							,    token_type_ids=__A							,    start_positions=__A							,    end_positions=__A							,    )
								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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.num_labels
								snake_case_   : Optional[Any]						=			MegatronBertForSequenceClassification(__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : str						=			model(__A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.num_labels
								snake_case_   : str						=			MegatronBertForTokenClassification(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : Optional[int]						=			model(__A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.num_choices
								snake_case_   : List[str]						=			MegatronBertForMultipleChoice(config=__A   )
								model.to(__A   )
								model.eval()
								snake_case_   : Union[str, Any]						=			input_ids.unsqueeze(1   ).expand(-1							,    self.num_choices							,    -1   ).contiguous()
								snake_case_   : Any						=			token_type_ids.unsqueeze(1   ).expand(-1							,    self.num_choices							,    -1   ).contiguous()
								snake_case_   : Optional[int]						=			input_mask.unsqueeze(1   ).expand(-1							,    self.num_choices							,    -1   ).contiguous()
								snake_case_   : Dict						=			model(
								    __A							,    attention_mask=__A							,    token_type_ids=__A							,    labels=__A							,    )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)   )





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Dict						=			self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		
								)   : Optional[int]						=			config_and_inputs
								snake_case_   : Optional[Any]						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       (
		    (
		        MegatronBertModel,
		        MegatronBertForMaskedLM,
		        MegatronBertForCausalLM,
		        MegatronBertForMultipleChoice,
		        MegatronBertForNextSentencePrediction,
		        MegatronBertForPreTraining,
		        MegatronBertForQuestionAnswering,
		        MegatronBertForSequenceClassification,
		        MegatronBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_lowerCamelCase			  =       (
		    {
		        '''feature-extraction''': MegatronBertModel,
		        '''fill-mask''': MegatronBertForMaskedLM,
		        '''question-answering''': MegatronBertForQuestionAnswering,
		        '''text-classification''': MegatronBertForSequenceClassification,
		        '''text-generation''': MegatronBertForCausalLM,
		        '''token-classification''': MegatronBertForTokenClassification,
		        '''zero-shot''': MegatronBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_lowerCamelCase			  =       True
		# test_resize_embeddings = False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase=False   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Any						=			super()._prepare_for_class(__A							,    __A							,    return_labels=__A   )

								if return_labels:
														if model_class in get_values(__A   ):
																				snake_case_   : Optional[int]						=			torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.seq_length)							,    dtype=torch.long							,    device=__A   )
																				snake_case_   : Union[str, Any]						=			torch.zeros(
																				    self.model_tester.batch_size							,    dtype=torch.long							,    device=__A   )
								return inputs_dict





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : str						=			MegatronBertModelTester(self   )
								snake_case_   : Any						=			ConfigTester(self							,    config_class=__A							,    hidden_size=3_7   )





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_model(*__A   )





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A   )




def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						return torch.tensor(
						    _lowercase					, dtype=torch.long					, device=_lowercase					, )


__lowerCAmelCase       :					int      					=					1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		@unittest.skip("""Model is not available."""   )
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			"""nvidia/megatron-bert-uncased-345m"""
								if "MYDIR" in os.environ:
														snake_case_   : Optional[int]						=			os.path.join(os.environ["""MYDIR"""]							,    __A   )
								snake_case_   : List[str]						=			MegatronBertModel.from_pretrained(__A   )
								model.to(__A   )
								model.half()
								snake_case_   : List[str]						=			_long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
								with torch.no_grad():
														snake_case_   : List[str]						=			model(__A   )[0]
								snake_case_   : List[str]						=			torch.Size((1, 9, 1_0_2_4)   )
								self.assertEqual(output.shape							,    __A   )

								snake_case_   : Optional[Any]						=			[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
								for ii in range(3   ):
														for jj in range(3   ):
																				snake_case_   : List[Any]						=			output[0, ii, jj]
																				snake_case_   : Tuple						=			expected[3 * ii + jj]
																				snake_case_   : Union[str, Any]						=			"""ii={} jj={} a={} b={}""".format(__A							,    __A							,    __A							,    __A   )
																				self.assertTrue(math.isclose(__A							,    __A							,    rel_tol=__A							,    abs_tol=__A   )							,    msg=__A   )










"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






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


						'''simple docstring'''






						snake_case_   : Union[str, 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

						snake_case_   : Any						=			"""huggingface/label-files"""
						snake_case_   : Tuple						=			num_labels
						snake_case_   : Dict						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : str						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

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

						snake_case_   : Dict						=			CvtConfig(num_labels=__UpperCamelCase					, idalabel=__UpperCamelCase					, labelaid=__UpperCamelCase    )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "13":
												snake_case_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

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

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

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

						snake_case_   : str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__UpperCamelCase    )
						for i in range(len(__UpperCamelCase    )    ):
												snake_case_   : Union[str, Any]						=			original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__UpperCamelCase    )
						model.save_pretrained(__UpperCamelCase    )
						image_processor.save_pretrained(__UpperCamelCase    )


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

if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					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       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

