
"""simple docstring"""



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	_snake_case          =						'''platform'''
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					PegasusConfig
				SCREAMING_SNAKE_CASE_:							Any							=					{}
				SCREAMING_SNAKE_CASE_:							str							=					"gelu"


				def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			int=13				,     UpperCAmelCase_	:			List[Any]=7				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Tuple=False				,     UpperCAmelCase_	:			Union[str, Any]=99				,     UpperCAmelCase_	:			List[Any]=32				,     UpperCAmelCase_	:			Any=5				,     UpperCAmelCase_	:			Union[str, Any]=4				,     UpperCAmelCase_	:			int=37				,     UpperCAmelCase_	:			Tuple=0.1				,     UpperCAmelCase_	:			List[str]=0.1				,     UpperCAmelCase_	:			Union[str, Any]=20				,     UpperCAmelCase_	:			Tuple=2				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			int=0				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			seq_length
											_lowerCAmelCase  	=			is_training
											_lowerCAmelCase  	=			use_labels
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_size

											_lowerCAmelCase  	=			hidden_dropout_prob
											_lowerCAmelCase  	=			attention_probs_dropout_prob
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			eos_token_id
											_lowerCAmelCase  	=			pad_token_id
											_lowerCAmelCase  	=			bos_token_id


				def 				__lowerCamelCase	(					self	:			Any  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size  ).clip(3				,     self.vocab_size  )
											_lowerCAmelCase  	=			np.expand_dims(np.array([self.eos_token_id] * self.batch_size  )				,     1  )
											_lowerCAmelCase  	=			np.concatenate([input_ids, eos_tensor]				,     axis=1  )

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

											_lowerCAmelCase  	=			self.config_cls(
											    vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_ids=[2]				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     decoder_start_token_id=self.pad_token_id				,     **self.config_updates				,     )
											_lowerCAmelCase  	=			prepare_pegasus_inputs_dict(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											return config, inputs_dict


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







											"""simple docstring"""




											_lowerCAmelCase  	=			20
											_lowerCAmelCase  	=			model_class_name(UpperCAmelCase_  )

											_lowerCAmelCase  	=			model.encode(inputs_dict['input_ids']  )

											_lowerCAmelCase	,		_lowerCAmelCase  	=			(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											_lowerCAmelCase  	=			model.init_cache(decoder_input_ids.shape[0]				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,     dtype='i4'  )

											_lowerCAmelCase  	=			jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,     )
											_lowerCAmelCase  	=			model.decode(
											    decoder_input_ids[:, :-1]				,     UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_				,     past_key_values=UpperCAmelCase_				,     decoder_position_ids=UpperCAmelCase_				,     )

											_lowerCAmelCase  	=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,     dtype='i4'  )
											_lowerCAmelCase  	=			model.decode(
											    decoder_input_ids[:, -1:]				,     UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_				,     past_key_values=outputs_cache.past_key_values				,     decoder_position_ids=UpperCAmelCase_				,     )

											_lowerCAmelCase  	=			model.decode(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
											self.parent.assertTrue(diff < 1E-3				,     msg=F"""Max diff is {diff}"""  )







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







											"""simple docstring"""




											_lowerCAmelCase  	=			20
											_lowerCAmelCase  	=			model_class_name(UpperCAmelCase_  )

											_lowerCAmelCase  	=			model.encode(inputs_dict['input_ids']  )

											_lowerCAmelCase	,		_lowerCAmelCase  	=			(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											_lowerCAmelCase  	=			jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
											    ]				,     axis=-1				,     )

											_lowerCAmelCase  	=			model.init_cache(decoder_input_ids.shape[0]				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,     )

											_lowerCAmelCase  	=			model.decode(
											    decoder_input_ids[:, :-1]				,     UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_				,     past_key_values=UpperCAmelCase_				,     decoder_position_ids=UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,     dtype='i4'  )
											_lowerCAmelCase  	=			model.decode(
											    decoder_input_ids[:, -1:]				,     UpperCAmelCase_				,     past_key_values=outputs_cache.past_key_values				,     decoder_attention_mask=UpperCAmelCase_				,     decoder_position_ids=UpperCAmelCase_				,     )

											_lowerCAmelCase  	=			model.decode(UpperCAmelCase_				,     UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_  )

											_lowerCAmelCase  	=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
											self.parent.assertTrue(diff < 1E-3				,     msg=F"""Max diff is {diff}"""  )






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					int=None     ,  SCREAMING_SNAKE_CASE:					List[str]=None     ,  ):






							"""simple docstring"""



							if attention_mask is None:
														_lowerCAmelCase  	=			np.not_equal(SCREAMING_SNAKE_CASE     ,  config.pad_token_id						).astype(np.inta						)
							if decoder_attention_mask is None:
														_lowerCAmelCase  	=			np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape     ,  dtype=np.inta						),
														        np.not_equal(decoder_input_ids[:, 1:]     ,  config.pad_token_id						).astype(np.inta						),
														    ]     ,  axis=-1     ,  )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}

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






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_:							int							=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE_:							int							=					True
				SCREAMING_SNAKE_CASE_:							int							=					False
				SCREAMING_SNAKE_CASE_:							List[str]							=					False
				SCREAMING_SNAKE_CASE_:							Tuple							=					False


				def 				__lowerCamelCase	(					self	:			Dict  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			FlaxPegasusModelTester(self  )
											_lowerCAmelCase  	=			ConfigTester(self				,     config_class=UpperCAmelCase_  )


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







											"""simple docstring"""




											self.config_tester.run_common_tests()


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







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )


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







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )


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







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__  ):
																									_lowerCAmelCase  	=			self._prepare_for_class(UpperCAmelCase_				,     UpperCAmelCase_  )
																									_lowerCAmelCase  	=			model_class(UpperCAmelCase_  )

																									@jax.jit
																									def encode_jitted(UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Tuple=None				,     **UpperCAmelCase_	:			Any  ):
																																return model.encode(input_ids=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_  )

																									with self.subTest('JIT Enabled'  ):
																																_lowerCAmelCase  	=			encode_jitted(**UpperCAmelCase_  ).to_tuple()

																									with self.subTest('JIT Disabled'  ):
																																with jax.disable_jit():
																																							_lowerCAmelCase  	=			encode_jitted(**UpperCAmelCase_  ).to_tuple()

																									self.assertEqual(len(UpperCAmelCase_  )				,     len(UpperCAmelCase_  )  )
																									for jitted_output, output in zip(UpperCAmelCase_				,     UpperCAmelCase_  ):
																																self.assertEqual(jitted_output.shape				,     output.shape  )


				def 				__lowerCamelCase	(					self	:			Any  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__  ):
																									_lowerCAmelCase  	=			model_class(UpperCAmelCase_  )
																									_lowerCAmelCase  	=			model.encode(inputs_dict['input_ids']				,     inputs_dict['attention_mask']  )

																									_lowerCAmelCase  	=			{
																									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																									    'encoder_outputs': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Optional[int]  ):
																																return model.decode(
																																    decoder_input_ids=UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_				,     encoder_outputs=UpperCAmelCase_				,     )

																									with self.subTest('JIT Enabled'  ):
																																_lowerCAmelCase  	=			decode_jitted(**UpperCAmelCase_  ).to_tuple()

																									with self.subTest('JIT Disabled'  ):
																																with jax.disable_jit():
																																							_lowerCAmelCase  	=			decode_jitted(**UpperCAmelCase_  ).to_tuple()

																									self.assertEqual(len(UpperCAmelCase_  )				,     len(UpperCAmelCase_  )  )
																									for jitted_output, output in zip(UpperCAmelCase_				,     UpperCAmelCase_  ):
																																self.assertEqual(jitted_output.shape				,     output.shape  )


				@slow
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[Any]:







											"""simple docstring"""




											for model_class_name in self.all_model_classes:
																		_lowerCAmelCase  	=			model_class_name.from_pretrained('google/pegasus-large'				,     from_pt=UpperCAmelCase_  )
																		_lowerCAmelCase  	=			np.ones((1, 1)  )
																		_lowerCAmelCase  	=			model(UpperCAmelCase_  )
																		self.assertIsNotNone(UpperCAmelCase_  )







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







											"""simple docstring"""




											_lowerCAmelCase  	=			FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'  )
											_lowerCAmelCase  	=			PegasusTokenizer.from_pretrained('google/pegasus-xsum'  )

											_lowerCAmelCase  	=			[
											    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
											    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
											]

											_lowerCAmelCase  	=			[
											    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
											    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
											]

											_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     return_tensors='np'				,     truncation=UpperCAmelCase_				,     max_length=512				,     padding=UpperCAmelCase_  )
											_lowerCAmelCase  	=			model.generate(**UpperCAmelCase_				,     num_beams=2  ).sequences
											_lowerCAmelCase  	=			tokenizer.batch_decode(UpperCAmelCase_				,     skip_special_tokens=UpperCAmelCase_  )
											assert tgt_text == decoded








"""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)






