



'''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        __A   (							_SCREAMING_SNAKE_CASE     :  Dict	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    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        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[int]					,   _SCREAMING_SNAKE_CASE     :  List[Any]	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    Any						    =							[]
					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        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[int]	):







					"""simple docstring"""






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



def        __A   (							):







					"""simple docstring"""






					__SCREAMING_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        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[int]					,   _SCREAMING_SNAKE_CASE     :  Dict					,   _SCREAMING_SNAKE_CASE     :  List[str]					,   _SCREAMING_SNAKE_CASE     :  Dict	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    Dict						    =							"imagenet-1k-id2label.json"
					__SCREAMING_SNAKE_CASE				:    Dict						    =							1_0_0_0

					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							"huggingface/label-files"
					__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							num_labels
					__SCREAMING_SNAKE_CASE				:    int						    =							json.load(open(cached_download(hf_hub_url(__lowerCamelCase					,   __lowerCamelCase					,   repo_type="dataset"	)	)					,   "r"	)	)
					__SCREAMING_SNAKE_CASE				:    Dict						    =							{int(__lowerCamelCase	): v for k, v in idalabel.items()}

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

					__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							CvtConfig(num_labels=__lowerCamelCase					,   idalabel=__lowerCamelCase					,   labelaid=__lowerCamelCase	)

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

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

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

					__SCREAMING_SNAKE_CASE				:    Dict						    =							CvtForImageClassification(__lowerCamelCase	)
					__SCREAMING_SNAKE_CASE				:    Dict						    =							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"	)
					__SCREAMING_SNAKE_CASE				:    int						    =							image_size
					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							torch.load(__lowerCamelCase					,   map_location=torch.device("cpu"	)	)

					__SCREAMING_SNAKE_CASE				:    Dict						    =							OrderedDict()
					__SCREAMING_SNAKE_CASE				:    Any						    =							[]

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

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

					model.load_state_dict(__lowerCamelCase	)
					model.save_pretrained(__lowerCamelCase	)
					image_processor.save_pretrained(__lowerCamelCase	)


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

if __name__ == "__main__":
					lowercase      			=	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.'''
					)

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




'''simple docstring'''



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1       ,  int((1.0 - self.overlap) * self.chunk_length ) )




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																								    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
																								    F' {self.sampling_rate} and not {sampling_rate}.' )
												else:
																		logger.warning(
																		    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug.""" )

												if padding and truncation:
																		raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
												elif padding is None:
																		# by default let's pad the inputs
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_audio.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase ).T]

												# verify inputs are valid
												for idx, example in enumerate(_lowercase ):
																		if example.ndim > 2:
																								raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
																		if self.feature_size == 1 and example.ndim != 1:
																								raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																								raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					BatchFeature({"""input_values""": raw_audio} )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

												_lowerCAmelCase    		=					[]
												for example in padded_inputs.pop("""input_values""" ):
																		if self.feature_size == 1:
																								_lowerCAmelCase    		=					example[..., None]
																		input_values.append(example.T )

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
