
"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[int]      =    '''Wav2Vec2FeatureExtractor'''
						_lowerCamelCase:    Union[str, Any]      =    '''AutoTokenizer'''


						def __init__( self     :      Union[str, Any]  ,A_     :      Optional[Any]  ,A_     :      Any      )     ->   Optional[Any]:
							super().__init__(A_  ,A_      )
							A						   =   self.feature_extractor
							A						   =   False


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      int  ,A_     :      Tuple  ,**A_     :      Union[str, Any]      )     ->   List[Any]:
							try:
								return super().from_pretrained(A_  ,**A_      )
							except OSError:
								warnings.warn(
								    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
								    ' include a `tokenizer_class` attribute is deprecated and will be '
								    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
								    ' attribute to either your `config.json` or `tokenizer_config.json` '
								    'file to suppress this warning: '  ,A_  ,)

								A						   =   WavaVecaFeatureExtractor.from_pretrained(A_  ,**A_      )
								A						   =   WavaVecaCTCTokenizer.from_pretrained(A_  ,**A_      )

								return cls(feature_extractor=A_  ,tokenizer=A_      )


						def __call__( self     :      Optional[int]  ,*A_     :      str  ,**A_     :      List[str]      )     ->   Dict:
							# For backward compatibility
							if self._in_target_context_manager:
								return self.current_processor(*A_  ,**A_      )

							if "raw_speech" in kwargs:
								warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'      )
								A						   =   kwargs.pop('raw_speech'      )
							else:
								A						   =   kwargs.pop('audio'  ,A_      )
							A						   =   kwargs.pop('sampling_rate'  ,A_      )
							A						   =   kwargs.pop('text'  ,A_      )
							if len(A_      ) > 0:
								A						   =   args[0]
								A						   =   args[1:]

							if audio is None and text is None:
								raise ValueError('You need to specify either an `audio` or `text` input to process.'      )

							if audio is not None:
								A						   =   self.feature_extractor(A_  ,*A_  ,sampling_rate=A_  ,**A_      )
							if text is not None:
								A						   =   self.tokenizer(A_  ,**A_      )

							if text is None:
								return inputs
							elif audio is None:
								return encodings
							else:
								A						   =   encodings['input_ids']
								return inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,*A_     :      Tuple  ,**A_     :      Optional[Any]      )     ->   int:
							# For backward compatibility
							if self._in_target_context_manager:
								return self.current_processor.pad(*A_  ,**A_      )

							A						   =   kwargs.pop('input_features'  ,A_      )
							A						   =   kwargs.pop('labels'  ,A_      )
							if len(A_      ) > 0:
								A						   =   args[0]
								A						   =   args[1:]

							if input_features is not None:
								A						   =   self.feature_extractor.pad(A_  ,*A_  ,**A_      )
							if labels is not None:
								A						   =   self.tokenizer.pad(A_  ,**A_      )

							if labels is None:
								return input_features
							elif input_features is None:
								return labels
							else:
								A						   =   labels['input_ids']
								return input_features


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,*A_     :      str  ,**A_     :      Optional[Any]      )     ->   Union[str, Any]:
							return self.tokenizer.batch_decode(*A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,*A_     :      int  ,**A_     :      Optional[int]      )     ->   List[Any]:
							return self.tokenizer.decode(*A_  ,**A_      )




						@contextmanager
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your audio inputs, or in a separate call.'      )
							A						   =   True
							A						   =   self.tokenizer
							yield
							A						   =   self.feature_extractor
							A						   =   False

"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def       _snake_case				(		snake_case__   :     int     ):
	A						   =   SwinvaConfig()
	A						   =   swinva_name.split('_'     )

	A						   =   name_split[1]
	if "to" in name_split[3]:
		A						   =   int(name_split[3][-3:]     )
	else:
		A						   =   int(name_split[3]     )
	if "to" in name_split[2]:
		A						   =   int(name_split[2][-2:]     )
	else:
		A						   =   int(name_split[2][6:]     )

	if model_size == "tiny":
		A						   =   96
		A						   =   (2, 2, 6, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "small":
		A						   =   96
		A						   =   (2, 2, 18, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "base":
		A						   =   128
		A						   =   (2, 2, 18, 2)
		A						   =   (4, 8, 16, 32)
	else:
		A						   =   192
		A						   =   (2, 2, 18, 2)
		A						   =   (6, 12, 24, 48)

	if "to" in swinva_name:
		A						   =   (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		A						   =   2_1841
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-22k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	else:
		A						   =   1000
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-1k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	A						   =   img_size
	A						   =   num_classes
	A						   =   embed_dim
	A						   =   depths
	A						   =   num_heads
	A						   =   window_size

	return config






def       _snake_case				(		snake_case__   :     List[Any]     ):
	if "patch_embed.proj" in name:
		A						   =   name.replace('patch_embed.proj'	,      'embeddings.patch_embeddings.projection'     )
	if "patch_embed.norm" in name:
		A						   =   name.replace('patch_embed.norm'	,      'embeddings.norm'     )
	if "layers" in name:
		A						   =   'encoder.' + name
	if "attn.proj" in name:
		A						   =   name.replace('attn.proj'	,      'attention.output.dense'     )
	if "attn" in name:
		A						   =   name.replace('attn'	,      'attention.self'     )
	if "norm1" in name:
		A						   =   name.replace('norm1'	,      'layernorm_before'     )
	if "norm2" in name:
		A						   =   name.replace('norm2'	,      'layernorm_after'     )
	if "mlp.fc1" in name:
		A						   =   name.replace('mlp.fc1'	,      'intermediate.dense'     )
	if "mlp.fc2" in name:
		A						   =   name.replace('mlp.fc2'	,      'output.dense'     )
	if "q_bias" in name:
		A						   =   name.replace('q_bias'	,      'query.bias'     )
	if "k_bias" in name:
		A						   =   name.replace('k_bias'	,      'key.bias'     )
	if "v_bias" in name:
		A						   =   name.replace('v_bias'	,      'value.bias'     )
	if "cpb_mlp" in name:
		A						   =   name.replace('cpb_mlp'	,      'continuous_position_bias_mlp'     )
	if name == "norm.weight":
		A						   =   'layernorm.weight'
	if name == "norm.bias":
		A						   =   'layernorm.bias'

	if "head" in name:
		A						   =   name.replace('head'	,      'classifier'     )
	else:
		A						   =   'swinv2.' + name

	return name






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     List[Any]     ):
	for key in orig_state_dict.copy().keys():
		A						   =   orig_state_dict.pop(snake_case__     )

		if "mask" in key:
			continue
		elif "qkv" in key:
			A						   =   key.split('.'     )
			A						   =   int(key_split[1]     )
			A						   =   int(key_split[3]     )
			A						   =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				A						   =   val[:dim, :]
				A						   =   val[dim : dim * 2, :]
				A						   =   val[-dim:, :]
			else:
				A						   =   val[:dim]
				A						   =   val[
				    dim : dim * 2
				]
				A						   =   val[-dim:]
		else:
			A						   =   val

	return orig_state_dict






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	A						   =   timm.create_model(snake_case__	,      pretrained=snake_case__     )
	timm_model.eval()

	A						   =   get_swinva_config(snake_case__     )
	A						   =   SwinvaForImageClassification(snake_case__     )
	model.eval()

	A						   =   convert_state_dict(timm_model.state_dict()	,      snake_case__     )
	model.load_state_dict(snake_case__     )

	A						   =   'http://images.cocodataset.org/val2017/000000039769.jpg'

	A						   =   AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'	,      '-'     )     )     )
	A						   =   Image.open(requests.get(snake_case__	,      stream=snake_case__     ).raw     )
	A						   =   image_processor(images=snake_case__	,      return_tensors='pt'     )

	A						   =   timm_model(inputs['pixel_values']     )
	A						   =   model(**snake_case__     ).logits

	assert torch.allclose(snake_case__	,      snake_case__	,      atol=1e-3     )

	print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'     )
	model.save_pretrained(snake_case__     )

	print(F'Saving image processor to {pytorch_dump_folder_path}'     )
	image_processor.save_pretrained(snake_case__     )

	model.push_to_hub(
	    repo_path_or_name=Path(snake_case__	,      snake_case__     )	,      organization='nandwalritik'	,      commit_message='Add model'	,      )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swinv2_name''',
		    default='''swinv2_tiny_patch4_window8_256''',
		    type=str,
		    help='''Name of the Swinv2 timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		_lowercase							   =				parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)