






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



def  __lowerCamelCase					(					):
				A__							 =       []
				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					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       '''imagenet-1k-id2label.json'''
				A__							 =       1000

				A__							 =       '''huggingface/label-files'''
				A__							 =       num_labels
				A__							 =       json.load(open(cached_download(hf_hub_url(__UpperCamelCase       ,__UpperCamelCase       ,repo_type='dataset'				)				)       ,'r'				)				)
				A__							 =       {int(__UpperCamelCase				): v for k, v in idalabel.items()}

				A__							 =       idalabel
				A__							 =       {v: k for k, v in idalabel.items()}

				A__							 =       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":
								A__							 =       [1, 2, 10]

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

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								A__							 =       [2, 2, 20]
								A__							 =       [3, 12, 16]
								A__							 =       [192, 768, 1024]

				A__							 =       CvtForImageClassification(__UpperCamelCase				)
				A__							 =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
				A__							 =       image_size
				A__							 =       torch.load(__UpperCamelCase       ,map_location=torch.device('cpu'				)				)

				A__							 =       OrderedDict()
				A__							 =       []

				for idx in range(len(config.depth				)				):
								if config.cls_token[idx]:
												A__							 =       list_of_state_dict + cls_token(__UpperCamelCase				)
								A__							 =       list_of_state_dict + embeddings(__UpperCamelCase				)
								for cnt in range(config.depth[idx]				):
												A__							 =       list_of_state_dict + attention(__UpperCamelCase       ,__UpperCamelCase				)

				A__							 =       list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__UpperCamelCase				)
				for i in range(len(__UpperCamelCase				)				):
								A__							 =       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__":
					SCREAMING_SNAKE_CASE						:						Tuple					   =       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.'''
					)

					SCREAMING_SNAKE_CASE						:						Optional[int]					   =       parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)












"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					"""WhisperFeatureExtractor"""
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""WhisperTokenizer"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(__a  ,					__a						)
					A__							 =       self.feature_extractor
					A__							 =       False


	def       _UpperCAmelCase						(      self  ,					__a=None  ,					__a=None  ,					__a=True						):


					"""simple docstring"""

					return self.tokenizer.get_decoder_prompt_ids(task=__a  ,					language=__a  ,					no_timestamps=__a						)


	def __call__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					if self._in_target_context_manager:
									return self.current_processor(*__a  ,					**__a						)

					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       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					return self.tokenizer.batch_decode(*__a  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					return self.tokenizer.decode(*__a  ,					**__a						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a="np"						):


					"""simple docstring"""

					return self.tokenizer.get_prompt_ids(__a  ,					return_tensors=__a						)


