


'''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      (   UpperCAmelCase_		:		Any	):
 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      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]	):
 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      (   UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						[]
 token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")	)
 return token





def __snake_case      (   ):
 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      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						"imagenet-1k-id2label.json"
 lowerCamelCase_  				=						1000

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

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

 lowerCamelCase_  				=						lowerCamelCase_  				=						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":
  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(UpperCAmelCase_	)
 lowerCamelCase_  				=						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"	)
 lowerCamelCase_  				=						image_size
 lowerCamelCase_  				=						torch.load(UpperCAmelCase_      ,       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(UpperCAmelCase_	)
  lowerCamelCase_  				=						list_of_state_dict + embeddings(UpperCAmelCase_	)
  for cnt in range(config.depth[idx]	):
   lowerCamelCase_  				=						list_of_state_dict + attention(UpperCAmelCase_      ,       UpperCAmelCase_	)

 lowerCamelCase_  				=						list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(UpperCAmelCase_	)
 for i in range(len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						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__":
     a_						:       Dict      			=				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."""
     )

     a_						:       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'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						feat_extract_norm
   lowerCamelCase_  				=						feat_extract_activation
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						conv_bias
   lowerCamelCase_  				=						num_conv_pos_embeddings
   lowerCamelCase_  				=						num_conv_pos_embedding_groups
   lowerCamelCase_  				=						len(self.conv_dim  )
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						feat_proj_dropout
   lowerCamelCase_  				=						final_dropout
   lowerCamelCase_  				=						layerdrop
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_ctc_classes
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						do_stable_layer_norm
   lowerCamelCase_  				=						use_weighted_layer_sum
   lowerCamelCase_  				=						classifier_proj_size

   if (
       (len(self.conv_stride  ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
       or (len(self.conv_dim  ) != self.num_feat_extract_layers)
   ):
    raise ValueError(
        "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
        " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
        f''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
        f''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   lowerCamelCase_  				=						num_codevectors_per_group
   lowerCamelCase_  				=						num_codevector_groups
   lowerCamelCase_  				=						contrastive_logits_temperature
   lowerCamelCase_  				=						feat_quantizer_dropout
   lowerCamelCase_  				=						num_negatives
   lowerCamelCase_  				=						codevector_dim
   lowerCamelCase_  				=						proj_codevector_dim
   lowerCamelCase_  				=						diversity_loss_weight

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return functools.reduce(operator.mul    ,						self.conv_stride    ,						1  )



