



"""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				(							UpperCAmelCase_ :       str     )	-> int:






	"""simple docstring"""





	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				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Union[str, Any]     )	-> Union[str, Any]:






	"""simple docstring"""





	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				(							UpperCAmelCase_ :       Tuple     )	-> int:






	"""simple docstring"""





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

def       _lowerCamelCase				(							)	-> Union[str, Any]:






	"""simple docstring"""





	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				(							UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Tuple     )	-> Union[str, Any]:






	"""simple docstring"""





	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__											=					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__":
			UpperCamelCase     =							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."""
			)

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








import functools
import operator

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


snake_case__				:	Union[str, Any]					       =					logging.get_logger(__name__)

snake_case__				:	Tuple					       =					{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"unispeech-sat"



       def __init__(						self				:				List[str]		,				__a				:				Dict=32		,				__a				:				int=768		,				__a				:				int=12		,				__a				:				Tuple=12		,				__a				:				Optional[int]=3_072		,				__a				:				int="gelu"		,				__a				:				Optional[int]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				Any=0.1		,				__a				:				str=0.0		,				__a				:				List[str]=0.0		,				__a				:				int=0.1		,				__a				:				List[Any]=0.1		,				__a				:				Tuple=0.02		,				__a				:				str=1e-5		,				__a				:				Optional[int]="group"		,				__a				:				Any="gelu"		,				__a				:				Optional[Any]=(512, 512, 512, 512, 512, 512, 512)		,				__a				:				Optional[Any]=(5, 2, 2, 2, 2, 2, 2)		,				__a				:				List[Any]=(10, 3, 3, 3, 3, 2, 2)		,				__a				:				Optional[Any]=False		,				__a				:				Optional[int]=128		,				__a				:				Dict=16		,				__a				:				str=False		,				__a				:				List[Any]=True		,				__a				:				Tuple=0.05		,				__a				:				str=10		,				__a				:				Any=2		,				__a				:				Any=0.0		,				__a				:				List[Any]=10		,				__a				:				str=0		,				__a				:				str=320		,				__a				:				int=2		,				__a				:				Optional[Any]=0.1		,				__a				:				Optional[int]=100		,				__a				:				Any=256		,				__a				:				Optional[int]=256		,				__a				:				int=0.1		,				__a				:				Tuple="mean"		,				__a				:				List[str]=False		,				__a				:				Optional[Any]=False		,				__a				:				List[Any]=256		,				__a				:				Dict=(512, 512, 512, 512, 1_500)		,				__a				:				int=(5, 3, 3, 1, 1)		,				__a				:				str=(1, 2, 3, 1, 1)		,				__a				:				str=512		,				__a				:				str=0		,				__a				:				List[Any]=1		,				__a				:				Optional[Any]=2		,				__a				:				Optional[int]=504		,				**__a				:				Tuple		,				)					->Dict:
          super().__init__(**__a		,				pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a							)
          lowerCamelCase_						:   Any		= hidden_size
          lowerCamelCase_						:   Optional[int]		= feat_extract_norm
          lowerCamelCase_						:   Dict		= feat_extract_activation
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   str		= conv_bias
          lowerCamelCase_						:   int		= num_conv_pos_embeddings
          lowerCamelCase_						:   List[Any]		= num_conv_pos_embedding_groups
          lowerCamelCase_						:   str		= len(self.conv_dim							)
          lowerCamelCase_						:   Any		= num_hidden_layers
          lowerCamelCase_						:   List[str]		= intermediate_size
          lowerCamelCase_						:   Tuple		= hidden_act
          lowerCamelCase_						:   List[str]		= num_attention_heads
          lowerCamelCase_						:   Any		= hidden_dropout
          lowerCamelCase_						:   Optional[int]		= attention_dropout
          lowerCamelCase_						:   Tuple		= activation_dropout
          lowerCamelCase_						:   str		= feat_proj_dropout
          lowerCamelCase_						:   Any		= final_dropout
          lowerCamelCase_						:   Optional[Any]		= layerdrop
          lowerCamelCase_						:   str		= layer_norm_eps
          lowerCamelCase_						:   str		= initializer_range
          lowerCamelCase_						:   Optional[Any]		= vocab_size
          lowerCamelCase_						:   Optional[int]		= num_clusters
          lowerCamelCase_						:   List[Any]		= do_stable_layer_norm
          lowerCamelCase_						:   Tuple		= use_weighted_layer_sum

          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_						:   List[str]		= apply_spec_augment
          lowerCamelCase_						:   str		= mask_time_prob
          lowerCamelCase_						:   List[Any]		= mask_time_length
          lowerCamelCase_						:   Optional[int]		= mask_time_min_masks
          lowerCamelCase_						:   List[Any]		= mask_feature_prob
          lowerCamelCase_						:   Optional[int]		= mask_feature_length
          lowerCamelCase_						:   Dict		= mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          lowerCamelCase_						:   Dict		= num_codevectors_per_group
          lowerCamelCase_						:   List[str]		= num_codevector_groups
          lowerCamelCase_						:   Union[str, Any]		= contrastive_logits_temperature
          lowerCamelCase_						:   Optional[int]		= feat_quantizer_dropout
          lowerCamelCase_						:   List[Any]		= num_negatives
          lowerCamelCase_						:   Optional[int]		= codevector_dim
          lowerCamelCase_						:   str		= proj_codevector_dim
          lowerCamelCase_						:   List[Any]		= diversity_loss_weight

          # ctc loss
          lowerCamelCase_						:   Optional[int]		= ctc_loss_reduction
          lowerCamelCase_						:   int		= ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          lowerCamelCase_						:   Dict		= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Optional[int]		= xvector_output_dim




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[Any]:
          return functools.reduce(operator.mul		,				self.conv_stride		,				1							)





