


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__	(      a	:   List[Any]				)    ->	Tuple:





			"""simple docstring"""
			a__ :int						  =    []
			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__	(      a	:   Tuple	,  a	:   List[str]				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Union[str, 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 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    []
			token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")				)
			return token


def 				lowerCamelCase__	(      )    ->	Optional[int]:





			"""simple docstring"""
			a__ :Optional[int]						  =    []
			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__	(      a	:   List[Any]	,  a	:   Optional[int]	,  a	:   Optional[int]	,  a	:   List[str]				)    ->	Tuple:





			"""simple docstring"""
			a__ :str						  =    "imagenet-1k-id2label.json"
			a__ :List[Any]						  =    1_000

			a__ :int						  =    "huggingface/label-files"
			a__ :Any						  =    num_labels
			a__ :int						  =    json.load(open(cached_download(hf_hub_url(a	,  a	,  repo_type="dataset"				)				)	,  "r"				)				)
			a__ :Union[str, Any]						  =    {int(a				): v for k, v in idalabel.items()}

			a__ :Optional[Any]						  =    idalabel
			a__ :Tuple						  =    {v: k for k, v in idalabel.items()}

			a__ :str						  =    CvtConfig(num_labels=a	,  idalabel=a	,  labelaid=a				)

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

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

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						a__ :Tuple						  =    [2, 2, 20]
						a__ :Optional[Any]						  =    [3, 12, 16]
						a__ :Dict						  =    [192, 768, 1_024]

			a__ :str						  =    CvtForImageClassification(a				)
			a__ :int						  =    AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				)
			a__ :Dict						  =    image_size
			a__ :Dict						  =    torch.load(a	,  map_location=torch.device("cpu"				)				)

			a__ :int						  =    OrderedDict()
			a__ :Optional[int]						  =    []

			for idx in range(len(config.depth				)				):
						if config.cls_token[idx]:
									a__ :List[Any]						  =    list_of_state_dict + cls_token(a				)
						a__ :Optional[int]						  =    list_of_state_dict + embeddings(a				)
						for cnt in range(config.depth[idx]				):
									a__ :Tuple						  =    list_of_state_dict + attention(a	,  a				)

			a__ :int						  =    list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(a				)
			for i in range(len(a				)				):
						a__ :Any						  =    original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(a				)
			model.save_pretrained(a				)
			image_processor.save_pretrained(a				)


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

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

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






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'pegasus'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Any   ,     __A						:   Dict=50265   ,     __A						:   List[Any]=1024   ,     __A						:   int=12   ,     __A						:   Optional[Any]=4096   ,     __A						:   Optional[int]=16   ,     __A						:   Dict=12   ,     __A						:   List[Any]=4096   ,     __A						:   List[str]=16   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=True   ,     __A						:   str="gelu"   ,     __A						:   Tuple=1024   ,     __A						:   Any=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=0.0   ,     __A						:   Tuple=0.02   ,     __A						:   Union[str, Any]=0   ,     __A						:   Union[str, Any]=False   ,     __A						:   Optional[Any]=0   ,     __A						:   Tuple=1   ,     __A						:   str=1   ,     **__A						:   Any   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    vocab_size
									a__ :List[str]						  =    max_position_embeddings
									a__ :int						  =    d_model
									a__ :Union[str, Any]						  =    encoder_ffn_dim
									a__ :List[Any]						  =    encoder_layers
									a__ :Union[str, Any]						  =    encoder_attention_heads
									a__ :Tuple						  =    decoder_ffn_dim
									a__ :List[Any]						  =    decoder_layers
									a__ :Tuple						  =    decoder_attention_heads
									a__ :Optional[int]						  =    dropout
									a__ :str						  =    attention_dropout
									a__ :Optional[int]						  =    activation_dropout
									a__ :str						  =    activation_function
									a__ :Dict						  =    init_std
									a__ :Any						  =    encoder_layerdrop
									a__ :int						  =    decoder_layerdrop
									a__ :Union[str, Any]						  =    use_cache
									a__ :List[Any]						  =    encoder_layers
									a__ :Dict						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A   ,     eos_token_id=__A   ,     is_encoder_decoder=__A   ,     decoder_start_token_id=__A   ,     forced_eos_token_id=__A   ,     **__A   ,     )




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									return self.encoder_attention_heads






						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return self.d_model


