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       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    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       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[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       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							[]
						token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')						)
						return token
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							[]
						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       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE							:    List[str]							=							1_000

						SCREAMING_SNAKE_CASE							:    Dict							=							"huggingface/label-files"
						SCREAMING_SNAKE_CASE							:    List[Any]							=							num_labels
						SCREAMING_SNAKE_CASE							:    Tuple							=							json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,  _lowerCamelCase		,  repo_type='''dataset'''						)						)		,  '''r'''						)						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							{int(_lowerCamelCase						): v for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:    Any							=							idalabel
						SCREAMING_SNAKE_CASE							:    List[Any]							=							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							CvtConfig(num_labels=_lowerCamelCase		,  idalabel=_lowerCamelCase		,  labelaid=_lowerCamelCase						)

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

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

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

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							CvtForImageClassification(_lowerCamelCase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''						)
						SCREAMING_SNAKE_CASE							:    int							=							image_size
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.load(_lowerCamelCase		,  map_location=torch.device('''cpu'''						)						)

						SCREAMING_SNAKE_CASE							:    Any							=							OrderedDict()
						SCREAMING_SNAKE_CASE							:    int							=							[]

						for idx in range(len(config.depth						)						):
												if config.cls_token[idx]:
																		SCREAMING_SNAKE_CASE							:    Dict							=							list_of_state_dict + cls_token(_lowerCamelCase						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							list_of_state_dict + embeddings(_lowerCamelCase						)
												for cnt in range(config.depth[idx]						):
																		SCREAMING_SNAKE_CASE							:    Any							=							list_of_state_dict + attention(_lowerCamelCase		,  _lowerCamelCase						)

						SCREAMING_SNAKE_CASE							:    List[str]							=							list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(_lowerCamelCase						)
						for i in range(len(_lowerCamelCase						)						):
												SCREAMING_SNAKE_CASE							:    Dict							=							original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(_lowerCamelCase						)
						model.save_pretrained(_lowerCamelCase						)
						image_processor.save_pretrained(_lowerCamelCase						)


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

if __name__ == "__main__":
					__UpperCamelCase		:       Union[str, Any]									=     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		:       List[str]									=     parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






from PIL import Image

def 			lowercase_				(			_lowerCamelCase							:       Image		,	_lowerCamelCase							:       int):
    lowercase__ :   List[str]    					=				(259 * (level + 255)) / (255 * (259 - level))

    def contrast(_lowerCamelCase							:       int) -> int:
        return int(128 + factor * (c - 128))

    return img.point(_lowerCamelCase)


if __name__ == "__main__":
      # Load image
      with Image.open('''image_data/lena.jpg''') as img:
            # Change contrast to 170
            UpperCamelCase  							=						change_contrast(img, 170)
            cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')



