






"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def 					_A			(__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		SwinvaConfig()
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		swinva_name.split('''_'''							)

    SCREAMING_SNAKE_CASE_      :  int    						=		name_split[1]
    if "to" in name_split[3]:
        SCREAMING_SNAKE_CASE_      :  str    						=		int(name_split[3][-3:]							)
    else:
        SCREAMING_SNAKE_CASE_      :  Any    						=		int(name_split[3]							)
    if "to" in name_split[2]:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		int(name_split[2][-2:]							)
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		int(name_split[2][6:]							)

    if model_size == "tiny":
        SCREAMING_SNAKE_CASE_      :  int    						=		96
        SCREAMING_SNAKE_CASE_      :  Dict    						=		(2, 2, 6, 2)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(3, 6, 12, 24)
    elif model_size == "small":
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		96
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_      :  int    						=		(3, 6, 12, 24)
    elif model_size == "base":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		1_28
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_      :  Any    						=		(4, 8, 16, 32)
    else:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		1_92
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(6, 12, 24, 48)

    if "to" in swinva_name:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		2_18_41
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_      :  Any    						=		'''imagenet-22k-id2label.json'''
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		json.load(open(hf_hub_download(__a    ,				__a    ,				repo_type='''dataset'''							)    ,				'''r'''							)							)
        SCREAMING_SNAKE_CASE_      :  int    						=		{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_      :  str    						=		idalabel
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		{v: k for k, v in idalabel.items()}

    else:
        SCREAMING_SNAKE_CASE_      :  Any    						=		10_00
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_      :  Any    						=		'''imagenet-1k-id2label.json'''
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		json.load(open(hf_hub_download(__a    ,				__a    ,				repo_type='''dataset'''							)    ,				'''r'''							)							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		idalabel
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE_      :  Dict    						=		img_size
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_classes
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		embed_dim
    SCREAMING_SNAKE_CASE_      :  str    						=		depths
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_heads
    SCREAMING_SNAKE_CASE_      :  str    						=		window_size

    return config



def 					_A			(__a							) ->       Any:
    """simple docstring"""






    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		name.replace('''patch_embed.proj'''    ,				'''embeddings.patch_embeddings.projection'''							)
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		name.replace('''patch_embed.norm'''    ,				'''embeddings.norm'''							)
    if "layers" in name:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''encoder.''' + name
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		name.replace('''attn.proj'''    ,				'''attention.output.dense'''							)
    if "attn" in name:
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		name.replace('''attn'''    ,				'''attention.self'''							)
    if "norm1" in name:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		name.replace('''norm1'''    ,				'''layernorm_before'''							)
    if "norm2" in name:
        SCREAMING_SNAKE_CASE_      :  str    						=		name.replace('''norm2'''    ,				'''layernorm_after'''							)
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE_      :  Any    						=		name.replace('''mlp.fc1'''    ,				'''intermediate.dense'''							)
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		name.replace('''mlp.fc2'''    ,				'''output.dense'''							)
    if "q_bias" in name:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		name.replace('''q_bias'''    ,				'''query.bias'''							)
    if "k_bias" in name:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		name.replace('''k_bias'''    ,				'''key.bias'''							)
    if "v_bias" in name:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		name.replace('''v_bias'''    ,				'''value.bias'''							)
    if "cpb_mlp" in name:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		name.replace('''cpb_mlp'''    ,				'''continuous_position_bias_mlp'''							)
    if name == "norm.weight":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''layernorm.weight'''
    if name == "norm.bias":
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''layernorm.bias'''

    if "head" in name:
        SCREAMING_SNAKE_CASE_      :  Any    						=		name.replace('''head'''    ,				'''classifier'''							)
    else:
        SCREAMING_SNAKE_CASE_      :  str    						=		'''swinv2.''' + name

    return name



def 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		orig_state_dict.pop(__a							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		key.split('''.'''							)
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		int(key_split[1]							)
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		int(key_split[3]							)
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		val[:dim, :]
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE_      :  Dict    						=		val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE_      :  Any    						=		val[:dim]
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		val[
                    dim : dim * 2
                ]
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		val[-dim:]
        else:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		val

    return orig_state_dict



def 					_A			(__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		timm.create_model(__a    ,				pretrained=__a							)
    timm_model.eval()

    SCREAMING_SNAKE_CASE_      :  int    						=		get_swinva_config(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		SwinvaForImageClassification(__a							)
    model.eval()

    SCREAMING_SNAKE_CASE_      :  Any    						=		convert_state_dict(timm_model.state_dict()    ,				__a							)
    model.load_state_dict(__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''    ,				'''-'''							)							)							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		Image.open(requests.get(__a    ,				stream=__a							).raw							)
    SCREAMING_SNAKE_CASE_      :  int    						=		image_processor(images=__a    ,				return_tensors='''pt'''							)

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		timm_model(inputs['''pixel_values''']							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(**__a							).logits

    assert torch.allclose(__a    ,				__a    ,				atol=1e-3							)

    print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'							)
    model.save_pretrained(__a							)

    print(f'Saving image processor to {pytorch_dump_folder_path}'							)
    image_processor.save_pretrained(__a							)

    model.push_to_hub(
        repo_path_or_name=Path(__a    ,				__a							)    ,				organization='''nandwalritik'''    ,				commit_message='''Add model'''    ,				)


if __name__ == "__main__":
  UpperCAmelCase_   : int   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swinv2_name""",
      default="""swinv2_tiny_patch4_window8_256""",
      type=str,
      help="""Name of the Swinv2 timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  UpperCAmelCase_   : int   			= parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










"""simple docstring"""






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCAmelCase_   : Optional[int]   			= """src/transformers"""
UpperCAmelCase_   : Tuple   			= """docs/source/en"""
UpperCAmelCase_   : Optional[Any]   			= """."""



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    with open(__a    ,				'''r'''    ,				encoding='''utf-8'''    ,				newline='''\n'''							) as f:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		f.readlines()
    # Find the start prompt.
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    while not lines[start_index].startswith(__a							):
        start_index += 1
    start_index += 1

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		start_index
    while not lines[end_index].startswith(__a							):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]							) <= 1:
        start_index += 1
    while len(lines[end_index]							) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCAmelCase_   : Optional[Any]   			= """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase_   : int   			= re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCAmelCase_   : Dict   			= re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCAmelCase_   : int   			= re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_   : Optional[int]   			= direct_transformers_import(TRANSFORMERS_PATH)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,				__a							)
    return [m.group(0							) for m in matches]



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2 if text == '''✅''' or text == '''❌''' else len(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		(width - text_length) // 2
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 					_A			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{name: config.replace('''Config'''    ,				''''''							) for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(__a							):
        SCREAMING_SNAKE_CASE_      :  Any    						=		None
        if attr_name.endswith('''Tokenizer'''							):
            SCREAMING_SNAKE_CASE_      :  Dict    						=		slow_tokenizers
            SCREAMING_SNAKE_CASE_      :  Dict    						=		attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fast_tokenizers
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attr_name[:-13]
        elif _re_tf_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  int    						=		tf_models
            SCREAMING_SNAKE_CASE_      :  Dict    						=		_re_tf_models.match(__a							).groups()[0]
        elif _re_flax_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_models
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		_re_flax_models.match(__a							).groups()[0]
        elif _re_pt_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  str    						=		pt_models
            SCREAMING_SNAKE_CASE_      :  int    						=		_re_pt_models.match(__a							).groups()[0]

        if lookup_dict is not None:
            while len(__a							) > 0:
                if attr_name in model_name_to_prefix.values():
                    SCREAMING_SNAKE_CASE_      :  List[str]    						=		True
                    break
                # Try again after removing the last word in the name
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(camel_case_split(__a							)[:-1]							)

    # Let's build that table!
    SCREAMING_SNAKE_CASE_      :  Any    						=		list(model_name_to_config.keys()							)
    model_names.sort(key=str.lower							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[len(__a							) + 2 for c in columns]
    SCREAMING_SNAKE_CASE_      :  str    						=		max([len(__a							) for name in model_names]							) + 2

    # Build the table per se
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''|''' + '''|'''.join([_center_text(__a    ,				__a							) for c, w in zip(__a    ,				__a							)]							) + '''|\n'''
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]							) + "|\n"

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{True: '''✅''', False: '''❌'''}
    for name in model_names:
        SCREAMING_SNAKE_CASE_      :  str    						=		model_name_to_prefix[name]
        SCREAMING_SNAKE_CASE_      :  int    						=		[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(__a    ,				__a							) for l, w in zip(__a    ,				__a							)]							) + "|\n"
    return table



def 					_A			(__a=False							) ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		_find_text_in_file(
        filename=os.path.join(__a    ,				'''index.md'''							)    ,				start_prompt='''<!--This table is updated automatically from the auto modules'''    ,				end_prompt='''<!-- End table-->'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(__a    ,				'''index.md'''							)    ,				'''w'''    ,				encoding='''utf-8'''    ,				newline='''\n'''							) as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
        else:
            raise ValueError(
                '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''							)


if __name__ == "__main__":
  UpperCAmelCase_   : Union[str, Any]   			= argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCAmelCase_   : Any   			= parser.parse_args()

  check_model_table(args.fix_and_overwrite)


