


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
       a_      :	List[str]							=						True if 'large' in model_name or 'huge' in model_name else False
       a_      :	str							=						True if 'large' in model_name or 'huge' in model_name else False
       a_      :	Tuple							=						True if 'large' in model_name or 'huge' in model_name else False

       if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
              if "fl3" in model_name:
                     a_      :	List[str]							=						[3, 3, 3, 3]
                     a_      :	Tuple							=						[5, 5, 5, 5]
              elif "fl4" in model_name:
                     a_      :	List[str]							=						[4, 4, 4, 4]
                     a_      :	List[Any]							=						[3, 3, 3, 3]

       if "tiny" in model_name or "small" in model_name or "base" in model_name:
              a_      :	Dict							=						[3, 3, 3, 3]
              if "lrf" in model_name:
                     a_      :	Union[str, Any]							=						[3, 3, 3, 3]
              else:
                     a_      :	Any							=						[2, 2, 2, 2]

       if "tiny" in model_name:
              a_      :	Tuple							=						96
       elif "small" in model_name:
              a_      :	Optional[int]							=						96
       elif "base" in model_name:
              a_      :	str							=						1_28
       elif "large" in model_name:
              a_      :	str							=						1_92
       elif "xlarge" in model_name:
              a_      :	int							=						2_56
       elif "huge" in model_name:
              a_      :	Optional[int]							=						3_52

       # set label information
       a_      :	str							=						'huggingface/label-files'
       if "large" in model_name or "huge" in model_name:
              a_      :	Tuple							=						'imagenet-22k-id2label.json'
       else:
              a_      :	int							=						'imagenet-1k-id2label.json'

       a_      :	Optional[int]							=						json.load(open(hf_hub_download(__A						,					__A						,					repo_type='dataset'       )						,					'r'       )       )
       a_      :	Optional[int]							=						{int(__A       ): v for k, v in idalabel.items()}
       a_      :	Union[str, Any]							=						{v: k for k, v in idalabel.items()}

       a_      :	Optional[Any]							=						FocalNetConfig(
           embed_dim=__A						,					depths=__A						,					focal_levels=__A						,					focal_windows=__A						,					use_conv_embed=__A						,					idalabel=__A						,					labelaid=__A						,					use_post_layernorm=__A						,					use_layerscale=__A						,					)

       return config







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]       )			->	Optional[int]:

       """simple docstring"""

       if "patch_embed.proj" in name:
              a_      :	Dict							=						name.replace('patch_embed.proj'						,					'embeddings.patch_embeddings.projection'       )
       if "patch_embed.norm" in name:
              a_      :	Any							=						name.replace('patch_embed.norm'						,					'embeddings.norm'       )
       if "layers" in name:
              a_      :	List[Any]							=						'encoder.' + name
       if "encoder.layers" in name:
              a_      :	str							=						name.replace('encoder.layers'						,					'encoder.stages'       )
       if "downsample.proj" in name:
              a_      :	Optional[Any]							=						name.replace('downsample.proj'						,					'downsample.projection'       )
       if "blocks" in name:
              a_      :	Any							=						name.replace('blocks'						,					'layers'       )
       if "modulation.f.weight" in name or "modulation.f.bias" in name:
              a_      :	Optional[int]							=						name.replace('modulation.f'						,					'modulation.projection_in'       )
       if "modulation.h.weight" in name or "modulation.h.bias" in name:
              a_      :	Optional[int]							=						name.replace('modulation.h'						,					'modulation.projection_context'       )
       if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
              a_      :	Optional[int]							=						name.replace('modulation.proj'						,					'modulation.projection_out'       )

       if name == "norm.weight":
              a_      :	Dict							=						'layernorm.weight'
       if name == "norm.bias":
              a_      :	Optional[Any]							=						'layernorm.bias'

       if "head" in name:
              a_      :	Dict							=						name.replace('head'						,					'classifier'       )
       else:
              a_      :	int							=						'focalnet.' + name

       return name







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Union[str, Any]						,					__A				:    int=False       )			->	List[Any]:

       """simple docstring"""

       a_      :	Tuple							=						{
           'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
           'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
           'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
           'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
           'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
           'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
           'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
           'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
           'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
           'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
       }
       # fmt: on

       a_      :	str							=						model_name_to_url[model_name]
       print('Checkpoint URL: '						,					__A       )
       a_      :	List[Any]							=						torch.hub.load_state_dict_from_url(__A						,					map_location='cpu'       )['model']

       # rename keys
       for key in state_dict.copy().keys():
              a_      :	List[str]							=						state_dict.pop(__A       )
              a_      :	Optional[Any]							=						val

       a_      :	Optional[int]							=						get_focalnet_config(__A       )
       a_      :	Any							=						FocalNetForImageClassification(__A       )
       model.eval()

       # load state dict
       model.load_state_dict(__A       )

       # verify conversion
       a_      :	List[Any]							=						'http://images.cocodataset.org/val2017/000000039769.jpg'

       a_      :	Optional[int]							=						BitImageProcessor(
           do_resize=__A						,					size={'shortest_edge': 2_56}						,					resample=PILImageResampling.BILINEAR						,					do_center_crop=__A						,					crop_size=2_24						,					do_normalize=__A						,					image_mean=__A						,					image_std=__A						,					)
       a_      :	Tuple							=						Image.open(requests.get(__A						,					stream=__A       ).raw       )
       a_      :	Optional[int]							=						processor(images=__A						,					return_tensors='pt'       )

       a_      :	int							=						transforms.Compose(
           [
               transforms.Resize(2_56       ),
               transforms.CenterCrop(2_24       ),
               transforms.ToTensor(),
               transforms.Normalize(mean=[0.485, 0.456, 0.406]						,					std=[0.229, 0.224, 0.225]       ),
           ]       )

       a_      :	str							=						image_transforms(__A       ).unsqueeze(0       )

       # verify pixel_values
       assert torch.allclose(inputs.pixel_values						,					__A						,					atol=1e-4       )

       a_      :	List[Any]							=						model(**__A       )

       a_      :	List[Any]							=						outputs.logits.argmax(-1       ).item()
       print('Predicted class:'						,					model.config.idalabel[predicted_class_idx]       )

       print('First values of logits:'						,					outputs.logits[0, :3]       )

       if model_name == "focalnet-tiny":
              a_      :	Optional[int]							=						torch.tensor([0.2166, -0.4368, 0.2191]       )
       elif model_name == "focalnet-tiny-lrf":
              a_      :	int							=						torch.tensor([1.1669, 0.0125, -0.1695]       )
       elif model_name == "focalnet-small":
              a_      :	Any							=						torch.tensor([0.4917, -0.0430, 0.1341]       )
       elif model_name == "focalnet-small-lrf":
              a_      :	Any							=						torch.tensor([-0.2588, -0.5342, -0.2331]       )
       elif model_name == "focalnet-base":
              a_      :	Union[str, Any]							=						torch.tensor([-0.1655, -0.4090, -0.1730]       )
       elif model_name == "focalnet-base-lrf":
              a_      :	int							=						torch.tensor([0.5306, -0.0483, -0.3928]       )
       assert torch.allclose(outputs.logits[0, :3]						,					__A						,					atol=1e-4       )
       print('Looks ok!'       )

       if pytorch_dump_folder_path is not None:
              print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""       )
              model.save_pretrained(__A       )
              processor.save_pretrained(__A       )

       if push_to_hub:
              print(F"""Pushing model and processor of {model_name} to the hub..."""       )
              model.push_to_hub(F"""{model_name}"""       )
              processor.push_to_hub(F"""{model_name}"""       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Tuple						      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='focalnet-tiny',
       type=str,
       help='Name of the FocalNet 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.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether to push the model and processor to the hub.',
   )

   UpperCAmelCase_   :				Optional[int]						      = parser.parse_args()
   convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import argparse
import os
import re


UpperCAmelCase_   :				List[Any]						      = 'src/transformers'

# Pattern that looks at the indentation in a line.
UpperCAmelCase_   :				Any						      = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_   :				str						      = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_   :				Dict						      = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_   :				int						      = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_   :				List[str]						      = re.compile(R'\[([^\]]+)\]')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						_re_indent.search(__A       )
       return "" if search is None else search.groups()[0]







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]=""						,					__A				:    Dict=None						,					__A				:    Dict=None       )			->	int:

       """simple docstring"""

       a_      :	Tuple							=						0
       a_      :	Dict							=						code.split('\n'       )
       if start_prompt is not None:
              while not lines[index].startswith(__A       ):
                     index += 1
              a_      :	List[Any]							=						['\n'.join(lines[:index]       )]
       else:
              a_      :	List[str]							=						[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_      :	Tuple							=						[lines[index]]
       index += 1
       while index < len(__A       ) and (end_prompt is None or not lines[index].startswith(__A       )):
              if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
                     if len(__A       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ' '       ):
                            current_block.append(lines[index]       )
                            blocks.append('\n'.join(__A       )       )
                            if index < len(__A       ) - 1:
                                   a_      :	Dict							=						[lines[index + 1]]
                                   index += 1
                            else:
                                   a_      :	Dict							=						[]
                     else:
                            blocks.append('\n'.join(__A       )       )
                            a_      :	int							=						[lines[index]]
              else:
                     current_block.append(lines[index]       )
              index += 1

       # Adds current block if it's nonempty.
       if len(__A       ) > 0:
              blocks.append('\n'.join(__A       )       )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(__A       ):
              blocks.append('\n'.join(lines[index:]       )       )

       return blocks







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	Any:

       """simple docstring"""

       def _inner(__A				:    Tuple       ):
              return key(__A       ).lower().replace('_'						,					''       )

       return _inner







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    List[Any]=None       )			->	List[str]:

       """simple docstring"""

       def noop(__A				:    Tuple       ):
              return x

       if key is None:
              a_      :	Optional[Any]							=						noop
       # Constants are all uppercase, they go first.
       a_      :	List[Any]							=						[obj for obj in objects if key(__A       ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_      :	Dict							=						[obj for obj in objects if key(__A       )[0].isupper() and not key(__A       ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_      :	Optional[int]							=						[obj for obj in objects if not key(__A       )[0].isupper()]

       a_      :	Optional[Any]							=						ignore_underscore(__A       )
       return sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Optional[int]:

       """simple docstring"""

       def _replace(__A				:    List[Any]       ):
              a_      :	Tuple							=						match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_      :	int							=						[part.strip().replace('"'						,					''       ) for part in imports.split(','       )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]       ) == 0:
                     a_      :	str							=						keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__A       )]       ) + "]"

       a_      :	Optional[int]							=						import_statement.split('\n'       )
       if len(__A       ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_      :	int							=						2 if lines[1].strip() == '[' else 1
              a_      :	int							=						[(i, _re_strip_line.search(__A       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
              a_      :	str							=						sort_objects(__A						,					key=lambda __A       : x[1]       )
              a_      :	int							=						[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
       elif len(__A       ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]       ) is not None:
                     a_      :	str							=						_re_bracket_content.sub(_replace						,					lines[1]       )
              else:
                     a_      :	List[str]							=						[part.strip().replace('"'						,					''       ) for part in lines[1].split(','       )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]       ) == 0:
                            a_      :	Optional[int]							=						keys[:-1]
                     a_      :	Any							=						get_indent(lines[1]       ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__A       )]       )
              return "\n".join(__A       )
       else:
              # Finally we have to deal with imports fitting on one line
              a_      :	Union[str, Any]							=						_re_bracket_content.sub(_replace						,					__A       )
              return import_statement







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any=True       )			->	Union[str, Any]:

       """simple docstring"""

       with open(__A						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_      :	Optional[Any]							=						split_code_in_indented_blocks(
           __A						,					start_prompt='_import_structure = {'						,					end_prompt='if TYPE_CHECKING:'       )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1						,					len(__A       ) - 1       ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_      :	Any							=						main_blocks[block_idx]
              a_      :	Any							=						block.split('\n'       )

              # Get to the start of the imports.
              a_      :	Any							=						0
              while line_idx < len(__A       ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_      :	Tuple							=						len(__A       )
                     else:
                            line_idx += 1
              if line_idx >= len(__A       ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_      :	List[str]							=						'\n'.join(block_lines[line_idx:-1]       )
              a_      :	List[Any]							=						get_indent(block_lines[1]       )
              # Slit the internal block into blocks of indent level 1.
              a_      :	Optional[int]							=						split_code_in_indented_blocks(__A						,					indent_level=__A       )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_      :	Tuple							=						_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_      :	Union[str, Any]							=						[(pattern.search(__A       ).groups()[0] if pattern.search(__A       ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_      :	str							=						[(i, key) for i, key in enumerate(__A       ) if key is not None]
              a_      :	Optional[int]							=						[x[0] for x in sorted(__A						,					key=lambda __A       : x[1]       )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_      :	int							=						0
              a_      :	int							=						[]
              for i in range(len(__A       )       ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]       )
                     else:
                            a_      :	List[str]							=						sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
                            reorderded_blocks.append(__A       )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_      :	Any							=						'\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

       if code != "\n".join(__A       ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""       )
                     with open(__A						,					'w'						,					encoding='utf-8'       ) as f:
                            f.write('\n'.join(__A       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for root, _, files in os.walk(__A       ):
              if "__init__.py" in files:
                     a_      :	Dict							=						sort_imports(os.path.join(__A						,					'__init__.py'       )						,					check_only=__A       )
                     if result:
                            a_      :	Tuple							=						[os.path.join(__A						,					'__init__.py'       )]
       if len(__A       ) > 0:
              raise ValueError(F"""Would overwrite {len(__A       )} files, run `make style`."""       )


if __name__ == "__main__":
   UpperCAmelCase_   :				int						      = argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   UpperCAmelCase_   :				Tuple						      = parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)


