
"""simple docstring"""





import argparse
import os
import re

import packaging.version


_a								=						"""examples/"""
_a								=						{
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
_a								=						{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
_a								=						"""README.md"""



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					with open(__snake_case,	'''r''',	encoding='''utf-8''',	newline='''\n'''      ) as f:
										_UpperCamelCase									=    f.read()
					_UpperCamelCase					,    _UpperCamelCase									=    REPLACE_PATTERNS[pattern]
					_UpperCamelCase									=    replace.replace('''VERSION''',	__snake_case      )
					_UpperCamelCase									=    re_pattern.sub(__snake_case,	__snake_case      )
					with open(__snake_case,	'''w''',	encoding='''utf-8''',	newline='''\n'''      ) as f:
										f.write(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						List[str]:


					"""simple docstring"""





					for folder, directories, fnames in os.walk(__snake_case      ):
										# Removing some of the folders with non-actively maintained examples from the walk
										if "research_projects" in directories:
															directories.remove('''research_projects'''      )
										if "legacy" in directories:
															directories.remove('''legacy'''      )
										for fname in fnames:
															if fname.endswith('''.py'''      ):
																				update_version_in_file(os.path.join(__snake_case,	__snake_case      ),	__snake_case,	pattern='''examples'''      )



def  lowerCamelCase__						(     __snake_case,	__snake_case=False      )				->						str:


					"""simple docstring"""





					for pattern, fname in REPLACE_FILES.items():
										update_version_in_file(__snake_case,	__snake_case,	__snake_case      )
					if not patch:
										update_version_in_examples(__snake_case      )



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


					"""simple docstring"""





					_UpperCamelCase									=    '''🤗 Transformers currently provides the following architectures'''
					_UpperCamelCase									=    '''1. Want to contribute a new model?'''
					with open(__snake_case,	'''r''',	encoding='''utf-8''',	newline='''\n'''      ) as f:
										_UpperCamelCase									=    f.readlines()

					# Find the start of the list.
					_UpperCamelCase									=    0
					while not lines[start_index].startswith(_start_prompt      ):
										start_index += 1
					start_index += 1

					_UpperCamelCase									=    start_index
					# Update the lines in the model list.
					while not lines[index].startswith(_end_prompt      ):
										if lines[index].startswith('''1.'''      ):
															_UpperCamelCase									=    lines[index].replace(
															    '''https://huggingface.co/docs/transformers/main/model_doc''',	'''https://huggingface.co/docs/transformers/model_doc''',	)
										index += 1

					with open(__snake_case,	'''w''',	encoding='''utf-8''',	newline='''\n'''      ) as f:
										f.writelines(__snake_case      )



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					with open(REPLACE_FILES['''init'''],	'''r'''      ) as f:
										_UpperCamelCase									=    f.read()
					_UpperCamelCase									=    REPLACE_PATTERNS['''init'''][0].search(__snake_case      ).groups()[0]
					return packaging.version.parse(__snake_case      )



def  lowerCamelCase__						(     __snake_case=False      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    get_version()
					if patch and default_version.is_devrelease:
										raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''      )
					if default_version.is_devrelease:
										_UpperCamelCase									=    default_version.base_version
					elif patch:
										_UpperCamelCase									=    F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
					else:
										_UpperCamelCase									=    F'''{default_version.major}.{default_version.minor + 1}.0'''

					# Now let's ask nicely if that's the right one.
					_UpperCamelCase									=    input(F'''Which version are you releasing? [{default_version}]'''      )
					if len(__snake_case      ) == 0:
										_UpperCamelCase									=    default_version

					print(F'''Updating version to {version}.'''      )
					global_version_update(__snake_case,	patch=__snake_case      )
					if not patch:
										print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
										clean_main_ref_in_model_list()



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    get_version()
					_UpperCamelCase									=    F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
					_UpperCamelCase									=    current_version.base_version

					# Check with the user we got that right.
					_UpperCamelCase									=    input(F'''Which version are we developing now? [{dev_version}]'''      )
					if len(__snake_case      ) == 0:
										_UpperCamelCase									=    dev_version

					print(F'''Updating version to {version}.'''      )
					global_version_update(__snake_case      )
					print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
					clean_main_ref_in_model_list()


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
				parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
				_a								=						parser.parse_args()
				if not args.post_release:
								pre_release_work(patch=args.patch)
				elif args.patch:
								print("""Nothing to do after a patch :-)""")
				else:
								post_release_work()








"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    original_name.split('''.'''      )[0]
					_UpperCamelCase									=    key.split('''.'''      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 2]      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 1]      )
					_UpperCamelCase									=    orig_block_num - offset

					_UpperCamelCase									=    key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''',	F'''block.{new_block_num}.{layer_num}.{new_name}'''      )
					return key



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    OrderedDict()
					_UpperCamelCase					,    _UpperCamelCase									=    0, 0
					for key, value in state_dict.items():
										if key.startswith('''network'''      ):
															_UpperCamelCase									=    key.replace('''network''',	'''poolformer.encoder'''      )
										if "proj" in key:
															# Works for the first embedding as well as the internal embedding layers
															if key.endswith('''bias'''      ) and "patch_embed" not in key:
																				patch_emb_offset += 1
															_UpperCamelCase									=    key[: key.find('''proj'''      )]
															_UpperCamelCase									=    key.replace(__snake_case,	F'''patch_embeddings.{total_embed_found}.'''      )
															_UpperCamelCase									=    key.replace('''proj''',	'''projection'''      )
															if key.endswith('''bias'''      ):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															_UpperCamelCase									=    '''poolformer.encoder.''' + key
										if "mlp.fc1" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''mlp.fc1''',	'''output.conv1'''      )
										if "mlp.fc2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''mlp.fc2''',	'''output.conv2'''      )
										if "norm1" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''norm1''',	'''before_norm'''      )
										if "norm2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''norm2''',	'''after_norm'''      )
										if "layer_scale_1" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''layer_scale_1''',	'''layer_scale_1'''      )
										if "layer_scale_2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''layer_scale_2''',	'''layer_scale_2'''      )
										if "head" in key:
															_UpperCamelCase									=    key.replace('''head''',	'''classifier'''      )
										_UpperCamelCase									=    value
					return new_state_dict



def  lowerCamelCase__						(     )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase									=    Image.open(requests.get(__snake_case,	stream=__snake_case      ).raw      )

					return image



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    PoolFormerConfig()

					# set attributes based on model_name
					_UpperCamelCase									=    '''huggingface/label-files'''
					_UpperCamelCase									=    model_name[-3:]
					_UpperCamelCase									=    10_00
					_UpperCamelCase									=    '''imagenet-1k-id2label.json'''
					_UpperCamelCase									=    (1, 10_00)

					# set config attributes
					_UpperCamelCase									=    json.load(open(hf_hub_download(__snake_case,	__snake_case,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(__snake_case      ): v for k, v in idalabel.items()}
					_UpperCamelCase									=    idalabel
					_UpperCamelCase									=    {v: k for k, v in idalabel.items()}
					if size == "s12":
										_UpperCamelCase									=    [2, 2, 6, 2]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s24":
										_UpperCamelCase									=    [4, 4, 12, 4]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.9
					elif size == "m36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					elif size == "m48":
										_UpperCamelCase									=    [8, 8, 24, 8]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					else:
										raise ValueError(F'''Size {size} not supported'''      )

					# load image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )

					# Prepare image
					_UpperCamelCase									=    prepare_img()
					_UpperCamelCase									=    image_processor(images=__snake_case,	return_tensors='''pt'''      ).pixel_values

					logger.info(F'''Converting model {model_name}...'''      )

					# load original state dict
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=torch.device('''cpu'''      )      )

					# rename keys
					_UpperCamelCase									=    rename_keys(__snake_case      )

					# create HuggingFace model and load state dict
					_UpperCamelCase									=    PoolFormerForImageClassification(__snake_case      )
					model.load_state_dict(__snake_case      )
					model.eval()

					# Define image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )
					_UpperCamelCase									=    image_processor(images=prepare_img(),	return_tensors='''pt'''      ).pixel_values

					# forward pass
					_UpperCamelCase									=    model(__snake_case      )
					_UpperCamelCase									=    outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										_UpperCamelCase									=    torch.tensor([-0.3045, -0.6758, -0.4869]      )
					elif size == "s24":
										_UpperCamelCase									=    torch.tensor([0.4402, -0.1374, -0.8045]      )
					elif size == "s36":
										_UpperCamelCase									=    torch.tensor([-0.6080, -0.5133, -0.5898]      )
					elif size == "m36":
										_UpperCamelCase									=    torch.tensor([0.3952, 0.2263, -1.2668]      )
					elif size == "m48":
										_UpperCamelCase									=    torch.tensor([0.1167, -0.0656, -0.3423]      )
					else:
										raise ValueError(F'''Size {size} not supported'''      )

					# verify logits
					assert logits.shape == expected_shape
					assert torch.allclose(logits[0, :3],	__snake_case,	atol=1e-2      )

					# finally, save model and image processor
					logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
					Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
					model.save_pretrained(__snake_case      )
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
					image_processor.save_pretrained(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()

				parser.add_argument(
				    """--model_name""",
				    default="""poolformer_s12""",
				    type=str,
				    help="""Name of the model you'd like to convert.""",
				)
				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
				)
				_a								=						parser.parse_args()
				convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






