
'''simple docstring'''

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase__ :			Optional[int]											=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase__ :			List[str]											=		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class  A  (						unittest.TestCase     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				tempfile.mkdtemp()
				os.makedirs(os.path.join(self.transformer_dir   ,				"models/bert/"				)				)
				lowerCAmelCase__										=				self.transformer_dir
				shutil.copy(
				    os.path.join(__magic_name__   ,				"src/transformers/models/bert/modeling_bert.py"				)   ,				os.path.join(self.transformer_dir   ,				"models/bert/modeling_bert.py"				)   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"src/transformers"
				shutil.rmtree(self.transformer_dir				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							str   ,				__magic_name__      :							int   ,				__magic_name__      :							Tuple=None				):


				"""simple docstring"""

				lowerCAmelCase__										=				comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
				if overwrite_result is not None:
							lowerCAmelCase__										=				comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
				lowerCAmelCase__										=				black.Mode(target_versions={black.TargetVersion.PYaa}   ,				line_length=119				)
				lowerCAmelCase__										=				black.format_str(__magic_name__   ,				mode=__magic_name__				)
				lowerCAmelCase__										=				os.path.join(self.transformer_dir   ,				"new_code.py"				)
				with open(__magic_name__   ,				"w"   ,				newline="\n"				) as f:
							f.write(__magic_name__				)
				if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(__magic_name__				)				) == 0				)
				else:
							check_copies.is_copy_consistent(f.name   ,				overwrite=__magic_name__				)
							with open(__magic_name__   ,				"r"				) as f:
										self.assertTrue(f.read()   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"				)
				self.assertEqual(__magic_name__   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"   ,				"BertLMPredictionHead"   ,				REFERENCE_CODE + "\n"   ,				)

				# With no empty line at the end
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"   ,				"BertLMPredictionHead"   ,				__magic_name__   ,				)

				# Copy consistency with rename
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"   ,				"TestModelLMPredictionHead"   ,				re.sub("Bert"   ,				"TestModel"   ,				__magic_name__				)   ,				)

				# Copy consistency with a really long name
				lowerCAmelCase__										=				"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
				self.check_copy_consistency(
				    f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""   ,				f"""{long_class_name}LMPredictionHead"""   ,				re.sub("Bert"   ,				__magic_name__   ,				__magic_name__				)   ,				)

				# Copy consistency with overwrite
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"   ,				"TestModelLMPredictionHead"   ,				__magic_name__   ,				overwrite_result=re.sub("Bert"   ,				"TestModel"   ,				__magic_name__				)   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
				    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
				    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
				    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
				    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
				    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
				    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
				    " method has been applied to compress GPT2 into"
				    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
				    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
				    " Multilingual BERT into"
				    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
				    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
				    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
				    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
				    " Luong, Quoc V. Le, Christopher D. Manning."
				)
				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)
				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
				    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
				    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
				    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
				    " method has been applied to compress GPT2 into"
				    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
				    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
				    " Multilingual BERT into"
				    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
				    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
				    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
				    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
				    " Christopher D. Manning 发布。\n"
				)

				lowerCAmelCase__						,lowerCAmelCase__										=				check_copies.convert_to_localized_md(
				    __magic_name__   ,				__magic_name__   ,				localized_readme["format_model_list"]				)

				self.assertFalse(__magic_name__				)
				self.assertEqual(__magic_name__   ,				__magic_name__				)

				lowerCAmelCase__						,lowerCAmelCase__										=				check_copies.convert_to_localized_md(
				    __magic_name__   ,				__magic_name__   ,				localized_readme["format_model_list"]				)

				# Check whether the number of models is equal to README.md after conversion.
				self.assertTrue(__magic_name__				)

				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
				    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
				    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
				    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
				)
				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
				    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)
				lowerCAmelCase__										=				(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)

				lowerCAmelCase__						,lowerCAmelCase__										=				check_copies.convert_to_localized_md(
				    __magic_name__   ,				__magic_name__   ,				localized_readme["format_model_list"]				)

				# Check if the model link is synchronized.
				self.assertEqual(__magic_name__   ,				__magic_name__				)







"""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)






