





import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_lowerCamelCase   				=			'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_lowerCamelCase   				=			'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

_lowerCamelCase   				=			'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,  _KWARGS_DESCRIPTION    )
class   UpperCamelCase_       (     datasets.Metric    ):


							def      _snake_case  (							self    :List[Any]		)   ->   List[Any]:



									"""simple docstring"""



									if version.parse(scb.__version__		) < version.parse("""1.4.12"""		):
											raise ImportWarning(
											    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"""
											    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""		)
									return datasets.MetricInfo(
									    description=_DESCRIPTION							,				citation=_CITATION							,				homepage="""http://www.cs.umd.edu/~snover/tercom/"""							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""string"""							,				id="""sequence"""		),
									            """references""": datasets.Sequence(datasets.Value("""string"""							,				id="""sequence"""		)							,				id="""references"""		),
									        }		)							,				codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""]							,				reference_urls=[
									        """https://github.com/jhclark/tercom""",
									    ]							,				)


							def      _snake_case  (							self    :Optional[int]							,				__A    :Optional[int]							,				__A    :Tuple							,				__A    :Optional[Any] = False							,				__A    :Tuple = False							,				__A    :Optional[Any] = False							,				__A    :Dict = False							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      len(references[0]		)
									if any(len(_UpperCAmelCase		) != references_per_prediction for refs in references		):
											raise ValueError("""Sacrebleu requires the same number of references for each prediction"""		)
									SCREAMING_SNAKE_CASE__			=      [[refs[i] for refs in references] for i in range(_UpperCAmelCase		)]

									SCREAMING_SNAKE_CASE__			=      TER(
									    normalized=_UpperCAmelCase							,				no_punct=_UpperCAmelCase							,				asian_support=_UpperCAmelCase							,				case_sensitive=_UpperCAmelCase							,				)
									SCREAMING_SNAKE_CASE__			=      sb_ter.corpus_score(_UpperCAmelCase							,				_UpperCAmelCase		)

									return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






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()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      original_name.split("""."""    )[0]
		SCREAMING_SNAKE_CASE__			=      key.split("""."""    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 2]    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 1]    )
		SCREAMING_SNAKE_CASE__			=      orig_block_num - offset

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      OrderedDict()
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      0, 0
		for key, value in state_dict.items():
				if key.startswith("""network"""    ):
						SCREAMING_SNAKE_CASE__			=      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
						SCREAMING_SNAKE_CASE__			=      key[: key.find("""proj"""    )]
						SCREAMING_SNAKE_CASE__			=      key.replace(UpperCamelCase__					,   f'''patch_embeddings.{total_embed_found}.'''    )
						SCREAMING_SNAKE_CASE__			=      key.replace("""proj"""					,   """projection"""    )
						if key.endswith("""bias"""    ):
								total_embed_found += 1
				if "patch_embeddings" in key:
						SCREAMING_SNAKE_CASE__			=      """poolformer.encoder.""" + key
				if "mlp.fc1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc1"""					,   """output.conv1"""    )
				if "mlp.fc2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc2"""					,   """output.conv2"""    )
				if "norm1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm1"""					,   """before_norm"""    )
				if "norm2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm2"""					,   """after_norm"""    )
				if "layer_scale_1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_1"""					,   """layer_scale_1"""    )
				if "layer_scale_2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_2"""					,   """layer_scale_2"""    )
				if "head" in key:
						SCREAMING_SNAKE_CASE__			=      key.replace("""head"""					,   """classifier"""    )
				SCREAMING_SNAKE_CASE__			=      value
		return new_state_dict






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )

		return image






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      PoolFormerConfig()

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE__			=      """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__			=      model_name[-3:]
		SCREAMING_SNAKE_CASE__			=      1_000
		SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__			=      (1, 1_000)

		# set config attributes
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(UpperCamelCase__					,   UpperCamelCase__					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(UpperCamelCase__    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__			=      idalabel
		SCREAMING_SNAKE_CASE__			=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      [2, 2, 6, 2]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      [4, 4, 12, 4]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      [8, 8, 24, 8]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		else:
				raise ValueError(f'''Size {size} not supported'''    )

		# load image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )

		# Prepare image
		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      image_processor(images=UpperCamelCase__					,   return_tensors="""pt"""    ).pixel_values

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

		# load original state dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location=torch.device("""cpu"""    )    )

		# rename keys
		SCREAMING_SNAKE_CASE__			=      rename_keys(UpperCamelCase__    )

		# create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE__			=      PoolFormerForImageClassification(UpperCamelCase__    )
		model.load_state_dict(UpperCamelCase__    )
		model.eval()

		# Define image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      image_processor(images=prepare_img()					,   return_tensors="""pt"""    ).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE__			=      model(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]    )
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]    )
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]    )
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]    )
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]    )
		else:
				raise ValueError(f'''Size {size} not supported'''    )

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

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


if __name__ == "__main__":
	_lowerCamelCase   				=			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.'
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)