


import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   SwinConfig()
			__lowercase 					=   swin_name.split('''_'''  )

			__lowercase 					=   name_split[1]
			__lowercase 					=   int(name_split[4]  )
			__lowercase 					=   int(name_split[3][-1]  )

			if model_size == "tiny":
						__lowercase 					=   96
						__lowercase 					=   (2, 2, 6, 2)
						__lowercase 					=   (3, 6, 12, 24)
			elif model_size == "small":
						__lowercase 					=   96
						__lowercase 					=   (2, 2, 18, 2)
						__lowercase 					=   (3, 6, 12, 24)
			elif model_size == "base":
						__lowercase 					=   1_28
						__lowercase 					=   (2, 2, 18, 2)
						__lowercase 					=   (4, 8, 16, 32)
			else:
						__lowercase 					=   1_92
						__lowercase 					=   (2, 2, 18, 2)
						__lowercase 					=   (6, 12, 24, 48)

			if "in22k" in swin_name:
						__lowercase 					=   2_18_41
			else:
						__lowercase 					=   10_00
						__lowercase 					=   '''huggingface/label-files'''
						__lowercase 					=   '''imagenet-1k-id2label.json'''
						__lowercase 					=   json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type='''dataset'''  )       ,			'''r'''  )  )
						__lowercase 					=   {int(_UpperCamelCase  ): v for k, v in idalabel.items()}
						__lowercase 					=   idalabel
						__lowercase 					=   {v: k for k, v in idalabel.items()}

			__lowercase 					=   img_size
			__lowercase 					=   num_classes
			__lowercase 					=   embed_dim
			__lowercase 					=   depths
			__lowercase 					=   num_heads
			__lowercase 					=   window_size

			return config


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			if "patch_embed.proj" in name:
						__lowercase 					=   name.replace('''patch_embed.proj'''       ,			'''embeddings.patch_embeddings.projection'''  )
			if "patch_embed.norm" in name:
						__lowercase 					=   name.replace('''patch_embed.norm'''       ,			'''embeddings.norm'''  )
			if "layers" in name:
						__lowercase 					=   '''encoder.''' + name
			if "attn.proj" in name:
						__lowercase 					=   name.replace('''attn.proj'''       ,			'''attention.output.dense'''  )
			if "attn" in name:
						__lowercase 					=   name.replace('''attn'''       ,			'''attention.self'''  )
			if "norm1" in name:
						__lowercase 					=   name.replace('''norm1'''       ,			'''layernorm_before'''  )
			if "norm2" in name:
						__lowercase 					=   name.replace('''norm2'''       ,			'''layernorm_after'''  )
			if "mlp.fc1" in name:
						__lowercase 					=   name.replace('''mlp.fc1'''       ,			'''intermediate.dense'''  )
			if "mlp.fc2" in name:
						__lowercase 					=   name.replace('''mlp.fc2'''       ,			'''output.dense'''  )

			if name == "norm.weight":
						__lowercase 					=   '''layernorm.weight'''
			if name == "norm.bias":
						__lowercase 					=   '''layernorm.bias'''

			if "head" in name:
						__lowercase 					=   name.replace('''head'''       ,			'''classifier'''  )
			else:
						__lowercase 					=   '''swin.''' + name

			return name


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			for key in orig_state_dict.copy().keys():
						__lowercase 					=   orig_state_dict.pop(_UpperCamelCase  )

						if "mask" in key:
									continue
						elif "qkv" in key:
									__lowercase 					=   key.split('''.'''  )
									__lowercase 					=   int(key_split[1]  )
									__lowercase 					=   int(key_split[3]  )
									__lowercase 					=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												__lowercase 					=   val[:dim, :]
												__lowercase 					=   val[
												    dim : dim * 2, :
												]
												__lowercase 					=   val[-dim:, :]
									else:
												__lowercase 					=   val[
												    :dim
												]
												__lowercase 					=   val[
												    dim : dim * 2
												]
												__lowercase 					=   val[
												    -dim:
												]
						else:
									__lowercase 					=   val

			return orig_state_dict


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   timm.create_model(_UpperCamelCase       ,			pretrained=_UpperCamelCase  )
			timm_model.eval()

			__lowercase 					=   get_swin_config(_UpperCamelCase  )
			__lowercase 					=   SwinForImageClassification(_UpperCamelCase  )
			model.eval()

			__lowercase 					=   convert_state_dict(timm_model.state_dict()       ,			_UpperCamelCase  )
			model.load_state_dict(_UpperCamelCase  )

			__lowercase 					=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''

			__lowercase 					=   AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''       ,			'''-'''  )  )  )
			__lowercase 					=   Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase  ).raw  )
			__lowercase 					=   image_processor(images=_UpperCamelCase       ,			return_tensors='''pt'''  )

			__lowercase 					=   timm_model(inputs['''pixel_values''']  )
			__lowercase 					=   model(**_UpperCamelCase  ).logits

			assert torch.allclose(_UpperCamelCase       ,			_UpperCamelCase       ,			atol=1E-3  )

			print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'  )
			model.save_pretrained(_UpperCamelCase  )

			print(F'Saving image processor to {pytorch_dump_folder_path}'  )
			image_processor.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Tuple		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--swin_name''',
	    default='''swin_tiny_patch4_window7_224''',
	    type=str,
	    help='''Name of the Swin 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.'''
	)

	a : Optional[int]		=				parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


a : Optional[int]		=				datasets.logging.get_logger(__name__)


a : Tuple		=				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

a : Union[str, Any]		=				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

a : Union[str, Any]		=				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

a : Tuple		=				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION				)
class 	lowerCamelCase_       (				datasets.Metric				):



					'''simple docstring'''







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







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION ,       citation=_CITATION ,       homepage='''https://github.com/google-research/bleurt''' ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''' ,       id='''sequence'''						),
								            '''references''': datasets.Value('''string''' ,       id='''sequence'''						),
								        }						) ,       codebase_urls=['''https://github.com/google-research/bleurt'''] ,       reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,       )






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''						)
											__lowercase 					=   '''bleurt-base-128'''

								if self.config_name.lower() in CHECKPOINT_URLS:
											__lowercase 					=   self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
											__lowercase 					=   self.config_name.upper()

								else:
											raise KeyError(
											    F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'						)

								# download the model checkpoint specified by self.config_name and set up the scorer
								__lowercase 					=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
								__lowercase 					=   score.BleurtScorer(os.path.join(snake_case_ ,       snake_case_						)						)






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.scorer.score(references=snake_case_ ,       candidates=snake_case_						)
								return {"scores": scores}


