






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   MobileNetVaConfig(layer_norm_eps=0.001      )

				if "_quant" in model_name:
								raise ValueError('Quantized models are not supported.'      )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'    ,    lowerCAmelCase__      )
				if matches:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   float(matches[1]      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   int(matches[2]      )

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   1001
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   'huggingface/label-files'
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   json.load(open(hf_hub_download(lowerCAmelCase__    ,    lowerCAmelCase__    ,    repo_type='dataset'      )    ,    'r'      )      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {int(lowerCAmelCase__      ) + 1: v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'background'
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   idalabel
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {v: k for k, v in idalabel.items()}

				return config



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   'http://images.cocodataset.org/val2017/000000039769.jpg'
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Image.open(requests.get(lowerCAmelCase__    ,    stream=lowerCAmelCase__      ).raw      )
				return im



@torch.no_grad()
def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Optional[Any]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_mobilenet_va_config(lowerCAmelCase__      )

				# Load 🤗 model
				SCREAMING_SNAKE_CASE_   :	Dict													=   MobileNetVaForImageClassification(lowerCAmelCase__      ).eval()

				# Load weights from TensorFlow checkpoint
				load_tf_weights_in_mobilenet_va(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MobileNetVaImageProcessor(
				    crop_size={'width': config.image_size, 'height': config.image_size}    ,    size={'shortest_edge': config.image_size + 32}    ,    )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processor(images=prepare_img()    ,    return_tensors='pt'      )
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   model(**lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   outputs.logits

				assert logits.shape == (1, 1001)

				if model_name == "mobilenet_v1_1.0_224":
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.tensor([-4.1_739, -1.1_233, 3.1_205]      )
				elif model_name == "mobilenet_v1_0.75_192":
								SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor([-3.9_440, -2.3_141, -0.3_333]      )
				else:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   None

				if expected_logits is not None:
								assert torch.allclose(logits[0, :3]    ,    lowerCAmelCase__    ,    atol=1E-4      )

				Path(lowerCAmelCase__      ).mkdir(exist_ok=lowerCAmelCase__      )
				print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
				model.save_pretrained(lowerCAmelCase__      )
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
				image_processor.save_pretrained(lowerCAmelCase__      )

				if push_to_hub:
								print('Pushing to the hub...'      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'google/' + model_name
								image_processor.push_to_hub(lowerCAmelCase__      )
								model.push_to_hub(lowerCAmelCase__      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='mobilenet_v1_1.0_224',
				    type=str,
				    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
				)
				parser.add_argument(
				    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A        =			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)







import os

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

import datasets


A        =			datasets.logging.get_logger(__name__)


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

A        =			'\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A        =			'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A        =			{
    '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 							__a							(  datasets.Metric						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						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   __snake_case			(						self ,    UpperCamelCase__       ):
						# check that config name specifies a valid BLEURT model
						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\').'       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   'bleurt-base-128'

						if self.config_name.lower() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Any													=   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
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
						SCREAMING_SNAKE_CASE_   :	int													=   score.BleurtScorer(os.path.join(UpperCamelCase__ ,    UpperCamelCase__       )       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.scorer.score(references=UpperCamelCase__ ,    candidates=UpperCamelCase__       )
						return {"scores": scores}