



"""simple docstring"""





import re
import string

import numpy as np

import datasets


lowerCamelCase  			=		"""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

lowerCamelCase  			=		"""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

lowerCamelCase  			=		"""
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,   _KWARGS_DESCRIPTION )
class  lowercase__  (       datasets.Metric ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[str]       )					->		Dict:


             '''simple docstring'''




             return datasets.MetricInfo(
                 description=_DESCRIPTION						,							citation=_CITATION						,							inputs_description=_KWARGS_DESCRIPTION						,							features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"						,							id="sequence"       ),
                         "references": datasets.Value("string"						,							id="sequence"       ),
                     }       )						,							reference_urls=[]						,							)







       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Optional[Any]=None						,							_UpperCAmelCase     :    List[Any]=False						,							_UpperCAmelCase     :    Tuple=False						,							_UpperCAmelCase     :    Optional[Any]=False						,							)					->		Dict:


             '''simple docstring'''




             if regexes_to_ignore is not None:
                   for s in regexes_to_ignore:
                         UpperCAmelCase_     					=				np.array([re.sub(_UpperCAmelCase						,							""						,							_UpperCAmelCase       ) for x in predictions]       )
                         UpperCAmelCase_     					=				np.array([re.sub(_UpperCAmelCase						,							""						,							_UpperCAmelCase       ) for x in references]       )
             else:
                   UpperCAmelCase_     					=				np.asarray(_UpperCAmelCase       )
                   UpperCAmelCase_     					=				np.asarray(_UpperCAmelCase       )

             if ignore_case:
                   UpperCAmelCase_     					=				np.char.lower(_UpperCAmelCase       )
                   UpperCAmelCase_     					=				np.char.lower(_UpperCAmelCase       )

             if ignore_punctuation:
                   UpperCAmelCase_     					=				string.punctuation.maketrans(""						,							""						,							string.punctuation       )
                   UpperCAmelCase_     					=				np.char.translate(_UpperCAmelCase						,							table=_UpperCAmelCase       )
                   UpperCAmelCase_     					=				np.char.translate(_UpperCAmelCase						,							table=_UpperCAmelCase       )

             if ignore_numbers:
                   UpperCAmelCase_     					=				string.digits.maketrans(""						,							""						,							string.digits       )
                   UpperCAmelCase_     					=				np.char.translate(_UpperCAmelCase						,							table=_UpperCAmelCase       )
                   UpperCAmelCase_     					=				np.char.translate(_UpperCAmelCase						,							table=_UpperCAmelCase       )

             UpperCAmelCase_     					=				predictions == references

             return {"exact_match": np.mean(_UpperCAmelCase       ) * 100}











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()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

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

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

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

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

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




