






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_		:						Dict             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= MobileNetVaConfig(layer_norm_eps=0.0_01)

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

			SCREAMING_SNAKE_CASE						= re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'							,       _UpperCAmelCase)
			if matches:
						SCREAMING_SNAKE_CASE						= float(matches[1])
						SCREAMING_SNAKE_CASE						= 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						= 1001
			SCREAMING_SNAKE_CASE						= 'imagenet-1k-id2label.json'
			SCREAMING_SNAKE_CASE						= 'huggingface/label-files'
			SCREAMING_SNAKE_CASE						= json.load(open(hf_hub_download(_UpperCAmelCase							,       _UpperCAmelCase							,       repo_type='dataset')							,       'r'))
			SCREAMING_SNAKE_CASE						= {int(_UpperCAmelCase) + 1: v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE						= 'background'
			SCREAMING_SNAKE_CASE						= idalabel
			SCREAMING_SNAKE_CASE						= {v: k for k, v in idalabel.items()}

			return config






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE						= Image.open(requests.get(_UpperCAmelCase							,       stream=_UpperCAmelCase).raw)
			return im






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE						= get_mobilenet_va_config(_UpperCAmelCase)

			# Load 🤗 model
			SCREAMING_SNAKE_CASE						= MobileNetVaForImageClassification(_UpperCAmelCase).eval()

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

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

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						SCREAMING_SNAKE_CASE						= torch.tensor([-4.17_39, -1.12_33, 3.12_05])
			elif model_name == "mobilenet_v1_0.75_192":
						SCREAMING_SNAKE_CASE						= torch.tensor([-3.94_40, -2.31_41, -0.33_33])
			else:
						SCREAMING_SNAKE_CASE						= None

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

			Path(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
			print(F'''Saving model {model_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 push_to_hub:
						print('Pushing to the hub...')
						SCREAMING_SNAKE_CASE						= 'google/' + model_name
						image_processor.push_to_hub(_UpperCAmelCase)
						model.push_to_hub(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						List[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.'
				)

				a_		:						Union[str, Any]             =    parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)












import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


a_		:						Optional[int]             =    '\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n'

a_		:						Tuple             =    '\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'

a_		:						Optional[Any]             =    '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "precision": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'precision@10\': 1.0}\n\n'






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return float((preds == labels).mean())






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= simple_accuracy(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= float(fa_score(y_true=_UpperCAmelCase							,       y_pred=_UpperCAmelCase))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= en_sentvecs.shape[0]

			# mean centering
			SCREAMING_SNAKE_CASE						= en_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)
			SCREAMING_SNAKE_CASE						= in_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)

			SCREAMING_SNAKE_CASE						= cdist(_UpperCAmelCase							,       _UpperCAmelCase							,       'cosine')
			SCREAMING_SNAKE_CASE						= np.array(range(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= sim.argsort(axis=1)[:, :10]
			SCREAMING_SNAKE_CASE						= np.any(preds == actual[:, None]							,       axis=1)
			return float(matches.mean())







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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						if self.config_name not in [
						    "wnli",
						    "copa",
						    "sna",
						    "csqa",
						    "wstp",
						    "inltkh",
						    "bbca",
						    "cvit-mkb-clsr",
						    "iitp-mr",
						    "iitp-pr",
						    "actsa-sc",
						    "md",
						    "wiki-ner",
						]:
									raise KeyError(
									    'You should supply a configuration name selected in '
									    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
									    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
									    '"wiki-ner"]')
						return datasets.MetricInfo(
						    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
						        {
						            'predictions': datasets.Value('int64')
						            if self.config_name != 'cvit-mkb-clsr'
						            else datasets.Sequence(datasets.Value('float32')),
						            'references': datasets.Value('int64')
						            if self.config_name != 'cvit-mkb-clsr'
						            else datasets.Sequence(datasets.Value('float32')),
						        })		,					codebase_urls=[]		,					reference_urls=[]		,					format='numpy' if self.config_name != 'cvit-mkb-clsr' else None		,					)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						if self.config_name == "cvit-mkb-clsr":
									return {"precision@10": precision_at_aa(a		,					a)}
						elif self.config_name in ["wiki-ner"]:
									return acc_and_fa(a		,					a)
						elif self.config_name in [
						    "wnli",
						    "copa",
						    "sna",
						    "csqa",
						    "wstp",
						    "inltkh",
						    "bbca",
						    "iitp-mr",
						    "iitp-pr",
						    "actsa-sc",
						    "md",
						]:
									return {"accuracy": simple_accuracy(a		,					a)}
						else:
									raise KeyError(
									    'You should supply a configuration name selected in '
									    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
									    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
									    '"wiki-ner"]')




