


'''simple docstring'''
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()
__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

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

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

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

					return config


def     a   (    )						->			Tuple:



					'''simple docstring'''






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


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a=False     )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				get_mobilenet_va_config(__a     )

					# Load 🤗 model
					UpperCamelCase__     :int							      =				MobileNetVaForImageClassification(__a     ).eval()

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

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

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										UpperCamelCase__     :Optional[Any]							      =				torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]     )
					elif model_name == "mobilenet_v1_0.75_192":
										UpperCamelCase__     :int							      =				torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
					else:
										UpperCamelCase__     :Optional[Any]							      =				None

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

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

					if push_to_hub:
										print('''Pushing to the hub...'''     )
										UpperCamelCase__     :int							      =				'''google/''' + model_name
										image_processor.push_to_hub(__a     )
										model.push_to_hub(__a     )


if __name__ == "__main__":
			__snake_case							   =  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.'''
			)

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



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Speech2TextFeatureExtractor'
		_a									=       'Speech2TextTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.feature_extractor
							UpperCamelCase__     :Optional[Any]							      =				False




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor(*UpperCamelCase_					,     **UpperCamelCase_							)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
												UpperCamelCase__     :Optional[Any]							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :List[Any]							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Tuple							      =				args[0]
												UpperCamelCase__     :Optional[int]							      =				args[1:]

							if audio is None and text is None:
												raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

							if audio is not None:
												UpperCamelCase__     :Any							      =				self.feature_extractor(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												UpperCamelCase__     :Dict							      =				encodings['''input_ids''']
												return inputs




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your audio inputs, or in a separate call.'''							)
							UpperCamelCase__     :Optional[Any]							      =				True
							UpperCamelCase__     :Any							      =				self.tokenizer
							yield
							UpperCamelCase__     :Any							      =				self.feature_extractor
							UpperCamelCase__     :Any							      =				False