
'''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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		snake_case   :    str			       =		MobileNetVaConfig(layer_norm_eps=0.001       )

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

		snake_case   :    Tuple			       =		re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''      ,    SCREAMING_SNAKE_CASE__       )
		if matches:
				snake_case   :    List[str]			       =		float(matches[1]       )
				snake_case   :    str			       =		int(matches[2]       )

		if model_name.startswith('''deeplabv3_'''       ):
				snake_case   :    Tuple			       =		8
				snake_case   :    Tuple			       =		21
				snake_case   :    Optional[Any]			       =		'''pascal-voc-id2label.json'''
		else:
				# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
				# of the usual 1000. The first class (index 0) is "background".
				snake_case   :    List[str]			       =		1001
				snake_case   :    Tuple			       =		'''imagenet-1k-id2label.json'''

		snake_case   :    Optional[Any]			       =		'''huggingface/label-files'''
		snake_case   :    Union[str, Any]			       =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    repo_type='''dataset'''       )      ,    '''r'''       )       )

		if config.num_labels == 1001:
				snake_case   :    Optional[int]			       =		{int(SCREAMING_SNAKE_CASE__       ) + 1: v for k, v in idalabel.items()}
				snake_case   :    int			       =		'''background'''
		else:
				snake_case   :    List[str]			       =		{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}

		snake_case   :    str			       =		idalabel
		snake_case   :    Optional[Any]			       =		{v: k for k, v in idalabel.items()}

		return config




def 					_UpperCamelCase		(    )   ->				Any:


		'''simple docstring'''


		snake_case   :    Union[str, Any]			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case   :    Tuple			       =		Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,    stream=SCREAMING_SNAKE_CASE__       ).raw       )
		return im




@torch.no_grad()
def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=False       )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    List[str]			       =		get_mobilenet_va_config(SCREAMING_SNAKE_CASE__       )

		# Load 🤗 model
		if model_name.startswith('''deeplabv3_'''       ):
				snake_case   :    Optional[int]			       =		MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__       ).eval()
		else:
				snake_case   :    Tuple			       =		MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__       ).eval()

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

		# Check outputs on an image, prepared by MobileNetV2ImageProcessor
		snake_case   :    Dict			       =		MobileNetVaImageProcessor(
		    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,    size={'''shortest_edge''': config.image_size + 32}      ,    )
		snake_case   :    Any			       =		image_processor(images=prepare_img()      ,    return_tensors='''pt'''       )
		snake_case   :    str			       =		model(**SCREAMING_SNAKE_CASE__       )
		snake_case   :    str			       =		outputs.logits

		if model_name.startswith('''deeplabv3_'''       ):
				assert logits.shape == (1, 21, 65, 65)

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						snake_case   :    Optional[int]			       =		torch.tensor(
						    [
						        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
						        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
						        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
						    ]       )

				else:
						raise ValueError(F'Unknown model name: {model_name}'       )

				assert torch.allclose(logits[0, :3, :3, :3]      ,    SCREAMING_SNAKE_CASE__      ,    atol=1E-4       )
		else:
				assert logits.shape == (1, 1001)

				if model_name == "mobilenet_v2_1.4_224":
						snake_case   :    Dict			       =		torch.tensor([0.0181, -1.0015, 0.4688]       )
				elif model_name == "mobilenet_v2_1.0_224":
						snake_case   :    str			       =		torch.tensor([0.2445, -1.1993, 0.1905]       )
				elif model_name == "mobilenet_v2_0.75_160":
						snake_case   :    Optional[int]			       =		torch.tensor([0.2482, 0.4136, 0.6669]       )
				elif model_name == "mobilenet_v2_0.35_96":
						snake_case   :    Optional[int]			       =		torch.tensor([0.1451, -0.4624, 0.7192]       )
				else:
						snake_case   :    Dict			       =		None

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

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

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


if __name__ == "__main__":
						lowercase__		    =	argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="mobilenet_v2_1.0_224",
						    type=str,
						    help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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."
						)

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




'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		os.path.join(self.tmpdirname   ,     UpperCamelCase__	)
				with open(self.feature_extraction_file   ,     '''w'''   ,     encoding='''utf-8'''	) as fp:
						fp.write(json.dumps(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_tokenizer()
				snake_case   :    List[str]			       =		self.get_feature_extractor()

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		SpeechTaProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		SpeechTaProcessor.from_pretrained(
				    self.tmpdirname   ,     bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''   ,     do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    List[str]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		processor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		processor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    Union[str, Any]			       =		self.get_tokenizer()

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[int]			       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



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






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    Tuple			       =		self.get_tokenizer()

				snake_case   :    List[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				self.assertListEqual(
				    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg='''`processor` and `feature_extractor` model input names do not match'''   ,     )


