






'''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()
lowerCamelCase_		  =						logging.get_logger(__name__)
def   lowerCamelCase      (		a_			)			->    str:
							lowerCAmelCase_       		=  MobileNetVaConfig(layer_norm_eps=0.001			)

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

							lowerCAmelCase_       		=  re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'				,		_UpperCamelCase			)
							if matches:
														lowerCAmelCase_       		=  float(matches[1]			)
														lowerCAmelCase_       		=  int(matches[2]			)

							if model_name.startswith('deeplabv3_'			):
														lowerCAmelCase_       		=  8
														lowerCAmelCase_       		=  21
														lowerCAmelCase_       		=  '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".
														lowerCAmelCase_       		=  1_001
														lowerCAmelCase_       		=  'imagenet-1k-id2label.json'

							lowerCAmelCase_       		=  'huggingface/label-files'
							lowerCAmelCase_       		=  json.load(open(hf_hub_download(_UpperCamelCase				,		_UpperCamelCase				,		repo_type='dataset'			)				,		'r'			)			)

							if config.num_labels == 1_001:
														lowerCAmelCase_       		=  {int(_UpperCamelCase			) + 1: v for k, v in idalabel.items()}
														lowerCAmelCase_       		=  'background'
							else:
														lowerCAmelCase_       		=  {int(_UpperCamelCase			): v for k, v in idalabel.items()}

							lowerCAmelCase_       		=  idalabel
							lowerCAmelCase_       		=  {v: k for k, v in idalabel.items()}

							return config
def   lowerCamelCase      (		)			->    Dict:
							lowerCAmelCase_       		=  'http://images.cocodataset.org/val2017/000000039769.jpg'
							lowerCAmelCase_       		=  Image.open(requests.get(_UpperCamelCase				,		stream=_UpperCamelCase			).raw			)
							return im
@torch.no_grad()
def   lowerCamelCase      (		a_				,		a_				,		a_				,		a_=False			)			->    int:
							lowerCAmelCase_       		=  get_mobilenet_va_config(_UpperCamelCase			)

							# Load 🤗 model
							if model_name.startswith('deeplabv3_'			):
														lowerCAmelCase_       		=  MobileNetVaForSemanticSegmentation(_UpperCamelCase			).eval()
							else:
														lowerCAmelCase_       		=  MobileNetVaForImageClassification(_UpperCamelCase			).eval()

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

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

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

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					lowerCAmelCase_       		=  torch.tensor(
																					    [
																					        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
																					        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
																					        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
																					    ]			)

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

														assert torch.allclose(logits[0, :3, :3, :3]				,		_UpperCamelCase				,		atol=1e-4			)
							else:
														assert logits.shape == (1, 1_001)

														if model_name == "mobilenet_v2_1.4_224":
																					lowerCAmelCase_       		=  torch.tensor([0.0_181, -1.0_015, 0.4_688]			)
														elif model_name == "mobilenet_v2_1.0_224":
																					lowerCAmelCase_       		=  torch.tensor([0.2_445, -1.1_993, 0.1_905]			)
														elif model_name == "mobilenet_v2_0.75_160":
																					lowerCAmelCase_       		=  torch.tensor([0.2_482, 0.4_136, 0.6_669]			)
														elif model_name == "mobilenet_v2_0.35_96":
																					lowerCAmelCase_       		=  torch.tensor([0.1_451, -0.4_624, 0.7_192]			)
														else:
																					lowerCAmelCase_       		=  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...'			)
														lowerCAmelCase_       		=  'google/' + model_name
														image_processor.push_to_hub(_UpperCamelCase			)
														model.push_to_hub(_UpperCamelCase			)


if __name__ == "__main__":
		lowerCamelCase_		  =						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."""
		)

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











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCamelCase_		  =						get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_torch
@require_torchaudio
@require_sentencepiece
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  tempfile.mkdtemp()

														lowerCAmelCase_       		=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
														lowerCAmelCase_       		=  dict(zip(lowercase_    ,    range(len(lowercase_     )     )     )     )
														lowerCAmelCase_       		=  Path(self.tmpdirname     )
														save_json(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['vocab_file']     )
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['spm_file']     )

														lowerCAmelCase_       		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
														tokenizer.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  {
														    'feature_size': 2_4,
														    'num_mel_bins': 2_4,
														    'padding_value': 0.0,
														    'sampling_rate': 1_6_0_0_0,
														    'return_attention_mask': False,
														    'do_normalize': True,
														}
														save_json(lowercase_    ,    save_dir / FEATURE_EXTRACTOR_NAME     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      List[Any]:
														'''simple docstring'''

														return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														shutil.rmtree(self.tmpdirname     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_tokenizer()
														lowerCAmelCase_       		=  self.get_feature_extractor()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

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

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

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  SpeechaTextProcessor(
														    tokenizer=self.get_tokenizer()    ,    feature_extractor=self.get_feature_extractor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  self.get_tokenizer(bos_token='(BOS)'    ,    eos_token='(EOS)'     )
														lowerCAmelCase_       		=  self.get_feature_extractor(do_normalize=lowercase_    ,    padding_value=1.0     )

														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(
														    self.tmpdirname    ,    bos_token='(BOS)'    ,    eos_token='(EOS)'    ,    do_normalize=lowercase_    ,    padding_value=1.0     )

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

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

							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  floats_list((3, 1_0_0_0)     )

														lowerCAmelCase_       		=  feature_extractor(lowercase_    ,    return_tensors='np'     )
														lowerCAmelCase_       		=  processor(lowercase_    ,    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   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  'This is a test string'

														lowerCAmelCase_       		=  processor(text=lowercase_     )

														lowerCAmelCase_       		=  tokenizer(lowercase_     )

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

							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase_       		=  processor.batch_decode(lowercase_     )
														lowerCAmelCase_       		=  tokenizer.batch_decode(lowercase_     )

														self.assertListEqual(lowercase_    ,    lowercase_     )






							def   _lowercase (	self     )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

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



