



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case__					:     Dict    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Tuple      							=      ["""input_features""", """attention_mask"""]



						def __init__(	self	:							Any							,      lowerCamelCase	:							List[str]=80							,      lowerCamelCase	:							Optional[int]=16_000							,      lowerCamelCase	:							List[str]=80							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							Dict=True							,      **lowerCamelCase	:							List[str]							,      ):






							'''simple docstring'''



							super().__init__(feature_size=lowerCamelCase							,      sampling_rate=lowerCamelCase							,      padding_value=lowerCamelCase							,      **lowerCamelCase	)
							__lowercase		       =			num_mel_bins
							__lowercase		       =			do_ceptral_normalize
							__lowercase		       =			normalize_means
							__lowercase		       =			normalize_vars
							__lowercase		       =			True



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							np.ndarray							,      ):






							'''simple docstring'''



							__lowercase		       =			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							__lowercase		       =			torch.from_numpy(lowerCamelCase	).unsqueeze(0	)
							__lowercase		       =			ta_kaldi.fbank(lowerCamelCase							,      num_mel_bins=self.num_mel_bins							,      sample_frequency=self.sampling_rate	)
							return features.numpy()



						@staticmethod
						def     _snake_case	(	lowerCamelCase	:							np.ndarray							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[bool] = True							,      lowerCamelCase	:							Optional[bool] = True							,      lowerCamelCase	:							float = 0.0							,      ):






							'''simple docstring'''



							if normalize_means:
								__lowercase		       =			x[:input_length].mean(axis=0	)
								__lowercase		       =			np.subtract(lowerCamelCase							,      lowerCamelCase	)
							if normalize_vars:
								__lowercase		       =			x[:input_length].std(axis=0	)
								__lowercase		       =			np.divide(lowerCamelCase							,      lowerCamelCase	)

							if input_length < x.shape[0]:
								__lowercase		       =			padding_value

							# make sure array is in float32
							__lowercase		       =			x.astype(np.floataa	)

							return x



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							List[np.ndarray]							,      lowerCamelCase	:							Optional[np.ndarray] = None	):






							'''simple docstring'''



							__lowercase		       =			attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(lowerCamelCase							,      lowerCamelCase							,      self.normalize_means							,      self.normalize_vars							,      self.padding_value	)
							    for x, n in zip(lowerCamelCase							,      lowerCamelCase	)
							]



						def __call__(	self	:							Dict							,      lowerCamelCase	:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,      lowerCamelCase	:							Union[bool, str, PaddingStrategy] = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[Union[str, TensorType]] = None							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      **lowerCamelCase	:							Dict							,      ):






							'''simple docstring'''



							if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
									raise ValueError(
									    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
									    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
									    f""" {self.sampling_rate} and not {sampling_rate}."""	)
							else:
								logger.warning(
								    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
								    "Failing to do so can result in silent errors that might be hard to debug."	)

							__lowercase		       =			isinstance(lowerCamelCase							,      np.ndarray	) and len(raw_speech.shape	) > 1
							if is_batched_numpy and len(raw_speech.shape	) > 2:
								raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""	)
							__lowercase		       =			is_batched_numpy or (
							    isinstance(lowerCamelCase							,      (list, tuple)	) and (isinstance(raw_speech[0]							,      (np.ndarray, tuple, list)	))
							)

							if is_batched:
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.floataa	) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCamelCase							,      np.ndarray	):
								__lowercase		       =			np.asarray(lowerCamelCase							,      dtype=np.floataa	)
							elif isinstance(lowerCamelCase							,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								__lowercase		       =			raw_speech.astype(np.floataa	)

							# always return batch
							if not is_batched:
								__lowercase		       =			[raw_speech]

							# extract fbank features
							__lowercase		       =			[self._extract_fbank_features(lowerCamelCase	) for waveform in raw_speech]

							# convert into correct format for padding
							__lowercase		       =			BatchFeature({"input_features": features}	)

							__lowercase		       =			self.pad(
							    lowerCamelCase							,      padding=lowerCamelCase							,      max_length=lowerCamelCase							,      truncation=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      return_attention_mask=lowerCamelCase							,      **lowerCamelCase							,      )

							# make sure list is in array format
							__lowercase		       =			padded_inputs.get("input_features"	)
							if isinstance(input_features[0]							,      lowerCamelCase	):
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.floataa	) for feature in input_features]

							__lowercase		       =			padded_inputs.get("attention_mask"	)
							if attention_mask is not None:
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.intaa	) for array in attention_mask]

							# Utterance-level cepstral mean and variance normalization
							if self.do_ceptral_normalize:
								__lowercase		       =			(
								    np.array(lowerCamelCase							,      dtype=np.intaa	)
								    if self._get_padding_strategies(lowerCamelCase							,      max_length=lowerCamelCase	) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								__lowercase		       =			self.normalize(
								    padded_inputs["input_features"]							,      attention_mask=lowerCamelCase	)

							if return_tensors is not None:
								__lowercase		       =			padded_inputs.convert_to_tensors(lowerCamelCase	)

							return padded_inputs








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__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_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_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_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    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			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 MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			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":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			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..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



