



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import WhisperFeatureExtractor

if is_torch_available():
	import torch

snake_case__					:     int    		=   random.Random()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=1.0  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None   ):
	if rng is None:
		__lowercase		       =			global_rng

	__lowercase		       =			[]
	for batch_idx in range(shape[0]   ):
		values.append([]   )
		for _ in range(shape[1]   ):
			values[-1].append(rng.random() * scale   )

	return values

@require_torch
@require_torchaudio
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							List[str]=7							,      lowerCamelCase	:							Optional[int]=400							,      lowerCamelCase	:							Union[str, Any]=2_000							,      lowerCamelCase	:							Union[str, Any]=10							,      lowerCamelCase	:							Optional[int]=160							,      lowerCamelCase	:							Union[str, Any]=8							,      lowerCamelCase	:							str=0.0							,      lowerCamelCase	:							Dict=4_000							,      lowerCamelCase	:							Optional[Any]=False							,      lowerCamelCase	:							int=True							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			min_seq_length
							__lowercase		       =			max_seq_length
							__lowercase		       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__lowercase		       =			padding_value
							__lowercase		       =			sampling_rate
							__lowercase		       =			return_attention_mask
							__lowercase		       =			do_normalize
							__lowercase		       =			feature_size
							__lowercase		       =			chunk_length
							__lowercase		       =			hop_length



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return {
							    "feature_size": self.feature_size,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int=False							,      lowerCamelCase	:							Union[str, Any]=False	):






							'''simple docstring'''



							def _flatten(lowerCamelCase	:							Any	):
								return list(itertools.chain(*lowerCamelCase	)	)

							if equal_length:
								__lowercase		       =			[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
							else:
								# make sure that inputs increase in size
								__lowercase		       =			[
								    floats_list((x, self.feature_size)	)
								    for x in range(self.min_seq_length							,      self.max_seq_length							,      self.seq_length_diff	)
								]
							if numpify:
								__lowercase		       =			[np.asarray(lowerCamelCase	) for x in speech_inputs]
							return speech_inputs

@require_torch
@require_torchaudio
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      WhisperFeatureExtractor if is_speech_available() else None



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			WhisperFeatureExtractionTester(self	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.feature_extraction_class(**self.feat_extract_dict	)

							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			feat_extract_first.save_pretrained(lowerCamelCase	)[0]
								check_json_file_has_correct_format(lowerCamelCase	)
								__lowercase		       =			self.feature_extraction_class.from_pretrained(lowerCamelCase	)

							__lowercase		       =			feat_extract_first.to_dict()
							__lowercase		       =			feat_extract_second.to_dict()
							__lowercase		       =			feat_extract_first.mel_filters
							__lowercase		       =			feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase	)	)
							self.assertEqual(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.feature_extraction_class(**self.feat_extract_dict	)

							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			os.path.join(lowerCamelCase							,      "feat_extract.json"	)
								feat_extract_first.to_json_file(lowerCamelCase	)
								__lowercase		       =			self.feature_extraction_class.from_json_file(lowerCamelCase	)

							__lowercase		       =			feat_extract_first.to_dict()
							__lowercase		       =			feat_extract_second.to_dict()
							__lowercase		       =			feat_extract_first.mel_filters
							__lowercase		       =			feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase	)	)
							self.assertEqual(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							# create three inputs of length 800, 1000, and 1200
							__lowercase		       =			[floats_list((1, x)	)[0] for x in range(800							,      1_400							,      200	)]
							__lowercase		       =			[np.asarray(lowerCamelCase	) for speech_input in speech_inputs]

							# Test feature size
							__lowercase		       =			feature_extractor(lowerCamelCase							,      padding="max_length"							,      return_tensors="np"	).input_features
							self.assertTrue(input_features.ndim == 3	)
							self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames	)
							self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size	)

							# Test not batched input
							__lowercase		       =			feature_extractor(speech_inputs[0]							,      return_tensors="np"	).input_features
							__lowercase		       =			feature_extractor(np_speech_inputs[0]							,      return_tensors="np"	).input_features
							self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)	)

							# Test batched
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase							,      lowerCamelCase	):
								self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)	)

							# Test 2-D numpy arrays are batched.
							__lowercase		       =			[floats_list((1, x)	)[0] for x in (800, 800, 800)]
							__lowercase		       =			np.asarray(lowerCamelCase	)
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase							,      lowerCamelCase	):
								self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)	)

							# Test truncation required
							__lowercase		       =			[floats_list((1, x)	)[0] for x in range(200							,      (feature_extractor.n_samples + 500)							,      200	)]
							__lowercase		       =			[np.asarray(lowerCamelCase	) for speech_input in speech_inputs]

							__lowercase		       =			[x[: feature_extractor.n_samples] for x in speech_inputs]
							__lowercase		       =			[np.asarray(lowerCamelCase	) for speech_input in speech_inputs_truncated]

							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="np"	).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase							,      lowerCamelCase	):
								self.assertTrue(np.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							import torch

							__lowercase		       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							__lowercase		       =			np.random.rand(100							,      32	).astype(np.floataa	)
							__lowercase		       =			np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
								__lowercase		       =			feature_extractor.pad([{"input_features": inputs}]							,      return_tensors="np"	)
								self.assertTrue(np_processed.input_features.dtype == np.floataa	)
								__lowercase		       =			feature_extractor.pad([{"input_features": inputs}]							,      return_tensors="pt"	)
								self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			load_dataset("hf-internal-testing/librispeech_asr_dummy"							,      "clean"							,      split="validation"	)
							# automatic decoding with librispeech
							__lowercase		       =			ds.sort("id"	).select(range(lowerCamelCase	)	)[:num_samples]["audio"]

							return [x["array"] for x in speech_samples]



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			torch.tensor(
							    [
							        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
							        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
							        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
							        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
							    ]	)
							# fmt: on

							__lowercase		       =			self._load_datasamples(1	)
							__lowercase		       =			WhisperFeatureExtractor()
							__lowercase		       =			feature_extractor(lowerCamelCase							,      return_tensors="pt"	).input_features
							self.assertEqual(input_features.shape							,      (1, 80, 3_000)	)
							self.assertTrue(torch.allclose(input_features[0, 0, :30]							,      lowerCamelCase							,      atol=1e-4	)	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							__lowercase		       =			self._load_datasamples(1	)[0]
							__lowercase		       =			((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
							__lowercase		       =			feat_extract.zero_mean_unit_var_norm([audio]							,      attention_mask=lowerCamelCase	)[0]

							self.assertTrue(np.all(np.mean(lowerCamelCase	) < 1e-3	)	)
							self.assertTrue(np.all(np.abs(np.var(lowerCamelCase	) - 1	) < 1e-3	)	)








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
	)



