'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowercase        =    random.Random()

if is_torch_available():
						import torch






def 							snake_case__				(     _A:   Dict ,  _A:   str=1.0 ,  _A:   Any=None ,  _A:   Tuple=None					)       ->      Union[str, Any]:

		'''simple docstring'''


		if rng is None:
				lowerCAmelCase     =    global_rng

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

		return values






class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=7			,       __lowerCAmelCase=400			,       __lowerCAmelCase=2000			,       __lowerCAmelCase=1			,       __lowerCAmelCase=0.0			,       __lowerCAmelCase=16000			,       __lowerCAmelCase=True			,       __lowerCAmelCase=True			,       ):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


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







			def 				a_			(  self			,       __lowerCAmelCase=False			,       __lowerCAmelCase=False):




					"""simple docstring"""


					def _flatten(__lowerCAmelCase):
							return list(itertools.chain(*__lowerCAmelCase))

					if equal_length:
							lowerCAmelCase     =    floats_list((self.batch_size, self.max_seq_length))
					else:
							# make sure that inputs increase in size
							lowerCAmelCase     =    [
							    _flatten(floats_list((x, self.feature_size)))
							    for x in range(self.min_seq_length			,       self.max_seq_length			,       self.seq_length_diff)
							]

					if numpify:
							lowerCAmelCase     =    [np.asarray(__lowerCAmelCase) for x in speech_inputs]

					return speech_inputs







@require_torch
@require_torchaudio
class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			ASTFeatureExtractor



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    ASTFeatureExtractionTester(self)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					# create three inputs of length 800, 1000, and 1200
					lowerCAmelCase     =    [floats_list((1, x))[0] for x in range(800			,       1400			,       200)]
					lowerCAmelCase     =    [np.asarray(__lowerCAmelCase) for speech_input in speech_inputs]

					# Test not batched input
					lowerCAmelCase     =    feat_extract(speech_inputs[0]			,       return_tensors="""np""").input_values
					lowerCAmelCase     =    feat_extract(np_speech_inputs[0]			,       return_tensors="""np""").input_values
					self.assertTrue(np.allclose(__lowerCAmelCase			,       __lowerCAmelCase			,       atol=1E-3))

					# Test batched
					lowerCAmelCase     =    feat_extract(__lowerCAmelCase			,       padding=__lowerCAmelCase			,       return_tensors="""np""").input_values
					lowerCAmelCase     =    feat_extract(__lowerCAmelCase			,       padding=__lowerCAmelCase			,       return_tensors="""np""").input_values
					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.
					lowerCAmelCase     =    [floats_list((1, x))[0] for x in (800, 800, 800)]
					lowerCAmelCase     =    np.asarray(__lowerCAmelCase)
					lowerCAmelCase     =    feat_extract(__lowerCAmelCase			,       return_tensors="""np""").input_values
					lowerCAmelCase     =    feat_extract(__lowerCAmelCase			,       return_tensors="""np""").input_values
					for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase			,       __lowerCAmelCase):
							self.assertTrue(np.allclose(__lowerCAmelCase			,       __lowerCAmelCase			,       atol=1E-3))



			@require_torch
			def 				a_			(  self):




					"""simple docstring"""


					import torch

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

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



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					from datasets import load_dataset

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

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







			@require_torch
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    torch.tensor(
					    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
					     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
					     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
					     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869])
					# fmt: on

					lowerCAmelCase     =    self._load_datasamples(1)
					lowerCAmelCase     =    ASTFeatureExtractor()
					lowerCAmelCase     =    feature_extractor(__lowerCAmelCase			,       return_tensors="""pt""").input_values
					self.assertEquals(input_values.shape			,       (1, 1024, 128))
					self.assertTrue(torch.allclose(input_values[0, 0, :30]			,       __lowerCAmelCase			,       atol=1E-4))





'''simple docstring'''




import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase        =    {
    '''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
    ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}






def 							snake_case__				(     _A:   str = "dhaka" ,  _A:   int = 5					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    min(_A ,  50					)  # Prevent abuse!
		lowerCAmelCase     =    {
		    """q""": query,
		    """tbm""": """isch""",
		    """hl""": """en""",
		    """ijn""": """0""",
		}

		lowerCAmelCase     =    requests.get("""https://www.google.com/search""" ,  params=_A ,  headers=_A					)
		lowerCAmelCase     =    BeautifulSoup(html.text ,  """html.parser"""					)
		lowerCAmelCase     =    """""".join(
		    re.findall(r"""AF_initDataCallback\(([^<]+)\);""" ,  str(soup.select("""script"""					)					)					)					)

		lowerCAmelCase     =    json.dumps(_A					)
		lowerCAmelCase     =    json.loads(_A					)

		lowerCAmelCase     =    re.findall(
		    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",""" ,  _A ,  )
		if not matched_google_image_data:
				return 0

		lowerCAmelCase     =    re.sub(
		    r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]""" ,  """""" ,  str(_A					) ,  )

		lowerCAmelCase     =    re.findall(
		    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]""" ,  _A ,  )
		for index, fixed_full_res_image in enumerate(_A					):
				if index >= max_images:
						return index
				lowerCAmelCase     =    bytes(_A ,  """ascii"""					).decode(
				    """unicode-escape"""					)
				lowerCAmelCase     =    bytes(_A ,  """ascii"""					).decode(
				    """unicode-escape"""					)
				lowerCAmelCase     =    urllib.request.build_opener()
				lowerCAmelCase     =    [
				    (
				        """User-Agent""",
				        """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
				        """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
				    )
				]
				urllib.request.install_opener(_A					)
				lowerCAmelCase     =    f"query_{query.replace(' ' ,  '_'					)}"
				if not os.path.exists(_A					):
						os.makedirs(_A					)
				urllib.request.urlretrieve(  # noqa: S310
				    _A ,  f"{path_name}/original_size_img_{index}.jpg"					)
		return index


if __name__ == "__main__":
						try:
												__lowercase        =    download_images_from_google_query(sys.argv[1])
												print(f'{image_count} images were downloaded to disk.')
						except IndexError:
												print('''Please provide a search term.''')
												raise




