


"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
	from huggingface_hub import snapshot_download
	from pyctcdecode import BeamSearchDecoderCTC

	from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
	from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
	from transformers import WavaVecaForCTC
@require_pyctcdecode
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
										__lowerCAmelCase       					=    dict(zip(UpperCamelCase__					,     range(len(UpperCamelCase__			)			)			)			)

										__lowerCAmelCase       					=    {
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										__lowerCAmelCase       					=    {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_60_00,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										__lowerCAmelCase       					=    tempfile.mkdtemp()
										__lowerCAmelCase       					=    os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]			)
										__lowerCAmelCase       					=    os.path.join(self.tmpdirname					,     UpperCamelCase__			)
										with open(self.vocab_file					,     "w"					,     encoding="utf-8"			) as fp:
													fp.write(json.dumps(UpperCamelCase__			) + "\n"			)

										with open(self.feature_extraction_file					,     "w"					,     encoding="utf-8"			) as fp:
													fp.write(json.dumps(UpperCamelCase__			) + "\n"			)

										# load decoder from hub
										__lowerCAmelCase       					=    '''hf-internal-testing/ngram-beam-search-decoder'''

							def     snake_case       (  self					,     **__a			):
										__lowerCAmelCase       					=    self.add_kwargs_tokens_map.copy()
										kwargs.update(UpperCamelCase__			)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,     **UpperCamelCase__			)

							def     snake_case       (  self					,     **__a			):
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,     **UpperCamelCase__			)

							def     snake_case       (  self					,     **__a			):
										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name					,     **UpperCamelCase__			)

							def     snake_case       (  self			):
										shutil.rmtree(self.tmpdirname			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										processor.save_pretrained(self.tmpdirname			)
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname			)

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

										# feature extractor
										self.assertEqual(processor.feature_extractor.to_json_string()					,     feature_extractor.to_json_string()			)
										self.assertIsInstance(processor.feature_extractor					,     UpperCamelCase__			)

										# decoder
										self.assertEqual(processor.decoder._alphabet.labels					,     decoder._alphabet.labels			)
										self.assertEqual(
										    processor.decoder.model_container[decoder._model_key]._unigram_set					,     decoder.model_container[decoder._model_key]._unigram_set					,     )
										self.assertIsInstance(processor.decoder					,     UpperCamelCase__			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(
										    tokenizer=self.get_tokenizer()					,     feature_extractor=self.get_feature_extractor()					,     decoder=self.get_decoder()			)
										processor.save_pretrained(self.tmpdirname			)

										# make sure that error is thrown when decoder alphabet doesn't match
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained(
										    self.tmpdirname					,     alpha=5.0					,     beta=3.0					,     score_boundary=-7.0					,     unk_score_offset=3			)

										# decoder
										self.assertEqual(processor.language_model.alpha					,     5.0			)
										self.assertEqual(processor.language_model.beta					,     3.0			)
										self.assertEqual(processor.language_model.score_boundary					,     -7.0			)
										self.assertEqual(processor.language_model.unk_score_offset					,     3			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(["xx"]			)
										with self.assertRaisesRegex(UpperCamelCase__					,     "include"			):
													WavaVecaProcessorWithLM(
													    tokenizer=UpperCamelCase__					,     feature_extractor=self.get_feature_extractor()					,     decoder=self.get_decoder()			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    floats_list((3, 10_00)			)

										__lowerCAmelCase       					=    feature_extractor(UpperCamelCase__					,     return_tensors="np"			)
										__lowerCAmelCase       					=    processor(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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    '''This is a test string'''

										__lowerCAmelCase       					=    processor(text=UpperCamelCase__			)

										__lowerCAmelCase       					=    tokenizer(UpperCamelCase__			)

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

							def     snake_case       (  self					,     __a=(2, 10, 16)					,     __a=77			):
										np.random.seed(UpperCamelCase__			)
										return np.random.rand(*UpperCamelCase__			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    self._get_dummy_logits(shape=(10, 16)					,     seed=13			)

										__lowerCAmelCase       					=    processor.decode(UpperCamelCase__			)

										__lowerCAmelCase       					=    decoder.decode_beams(UpperCamelCase__			)[0]

										self.assertEqual(decoded_decoder[0]					,     decoded_processor.text			)
										self.assertEqual("</s> <s> </s>"					,     decoded_processor.text			)
										self.assertEqual(decoded_decoder[-2]					,     decoded_processor.logit_score			)
										self.assertEqual(decoded_decoder[-1]					,     decoded_processor.lm_score			)

							@parameterized.expand([[None], ["fork"], ["spawn"]]			)
							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    self._get_dummy_logits()

										# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
										#       otherwise, the LM won't be available to the pool's sub-processes.
										# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
										if pool_context is None:
													__lowerCAmelCase       					=    processor.batch_decode(UpperCamelCase__			)
										else:
													with get_context(UpperCamelCase__			).Pool() as pool:
																__lowerCAmelCase       					=    processor.batch_decode(UpperCamelCase__					,     UpperCamelCase__			)

										__lowerCAmelCase       					=    list(UpperCamelCase__			)

										with get_context("fork"			).Pool() as p:
													__lowerCAmelCase       					=    decoder.decode_beams_batch(UpperCamelCase__					,     UpperCamelCase__			)

										__lowerCAmelCase       					=    [], [], []
										for beams in decoded_beams:
													texts_decoder.append(beams[0][0]			)
													logit_scores_decoder.append(beams[0][-2]			)
													lm_scores_decoder.append(beams[0][-1]			)

										self.assertListEqual(UpperCamelCase__					,     decoded_processor.text			)
										self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]					,     decoded_processor.text			)
										self.assertListEqual(UpperCamelCase__					,     decoded_processor.logit_score			)
										self.assertListEqual(UpperCamelCase__					,     decoded_processor.lm_score			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    self._get_dummy_logits()

										__lowerCAmelCase       					=    15
										__lowerCAmelCase       					=    -20.0
										__lowerCAmelCase       					=    -4.0

										__lowerCAmelCase       					=    processor.batch_decode(
										    UpperCamelCase__					,     beam_width=UpperCamelCase__					,     beam_prune_logp=UpperCamelCase__					,     token_min_logp=UpperCamelCase__					,     )
										__lowerCAmelCase       					=    decoded_processor_out.text

										__lowerCAmelCase       					=    list(UpperCamelCase__			)

										with get_context("fork"			).Pool() as pool:
													__lowerCAmelCase       					=    decoder.decode_beams_batch(
													    UpperCamelCase__					,     UpperCamelCase__					,     beam_width=UpperCamelCase__					,     beam_prune_logp=UpperCamelCase__					,     token_min_logp=UpperCamelCase__					,     )

										__lowerCAmelCase       					=    [d[0][0] for d in decoded_decoder_out]
										__lowerCAmelCase       					=    [d[0][2] for d in decoded_decoder_out]
										__lowerCAmelCase       					=    [d[0][3] for d in decoded_decoder_out]

										self.assertListEqual(UpperCamelCase__					,     UpperCamelCase__			)
										self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]					,     UpperCamelCase__			)

										self.assertTrue(np.array_equal(UpperCamelCase__					,     decoded_processor_out.logit_score			)			)
										self.assertTrue(np.allclose([-20.0_54, -18.4_47]					,     UpperCamelCase__					,     atol=1e-3			)			)

										self.assertTrue(np.array_equal(UpperCamelCase__					,     decoded_processor_out.lm_score			)			)
										self.assertTrue(np.allclose([-15.5_54, -13.94_74]					,     UpperCamelCase__					,     atol=1e-3			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

										__lowerCAmelCase       					=    self._get_dummy_logits()

										__lowerCAmelCase       					=    2.0
										__lowerCAmelCase       					=    5.0
										__lowerCAmelCase       					=    -20.0
										__lowerCAmelCase       					=    True

										__lowerCAmelCase       					=    processor.batch_decode(
										    UpperCamelCase__					,     alpha=UpperCamelCase__					,     beta=UpperCamelCase__					,     unk_score_offset=UpperCamelCase__					,     lm_score_boundary=UpperCamelCase__					,     )
										__lowerCAmelCase       					=    decoded_processor_out.text

										__lowerCAmelCase       					=    list(UpperCamelCase__			)
										decoder.reset_params(
										    alpha=UpperCamelCase__					,     beta=UpperCamelCase__					,     unk_score_offset=UpperCamelCase__					,     lm_score_boundary=UpperCamelCase__					,     )

										with get_context("fork"			).Pool() as pool:
													__lowerCAmelCase       					=    decoder.decode_beams_batch(
													    UpperCamelCase__					,     UpperCamelCase__					,     )

										__lowerCAmelCase       					=    [d[0][0] for d in decoded_decoder_out]

										self.assertListEqual(UpperCamelCase__					,     UpperCamelCase__			)
										self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]					,     UpperCamelCase__			)
										__lowerCAmelCase       					=    processor.decoder.model_container[processor.decoder._model_key]
										self.assertEqual(lm_model.alpha					,     2.0			)
										self.assertEqual(lm_model.beta					,     5.0			)
										self.assertEqual(lm_model.unk_score_offset					,     -20.0			)
										self.assertEqual(lm_model.score_boundary					,     UpperCamelCase__			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)

										__lowerCAmelCase       					=    processor.decoder.model_container[processor.decoder._model_key]
										__lowerCAmelCase       					=    Path(language_model._kenlm_model.path.decode("utf-8"			)			).parent.parent.absolute()

										__lowerCAmelCase       					=    os.listdir(UpperCamelCase__			)
										__lowerCAmelCase       					=    ['''alphabet.json''', '''language_model''']

										downloaded_decoder_files.sort()
										expected_decoder_files.sort()

										# test that only decoder relevant files from
										# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
										# are downloaded and none of the rest (e.g. README.md, ...)
										self.assertListEqual(UpperCamelCase__					,     UpperCamelCase__			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    snapshot_download("hf-internal-testing/processor_with_lm"			)

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__			)

										__lowerCAmelCase       					=    processor.decoder.model_container[processor.decoder._model_key]
										__lowerCAmelCase       					=    Path(language_model._kenlm_model.path.decode("utf-8"			)			).parent.parent.absolute()

										__lowerCAmelCase       					=    os.listdir(UpperCamelCase__			)
										__lowerCAmelCase       					=    os.listdir(UpperCamelCase__			)

										local_decoder_files.sort()
										expected_decoder_files.sort()

										# test that both decoder form hub and local files in cache are the same
										self.assertListEqual(UpperCamelCase__					,     UpperCamelCase__			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
										__lowerCAmelCase       					=    AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"			)

										__lowerCAmelCase       					=    floats_list((3, 10_00)			)

										__lowerCAmelCase       					=    processor_wavaveca(UpperCamelCase__					,     return_tensors="np"			)
										__lowerCAmelCase       					=    processor_auto(UpperCamelCase__					,     return_tensors="np"			)

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

										__lowerCAmelCase       					=    self._get_dummy_logits()

										__lowerCAmelCase       					=    processor_wavaveca.batch_decode(UpperCamelCase__			)
										__lowerCAmelCase       					=    processor_auto.batch_decode(UpperCamelCase__			)

										self.assertListEqual(decoded_wavaveca.text					,     decoded_auto.text			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_feature_extractor()
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_decoder()

										__lowerCAmelCase       					=    WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__					,     feature_extractor=UpperCamelCase__					,     decoder=UpperCamelCase__			)

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

							@staticmethod
							def     snake_case       (  __a					,     __a			):
										__lowerCAmelCase       					=    [d[key] for d in offsets]
										return retrieved_list

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
										__lowerCAmelCase       					=    self._get_dummy_logits()[0]

										__lowerCAmelCase       					=    processor.decode(UpperCamelCase__					,     output_word_offsets=UpperCamelCase__			)
										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys()			)					,     4			)
										self.assertTrue("text" in outputs			)
										self.assertTrue("word_offsets" in outputs			)
										self.assertTrue(isinstance(UpperCamelCase__					,     UpperCamelCase__			)			)

										self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]					,     "word"			)			)					,     outputs.text			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]					,     "word"			)					,     ["<s>", "<s>", "</s>"]			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]					,     "start_offset"			)					,     [0, 2, 4]			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]					,     "end_offset"			)					,     [1, 3, 5]			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
										__lowerCAmelCase       					=    self._get_dummy_logits()

										__lowerCAmelCase       					=    processor.batch_decode(UpperCamelCase__					,     output_word_offsets=UpperCamelCase__			)

										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys()			)					,     4			)
										self.assertTrue("text" in outputs			)
										self.assertTrue("word_offsets" in outputs			)
										self.assertTrue(isinstance(UpperCamelCase__					,     UpperCamelCase__			)			)

										self.assertListEqual(
										    [" ".join(self.get_from_offsets(UpperCamelCase__					,     "word"			)			) for o in outputs["word_offsets"]]					,     outputs.text			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]					,     "word"			)					,     ["<s>", "<s>", "</s>"]			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]					,     "start_offset"			)					,     [0, 2, 4]			)
										self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]					,     "end_offset"			)					,     [1, 3, 5]			)



							@slow
							@require_torch
							@require_torchaudio
							def     snake_case       (  self			):
										import torch

										__lowerCAmelCase       					=    load_dataset("common_voice"					,     "en"					,     split="train"					,     streaming=UpperCamelCase__			)
										__lowerCAmelCase       					=    ds.cast_column("audio"					,     datasets.Audio(sampling_rate=1_60_00			)			)
										__lowerCAmelCase       					=    iter(UpperCamelCase__			)
										__lowerCAmelCase       					=    next(UpperCamelCase__			)

										__lowerCAmelCase       					=    AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"			)
										__lowerCAmelCase       					=    WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"			)

										# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
										__lowerCAmelCase       					=    processor(sample["audio"]["array"]					,     return_tensors="pt"			).input_values

										with torch.no_grad():
													__lowerCAmelCase       					=    model(UpperCamelCase__			).logits.cpu().numpy()

										__lowerCAmelCase       					=    processor.decode(logits[0]					,     output_word_offsets=UpperCamelCase__			)

										__lowerCAmelCase       					=    model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
										__lowerCAmelCase       					=    [
										    {
										        '''start_time''': d['''start_offset'''] * time_offset,
										        '''end_time''': d['''end_offset'''] * time_offset,
										        '''word''': d['''word'''],
										    }
										    for d in output['''word_offsets''']
										]

										__lowerCAmelCase       					=    '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

										# output words
										self.assertEqual(" ".join(self.get_from_offsets(UpperCamelCase__					,     "word"			)			)					,     UpperCamelCase__			)
										self.assertEqual(" ".join(self.get_from_offsets(UpperCamelCase__					,     "word"			)			)					,     output.text			)

										# output times
										__lowerCAmelCase       					=    torch.tensor(self.get_from_offsets(UpperCamelCase__					,     "start_time"			)			)
										__lowerCAmelCase       					=    torch.tensor(self.get_from_offsets(UpperCamelCase__					,     "end_time"			)			)

										# fmt: off
										__lowerCAmelCase       					=    torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]			)
										__lowerCAmelCase       					=    torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]			)
										# fmt: on

										self.assertTrue(torch.allclose(UpperCamelCase__					,     UpperCamelCase__					,     atol=0.0_1			)			)
										self.assertTrue(torch.allclose(UpperCamelCase__					,     UpperCamelCase__					,     atol=0.0_1			)			)








"""simple docstring"""


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

import requests
from bsa import BeautifulSoup

A  :     Union[str, Any]    					=    {
    "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 _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




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

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

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

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

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"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(_UpperCamelCase   )
						__lowerCAmelCase       					=    f"query_{query.replace(' '					,				'_'   )}"
						if not os.path.exists(_UpperCamelCase   ):
									os.makedirs(_UpperCamelCase   )
						urllib.request.urlretrieve(  # noqa: S310
						    _UpperCamelCase					,				f"{path_name}/original_size_img_{index}.jpg"   )
			return index


if __name__ == "__main__":
	try:
		A  :     Any    					=    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




