


"""simple docstring"""


from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase__  :     str    					=    logging.get_logger(__name__)
@add_end_docstrings(lowercase_	)
class 						_UpperCamelCase						(     lowercase_	):
							'''simple docstring'''







							def __init__(  self					,     **__a			):
										super().__init__(**UpperCamelCase__			)

										if self.framework != "pt":
													raise ValueError(f"The {self.__class__} is only available in PyTorch."			)
										# No specific FOR_XXX available yet

							def __call__(  self					,     __a					,     **__a			):
										return super().__call__(UpperCamelCase__					,     **UpperCamelCase__			)

							def     snake_case       (  self					,     **__a			):
										__lowerCAmelCase       					=    {}
										if "candidate_labels" in kwargs:
													__lowerCAmelCase       					=    kwargs["candidate_labels"]
										if "hypothesis_template" in kwargs:
													__lowerCAmelCase       					=    kwargs["hypothesis_template"]

										return preprocess_params, {}, {}

							def     snake_case       (  self					,     __a					,     __a=None					,     __a="This is a sound of {}."			):







										if isinstance(UpperCamelCase__					,     UpperCamelCase__			):
													if audio.startswith("http://"			) or audio.startswith("https://"			):
																# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																# like http_huggingface_co.png
																__lowerCAmelCase       					=    requests.get(UpperCamelCase__			).content
													else:
																with open(UpperCamelCase__					,     "rb"			) as f:
																			__lowerCAmelCase       					=    f.read()

										if isinstance(UpperCamelCase__					,     UpperCamelCase__			):
													__lowerCAmelCase       					=    ffmpeg_read(UpperCamelCase__					,     self.feature_extractor.sampling_rate			)

										if not isinstance(UpperCamelCase__					,     np.ndarray			):
													raise ValueError("We expect a numpy ndarray as input"			)
										if len(audio.shape			) != 1:
													raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"			)

										__lowerCAmelCase       					=    self.feature_extractor(
										    [audio]					,     sampling_rate=self.feature_extractor.sampling_rate					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    candidate_labels
										__lowerCAmelCase       					=    [hypothesis_template.format(UpperCamelCase__			) for x in candidate_labels]
										__lowerCAmelCase       					=    self.tokenizer(UpperCamelCase__					,     return_tensors=self.framework					,     padding=UpperCamelCase__			)
										__lowerCAmelCase       					=    [text_inputs]
										return inputs

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    model_inputs.pop("candidate_labels"			)
										__lowerCAmelCase       					=    model_inputs.pop("text_inputs"			)
										if isinstance(text_inputs[0]					,     UpperCamelCase__			):
													__lowerCAmelCase       					=    text_inputs[0]
										else:
													# Batching case.
													__lowerCAmelCase       					=    text_inputs[0][0]

										__lowerCAmelCase       					=    self.model(**UpperCamelCase__					,     **UpperCamelCase__			)

										__lowerCAmelCase       					=    {
										    "candidate_labels": candidate_labels,
										    "logits": outputs.logits_per_audio,
										}
										return model_outputs



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    model_outputs.pop("candidate_labels"			)
										__lowerCAmelCase       					=    model_outputs["logits"][0]

										if self.framework == "pt":
													__lowerCAmelCase       					=    logits.softmax(dim=0			)
													__lowerCAmelCase       					=    probs.tolist()
										else:
													raise ValueError("`tf` framework not supported."			)

										__lowerCAmelCase       					=    [
										    {"score": score, "label": candidate_label}
										    for score, candidate_label in sorted(zip(UpperCamelCase__					,     UpperCamelCase__			)					,     key=lambda __a			: -x[0]			)
										]
										return result








"""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




