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

import requests
from bsa import BeautifulSoup

__lowerCAmelCase			:     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 a__    (       A_ = "dhaka",     A_ = 5					):






  '''simple docstring'''





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

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

  __magic_name__				     =       json.dumps(A_					)
  __magic_name__				     =       json.loads(A_					)

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

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

  __magic_name__				     =       re.findall(
      R"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]""",     A_,     )
  for index, fixed_full_res_image in enumerate(A_					):
    if index >= max_images:
      return index
    __magic_name__				     =       bytes(A_,     """ascii"""					).decode(
        """unicode-escape"""					)
    __magic_name__				     =       bytes(A_,     """ascii"""					).decode(
        """unicode-escape"""					)
    __magic_name__				     =       urllib.request.build_opener()
    __magic_name__				     =       [
        (
            "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_					)
    __magic_name__				     =       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:
        __lowerCAmelCase			:     List[str]           =   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





"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.distilbert.modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertModel,
  )







class _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						,	)	->		str:

       '''simple docstring'''




       snake_case    :      List[str]      					=    parent
       snake_case    :      int      					=    13
       snake_case    :      Optional[int]      					=    7
       snake_case    :      Tuple      					=    True
       snake_case    :      Optional[Any]      					=    True
       snake_case    :      Optional[Any]      					=    False
       snake_case    :      Optional[Any]      					=    True
       snake_case    :      List[Any]      					=    99
       snake_case    :      Union[str, Any]      					=    32
       snake_case    :      Union[str, Any]      					=    2
       snake_case    :      Union[str, Any]      					=    4
       snake_case    :      Optional[Any]      					=    37
       snake_case    :      str      					=    "gelu"
       snake_case    :      int      					=    0.1
       snake_case    :      Any      					=    0.1
       snake_case    :      Any      					=    512
       snake_case    :      Union[str, Any]      					=    16
       snake_case    :      List[str]      					=    2
       snake_case    :      int      					=    0.02
       snake_case    :      str      					=    3
       snake_case    :      Tuple      					=    4
       snake_case    :      Optional[Any]      					=    None







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      List[str]      					=    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

       snake_case    :      Optional[Any]      					=    None
       if self.use_input_mask:
           snake_case    :      Dict      					=    random_attention_mask([self.batch_size, self.seq_length]						)

       snake_case    :      List[Any]      					=    None
       snake_case    :      int      					=    None
       snake_case    :      Tuple      					=    None
       if self.use_labels:
           snake_case    :      str      					=    ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)
           snake_case    :      int      					=    ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels						)
           snake_case    :      List[Any]      					=    ids_tensor([self.batch_size]						,	self.num_choices						)

       snake_case    :      str      					=    DistilBertConfig(
           vocab_size=self.vocab_size						,	dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	hidden_dim=self.intermediate_size						,	hidden_act=self.hidden_act						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    TFDistilBertModel(config=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}

       snake_case    :      Union[str, Any]      					=    model(UpperCamelCase__						)

       snake_case    :      Tuple      					=    [input_ids, input_mask]

       snake_case    :      Optional[Any]      					=    model(UpperCamelCase__						)

       self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Tuple      					=    TFDistilBertForMaskedLM(config=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[str]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    TFDistilBertForQuestionAnswering(config=UpperCamelCase__						)
       snake_case    :      Optional[Any]      					=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
       }
       snake_case    :      str      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    self.num_labels
       snake_case    :      List[str]      					=    TFDistilBertForSequenceClassification(UpperCamelCase__						)
       snake_case    :      List[str]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[str]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.num_choices
       snake_case    :      Any      					=    TFDistilBertForMultipleChoice(UpperCamelCase__						)
       snake_case    :      List[str]      					=    tf.tile(tf.expand_dims(UpperCamelCase__						,	1						)						,	(1, self.num_choices, 1)						)
       snake_case    :      Any      					=    tf.tile(tf.expand_dims(UpperCamelCase__						,	1						)						,	(1, self.num_choices, 1)						)
       snake_case    :      Optional[Any]      					=    {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
       }
       snake_case    :      int      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      str      					=    self.num_labels
       snake_case    :      Tuple      					=    TFDistilBertForTokenClassification(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[Any]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)						)







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    self.prepare_config_and_inputs()
       ((snake_case)					,(snake_case)					,(snake_case)					,(snake_case)					,(snake_case)					,(snake_case))    :      Union[str, Any]      					=    config_and_inputs
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_tf
class _lowerCAmelCase				(      snake_case_    ,							snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       List[str]							  =  (
       (
           TFDistilBertModel,
           TFDistilBertForMaskedLM,
           TFDistilBertForQuestionAnswering,
           TFDistilBertForSequenceClassification,
           TFDistilBertForTokenClassification,
           TFDistilBertForMultipleChoice,
       )
       if is_tf_available()
       else None
   )
   __UpperCAmelCase :       Optional[int]							  =  (
       {
           '''feature-extraction''': TFDistilBertModel,
           '''fill-mask''': TFDistilBertForMaskedLM,
           '''question-answering''': TFDistilBertForQuestionAnswering,
           '''text-classification''': TFDistilBertForSequenceClassification,
           '''token-classification''': TFDistilBertForTokenClassification,
           '''zero-shot''': TFDistilBertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase :       Optional[int]							  =  False
   __UpperCAmelCase :       Optional[Any]							  =  False







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      int      					=    TFDistilBertModelTester(self						)
       snake_case    :      str      					=    ConfigTester(self						,	config_class=UpperCamelCase__						,	dim=37						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      str      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]						):
           snake_case    :      Optional[int]      					=    TFDistilBertModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







@require_tf
class _lowerCAmelCase				(      unittest.TestCase	):







   @slow
   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      str      					=    TFDistilBertModel.from_pretrained("distilbert-base-uncased"						)
       snake_case    :      Optional[int]      					=    tf.constant([[0, 1, 2, 3, 4, 5]]						)
       snake_case    :      Any      					=    model(UpperCamelCase__						)[0]

       snake_case    :      Dict      					=    [1, 6, 768]
       self.assertEqual(output.shape						,	UpperCamelCase__						)

       snake_case    :      str      					=    tf.constant(
           [
               [
                   [0.19261885, -0.13732955, 0.4119799],
                   [0.22150156, -0.07422661, 0.39037204],
                   [0.22756018, -0.0896414, 0.3701467],
               ]
           ]						)
       tf.debugging.assert_near(output[:, :3, :3]						,	UpperCamelCase__						,	atol=1e-4						)





