



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

import requests
from bsa import BeautifulSoup

UpperCAmelCase_		 =					{
    """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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str = "dhaka"	,   _snake_case	:int = 5				)  ->     int:
   _A      =       min(_snake_case	,   50				)  # Prevent abuse!
   _A      =       {
       '''q''': query,
       '''tbm''': '''isch''',
       '''hl''': '''en''',
       '''ijn''': '''0''',
   }

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

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

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

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

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


if __name__ == "__main__":
   try:
      UpperCAmelCase_		 =					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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, TensorType

_A  =	logging.get_logger(__name__)

_A  =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

							super().__init__(tie_word_embeddings=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							    ]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



