
"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=13  ,    _lowerCamelCase=7  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=99  ,    _lowerCamelCase=32  ,    _lowerCamelCase=5  ,    _lowerCamelCase=4  ,    _lowerCamelCase=37  ,    _lowerCamelCase="gelu"  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=512  ,    _lowerCamelCase=16  ,    _lowerCamelCase=2  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=3  ,    _lowerCamelCase=4  ,    _lowerCamelCase=None  ,    )			->							Optional[int]:




										'''simple docstring'''
										__lowercase       =       parent
										__lowercase       =       batch_size
										__lowercase       =       seq_length
										__lowercase       =       is_training
										__lowercase       =       use_token_type_ids
										__lowercase       =       use_labels
										__lowercase       =       vocab_size
										__lowercase       =       hidden_size
										__lowercase       =       num_hidden_layers
										__lowercase       =       num_attention_heads
										__lowercase       =       intermediate_size
										__lowercase       =       hidden_act
										__lowercase       =       hidden_dropout_prob
										__lowercase       =       attention_probs_dropout_prob
										__lowercase       =       max_position_embeddings
										__lowercase       =       type_vocab_size
										__lowercase       =       type_sequence_label_size
										__lowercase       =       initializer_range
										__lowercase       =       num_labels
										__lowercase       =       num_choices
										__lowercase       =       scope
										__lowercase       =       self.vocab_size - 1
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[int]:




										'''simple docstring'''
										__lowercase       =       ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size       )

										__lowercase       =       None
										if self.use_token_type_ids:
													__lowercase       =       ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size       )

										__lowercase       =       None
										__lowercase       =       None
										__lowercase       =       None
										if self.use_labels:
													__lowercase       =       ids_tensor([self.batch_size]  ,    self.type_sequence_label_size       )
													__lowercase       =       ids_tensor([self.batch_size, self.seq_length]  ,    self.num_labels       )
													__lowercase       =       ids_tensor([self.batch_size]  ,    self.num_choices       )

										__lowercase       =       OpenAIGPTConfig(
										    vocab_size=self.vocab_size  ,    n_embd=self.hidden_size  ,    n_layer=self.num_hidden_layers  ,    n_head=self.num_attention_heads  ,    n_positions=self.max_position_embeddings  ,    pad_token_id=self.pad_token_id  ,    )

										__lowercase       =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,    2       )

										return (
										    config,
										    input_ids,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    *_lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       OpenAIGPTModel(config=_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()

										__lowercase       =       model(_lowerCamelCase  ,    token_type_ids=_lowerCamelCase  ,    head_mask=_lowerCamelCase       )
										__lowercase       =       model(_lowerCamelCase  ,    token_type_ids=_lowerCamelCase       )
										__lowercase       =       model(_lowerCamelCase       )

										self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    *_lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       OpenAIGPTLMHeadModel(_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()

										__lowercase       =       model(_lowerCamelCase  ,    token_type_ids=_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.loss.shape  ,    ()       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    *_lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       OpenAIGPTDoubleHeadsModel(_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()

										__lowercase       =       model(_lowerCamelCase  ,    token_type_ids=_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.loss.shape  ,    ()       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    *_lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.num_labels
										__lowercase       =       OpenAIGPTForSequenceClassification(_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()

										__lowercase       =       ids_tensor([self.batch_size]  ,    self.type_sequence_label_size       )
										__lowercase       =       model(_lowerCamelCase  ,    token_type_ids=_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)       )





							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       self.prepare_config_and_inputs()
										(
										    (
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					(
										    __lowercase
										)   ,					
										)       =       config_and_inputs
										__lowercase       =       {
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "head_mask": head_mask,
										}

										return config, inputs_dict





@require_torch
class 					__a     ( __a					,   __a					,   __a					,   unittest.TestCase       ):



							'''simple docstring'''







							_lowerCamelCase					:			Optional[int]          =   (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase					:			Tuple          =   (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							_lowerCamelCase					:			Any          =   (
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
													# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
													# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
													# tiny config could not be created.
													return True

										return False
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase=False       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       super()._prepare_for_class(_lowerCamelCase  ,    _lowerCamelCase  ,    return_labels=_lowerCamelCase       )

										if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																__lowercase       =       torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,    dtype=torch.long  ,    device=_lowerCamelCase  ,    )
																__lowercase       =       inputs_dict["labels"]
																__lowercase       =       inputs_dict["labels"]
																__lowercase       =       torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.num_choices)  ,    dtype=torch.long  ,    device=_lowerCamelCase  ,    )
																__lowercase       =       torch.zeros(
																    self.model_tester.batch_size  ,    dtype=torch.long  ,    device=_lowerCamelCase       )
										return inputs_dict
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       OpenAIGPTModelTester(self       )
										__lowercase       =       ConfigTester(self  ,    config_class=_lowerCamelCase  ,    n_embd=37       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										self.config_tester.run_common_tests()
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowerCamelCase       )





							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowercase       =       OpenAIGPTModel.from_pretrained(_lowerCamelCase       )
													self.assertIsNotNone(_lowerCamelCase       )





@require_torch
class 					__a     ( unittest.TestCase       ):



							'''simple docstring'''







							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__lowercase       =       OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"       )
										model.to(_lowerCamelCase       )
										__lowercase       =       torch.tensor([[481, 4_735, 544]]  ,    dtype=torch.long  ,    device=_lowerCamelCase       )  # the president is
										__lowercase       =       [
										    481,
										    4_735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    40_477,
										    244,
										    249,
										    719,
										    881,
										    487,
										    544,
										    240,
										    244,
										    603,
										    481,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										__lowercase       =       model.generate(_lowerCamelCase  ,    do_sample=_lowerCamelCase       )
										self.assertListEqual(output_ids[0].tolist()  ,    _lowerCamelCase       )







"""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      lowerCAmelCase__			(		__magic_name__ = "dhaka"			,   __magic_name__ = 5      )						->int:
			__lowercase       =       min(__magic_name__			,   5_0      )  # Prevent abuse!
			__lowercase       =       {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

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

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

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

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





