

'''simple docstring'''


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

import requests
from bsa import BeautifulSoup

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"
}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase = "dhaka"   ,UpperCamelCase = 5  )    ->    int:
	UpperCAmelCase_		:      Tuple       	=					min(UpperCamelCase   ,5_0  )  # Prevent abuse!
	UpperCAmelCase_		:      List[Any]       	=					{
	    'q': query,
	    'tbm': 'isch',
	    'hl': 'en',
	    'ijn': '0',
	}

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

	UpperCAmelCase_		:      Optional[Any]       	=					json.dumps(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					json.loads(UpperCamelCase  )

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

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

	UpperCAmelCase_		:      List[Any]       	=					re.findall(
	    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'   ,UpperCamelCase   ,)
	for index, fixed_full_res_image in enumerate(UpperCamelCase  ):
		if index >= max_images:
			return index
		UpperCAmelCase_		:      Dict       	=					bytes(UpperCamelCase   ,'ascii'  ).decode(
		    'unicode-escape'  )
		UpperCAmelCase_		:      Dict       	=					bytes(UpperCamelCase   ,'ascii'  ).decode(
		    'unicode-escape'  )
		UpperCAmelCase_		:      List[Any]       	=					urllib.request.build_opener()
		UpperCAmelCase_		:      List[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',
		    )
		]
		urllib.request.install_opener(UpperCamelCase  )
		UpperCAmelCase_		:      int       	=					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:
												lowerCAmelCase__										= 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'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
						import torch

						from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=99    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=4    ,			_snake_case=None    ,			)							->				str:
								UpperCAmelCase_		:      Dict       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      Dict       	=					is_training
								UpperCAmelCase_		:      Tuple       	=					use_input_mask
								UpperCAmelCase_		:      List[Any]       	=					use_token_type_ids
								UpperCAmelCase_		:      Optional[int]       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      Any       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      Optional[int]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      int       	=					type_sequence_label_size
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					num_labels
								UpperCAmelCase_		:      Optional[Any]       	=					num_choices
								UpperCAmelCase_		:      str       	=					scope


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

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

								UpperCAmelCase_		:      Union[str, Any]       	=					None
								if self.use_token_type_ids:
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

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

								UpperCAmelCase_		:      Tuple       	=					self.get_config()

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


							def 						_snake_case							(			self)							->				Optional[Any]:
								return OpenLlamaConfig(
								    vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_snake_case    ,			initializer_range=self.initializer_range    ,			use_stable_embedding=_snake_case    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					OpenLlamaModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Dict       	=					model(_snake_case    ,			attention_mask=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					True
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaModel(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			)
								UpperCAmelCase_		:      Any       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					True
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      int       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()

								# first forward pass
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      Dict       	=					outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase_		:      str       	=					ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
								UpperCAmelCase_		:      int       	=					ids_tensor((self.batch_size, 3)    ,			vocab_size=2)

								# append to next input_ids and
								UpperCAmelCase_		:      Tuple       	=					torch.cat([input_ids, next_tokens]    ,			dim=-1)
								UpperCAmelCase_		:      List[str]       	=					torch.cat([input_mask, next_mask]    ,			dim=-1)

								UpperCAmelCase_		:      Optional[Any]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]

								# select random slice
								UpperCAmelCase_		:      Optional[Any]       	=					ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
								UpperCAmelCase_		:      Optional[int]       	=					output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase_		:      Union[str, Any]       	=					output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-3))





							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    
								)		:      Union[str, Any]       	=					config_and_inputs
								UpperCAmelCase_		:      List[str]       	=					{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Dict=   (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							_lowerCamelCase		:      str=   (OpenLlamaForCausalLM,) if is_torch_available() else ()
							_lowerCamelCase		:      Optional[int]=   (
							    {
							        "feature-extraction": OpenLlamaModel,
							        "text-classification": OpenLlamaForSequenceClassification,
							        "text-generation": OpenLlamaForCausalLM,
							        "zero-shot": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase		:      Tuple=   False
							_lowerCamelCase		:      int=   False


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Any       	=					OpenLlamaModelTester(self)
								UpperCAmelCase_		:      Union[str, Any]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				Optional[Any]:
								self.config_tester.run_common_tests()


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_		:      List[Any]       	=					type
									self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[str]       	=					3
								UpperCAmelCase_		:      List[str]       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Optional[Any]       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Optional[Any]       	=					3
								UpperCAmelCase_		:      int       	=					'single_label_classification'
								UpperCAmelCase_		:      int       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Any       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      int       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      int       	=					3
								UpperCAmelCase_		:      Any       	=					'multi_label_classification'
								UpperCAmelCase_		:      Tuple       	=					input_dict['input_ids']
								UpperCAmelCase_		:      str       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]    ,			self.model_tester.type_sequence_label_size).to(torch.float)
								UpperCAmelCase_		:      Optional[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[Any]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass





							@parameterized.expand([('linear',), ('dynamic',)])
							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[Any]       	=					ids_tensor([1, 10]    ,			config.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					ids_tensor([1, int(config.max_position_embeddings * 1.5)]    ,			config.vocab_size)

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      int       	=					OpenLlamaModel(_snake_case)
								original_model.to(_snake_case)
								original_model.eval()
								UpperCAmelCase_		:      Optional[Any]       	=					original_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Tuple       	=					original_model(_snake_case).last_hidden_state

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      Dict       	=					{'type': scaling_type, 'factor': 10.0}
								UpperCAmelCase_		:      Union[str, Any]       	=					OpenLlamaModel(_snake_case)
								scaled_model.to(_snake_case)
								scaled_model.eval()
								UpperCAmelCase_		:      Dict       	=					scaled_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Optional[int]       	=					scaled_model(_snake_case).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
									self.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))
								else:
									self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))






