


"""simple docstring"""



import unittest

from transformers import AutoTokenizer, FalconConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
  )







class _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__=3						,	UpperCamelCase__=7						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=99						,	UpperCamelCase__=32						,	UpperCamelCase__=5						,	UpperCamelCase__=4						,	UpperCamelCase__=37						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=16						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=3						,	UpperCamelCase__=4						,	UpperCamelCase__=None						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      List[str]      					=    parent
       snake_case    :      List[Any]      					=    batch_size
       snake_case    :      Any      					=    seq_length
       snake_case    :      List[Any]      					=    is_training
       snake_case    :      Dict      					=    use_input_mask
       snake_case    :      Optional[Any]      					=    use_token_type_ids
       snake_case    :      Tuple      					=    use_labels
       snake_case    :      int      					=    vocab_size
       snake_case    :      Optional[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Tuple      					=    num_attention_heads
       snake_case    :      Tuple      					=    intermediate_size
       snake_case    :      Optional[int]      					=    hidden_act
       snake_case    :      Tuple      					=    hidden_dropout_prob
       snake_case    :      str      					=    attention_probs_dropout_prob
       snake_case    :      Optional[Any]      					=    max_position_embeddings
       snake_case    :      Union[str, Any]      					=    type_vocab_size
       snake_case    :      str      					=    type_sequence_label_size
       snake_case    :      Any      					=    initializer_range
       snake_case    :      List[str]      					=    num_labels
       snake_case    :      Dict      					=    num_choices
       snake_case    :      str      					=    scope







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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

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

       snake_case    :      str      					=    None

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

       snake_case    :      List[Any]      					=    self.get_config()

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







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

       '''simple docstring'''




       return FalconConfig(
           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=UpperCamelCase__						,	initializer_range=self.initializer_range						,	pad_token_id=1						,	new_decoder_architecture=UpperCamelCase__						,	)







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

       '''simple docstring'''




       snake_case    :      Any      					=    FalconModel(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Optional[Any]      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						)
       snake_case    :      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__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    True
       snake_case    :      Optional[int]      					=    FalconModel(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Tuple      					=    model(
           UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	encoder_attention_mask=UpperCamelCase__						,	)
       snake_case    :      Tuple      					=    model(
           UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	)
       snake_case    :      List[Any]      					=    model(UpperCamelCase__						,	attention_mask=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__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      str      					=    FalconForCausalLM(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Any      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	labels=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__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		Dict:

       '''simple docstring'''




       snake_case    :      Dict      					=    True
       snake_case    :      List[Any]      					=    True
       snake_case    :      str      					=    FalconForCausalLM(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()

       # first forward pass
       snake_case    :      str      					=    model(
           UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	encoder_attention_mask=UpperCamelCase__						,	use_cache=UpperCamelCase__						,	)
       snake_case    :      Union[str, Any]      					=    outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       snake_case    :      Any      					=    ids_tensor((self.batch_size, 3)						,	config.vocab_size						)
       snake_case    :      Optional[int]      					=    ids_tensor((self.batch_size, 3)						,	vocab_size=2						)

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

       snake_case    :      Tuple      					=    model(
           UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	encoder_attention_mask=UpperCamelCase__						,	output_hidden_states=UpperCamelCase__						,	)["hidden_states"][0]
       snake_case    :      Dict      					=    model(
           UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	encoder_attention_mask=UpperCamelCase__						,	past_key_values=UpperCamelCase__						,	output_hidden_states=UpperCamelCase__						,	)["hidden_states"][0]

       # select random slice
       snake_case    :      Any      					=    ids_tensor((1,)						,	output_from_past.shape[-1]						).item()
       snake_case    :      Any      					=    output_from_no_past[:, -3:, random_slice_idx].detach()
       snake_case    :      Optional[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(UpperCamelCase__						,	UpperCamelCase__						,	atol=1e-3						)						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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







@require_torch
class _lowerCAmelCase				(      snake_case_    ,							snake_case_    ,							snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       Optional[int]							  =  (
       (
           FalconModel,
           FalconForCausalLM,
           FalconForSequenceClassification,
           FalconForTokenClassification,
           FalconForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase :       Dict							  =  (FalconForCausalLM,) if is_torch_available() else ()
   __UpperCAmelCase :       Tuple							  =  (
       {
           '''feature-extraction''': FalconModel,
           '''text-classification''': FalconForSequenceClassification,
           '''text-generation''': FalconForCausalLM,
           '''question-answering''': FalconForQuestionAnswering,
           '''token-classification''': FalconForTokenClassification,
           '''zero-shot''': FalconForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase :       Optional[int]							  =  False
   __UpperCAmelCase :       Union[str, Any]							  =  False







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    FalconModelTester(self						)
       snake_case    :      str      					=    ConfigTester(self						,	config_class=UpperCamelCase__						,	hidden_size=37						)







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

       '''simple docstring'''




       self.config_tester.run_common_tests()







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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case					,*snake_case    :      str      					=    self.model_tester.prepare_config_and_inputs()
       for alibi in [True, False]:
           snake_case    :      Optional[int]      					=    alibi
           self.model_tester.create_and_check_model(UpperCamelCase__						,	*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case					,snake_case    :      List[str]      					=    self.model_tester.prepare_config_and_inputs_for_common()
       snake_case    :      int      					=    3
       snake_case    :      Dict      					=    input_dict["input_ids"]
       snake_case    :      str      					=    input_ids.ne(1						).to(UpperCamelCase__						)
       snake_case    :      Optional[Any]      					=    ids_tensor([self.model_tester.batch_size]						,	self.model_tester.type_sequence_label_size						)
       snake_case    :      Any      					=    FalconForSequenceClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Dict      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	labels=UpperCamelCase__						)
       self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)						)







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

       '''simple docstring'''




       snake_case					,snake_case    :      str      					=    self.model_tester.prepare_config_and_inputs_for_common()
       snake_case    :      Tuple      					=    3
       snake_case    :      int      					=    "single_label_classification"
       snake_case    :      Optional[Any]      					=    input_dict["input_ids"]
       snake_case    :      Any      					=    input_ids.ne(1						).to(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    ids_tensor([self.model_tester.batch_size]						,	self.model_tester.type_sequence_label_size						)
       snake_case    :      Union[str, Any]      					=    FalconForSequenceClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Any      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	labels=UpperCamelCase__						)
       self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)						)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case					,snake_case    :      Tuple      					=    self.model_tester.prepare_config_and_inputs_for_common()
       snake_case    :      Union[str, Any]      					=    input_dict["input_ids"]
       snake_case    :      Optional[int]      					=    FalconForCausalLM(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      Optional[Any]      					=    model(UpperCamelCase__						,	use_cache=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    input_ids.shape[0]
       snake_case    :      Tuple      					=    model._convert_to_rw_cache(result.past_key_values						)
       snake_case    :      Any      					=    model._convert_cache_to_standard_format(UpperCamelCase__						,	UpperCamelCase__						)
       for layer in range(len(UpperCamelCase__						)						):
           for tensor_idx in range(2						):
               self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3						)
               self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4						)
               self.assertTrue(
                   torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]						)						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case					,snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs_for_common()
       snake_case    :      Any      					=    3
       snake_case    :      Tuple      					=    "multi_label_classification"
       snake_case    :      Any      					=    input_dict["input_ids"]
       snake_case    :      Tuple      					=    input_ids.ne(1						).to(UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    ids_tensor(
           [self.model_tester.batch_size, config.num_labels]						,	self.model_tester.type_sequence_label_size						).to(torch.float						)
       snake_case    :      Optional[Any]      					=    FalconForSequenceClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      str      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	labels=UpperCamelCase__						)
       self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)						)







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

       '''simple docstring'''




       for model_class in self.all_generative_model_classes:
           snake_case					,snake_case    :      List[Any]      					=    self.model_tester.prepare_config_and_inputs_for_common()

           # If it doesn't support cache, pass the test
           if not hasattr(UpperCamelCase__						,	"use_cache"						):
               return

           snake_case    :      Union[str, Any]      					=    model_class(UpperCamelCase__						).to(UpperCamelCase__						)
           if "use_cache" not in inputs:
               snake_case    :      List[Any]      					=    True
           snake_case    :      Union[str, Any]      					=    model(**UpperCamelCase__						)

           # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
           if "past_key_values" not in outputs:
               return

           snake_case    :      Tuple      					=    (
               getattr(UpperCamelCase__						,	"decoder_layers"						,	UpperCamelCase__						)
               or getattr(UpperCamelCase__						,	"num_decoder_layers"						,	UpperCamelCase__						)
               or config.num_hidden_layers
           )
           snake_case    :      List[Any]      					=    getattr(UpperCamelCase__						,	"num_kv_heads"						,	config.num_attention_heads						)
           snake_case    :      List[str]      					=    getattr(UpperCamelCase__						,	"d_model"						,	config.hidden_size						)
           snake_case    :      str      					=    embed_dim // num_attention_heads

           snake_case    :      int      					=    outputs["past_key_values"]
           self.assertEqual(len(UpperCamelCase__						)						,	UpperCamelCase__						)

           snake_case					,snake_case    :      Optional[int]      					=    inputs["input_ids"].shape
           for i in range(UpperCamelCase__						):
               if config.new_decoder_architecture:
                   snake_case    :      Dict      					=    config.num_attention_heads
               elif config.multi_query:
                   snake_case    :      Optional[Any]      					=    1
               self.assertEqual(len(past_kv[0]						)						,	2						)  # K V for the decoder = 2
               self.assertEqual(
                   past_kv[i][0].shape						,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)						)
               self.assertEqual(
                   past_kv[i][1].shape						,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)						)







@require_torch
class _lowerCAmelCase				(      unittest.TestCase	):







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

       '''simple docstring'''




       snake_case    :      Dict      					=    AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"						)
       snake_case    :      Any      					=    FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"						)
       model.eval()
       model.to(UpperCamelCase__						)
       snake_case    :      str      					=    tokenizer("My favorite food is"						,	return_tensors="pt"						).to(UpperCamelCase__						)

       snake_case    :      List[Any]      					=    (
           "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
       )

       snake_case    :      Optional[int]      					=    model.generate(**UpperCamelCase__						,	do_sample=UpperCamelCase__						,	max_new_tokens=19						)
       snake_case    :      List[Any]      					=    tokenizer.batch_decode(UpperCamelCase__						)[0]

       self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)







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

       '''simple docstring'''




       for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
           snake_case    :      List[str]      					=    AutoTokenizer.from_pretrained(UpperCamelCase__						)
           snake_case    :      int      					=    FalconForCausalLM.from_pretrained(UpperCamelCase__						)
           model.eval()
           model.to(UpperCamelCase__						)
           snake_case    :      Dict      					=    tokenizer("My favorite food is"						,	return_tensors="pt"						).to(UpperCamelCase__						)

           # We just test that these run without errors - the models are randomly initialized
           # and so the actual text outputs will be garbage
           model.generate(**UpperCamelCase__						,	do_sample=UpperCamelCase__						,	max_new_tokens=4						)
           model.generate(**UpperCamelCase__						,	do_sample=UpperCamelCase__						,	max_new_tokens=4						)
           model.generate(**UpperCamelCase__						,	num_beams=2						,	max_new_tokens=4						)







   @slow
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       with torch.no_grad():
           for repo in [
               "Rocketknight1/falcon-rw-1b",
               "Rocketknight1/tiny-random-falcon-7b",
               "Rocketknight1/tiny-random-falcon-40b",
           ]:
               snake_case    :      List[str]      					=    AutoTokenizer.from_pretrained(UpperCamelCase__						)
               snake_case    :      List[str]      					=    FalconForCausalLM.from_pretrained(UpperCamelCase__						)
               model.eval()
               model.to(device=UpperCamelCase__						)
               snake_case    :      str      					=    tokenizer("My favorite food is"						,	return_tensors="pt"						).to(UpperCamelCase__						)

               # Test results are the same with and without cache
               snake_case    :      int      					=    model.generate(**UpperCamelCase__						,	do_sample=UpperCamelCase__						,	max_new_tokens=20						,	use_cache=UpperCamelCase__						)
               snake_case    :      Dict      					=    model.generate(**UpperCamelCase__						,	do_sample=UpperCamelCase__						,	max_new_tokens=20						,	use_cache=UpperCamelCase__						)
               self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0						)









"""simple docstring"""



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

import requests
from bsa import BeautifulSoup

__snake_case   			=    {
    """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						(						lowercase  :  str = "dhaka"						,							lowercase  :  int = 5 )	->						int:


    """simple docstring"""





    snake_case    :      List[Any]      					=    min(lowercase						,							50 )  # Prevent abuse!
    snake_case    :      Optional[Any]      					=    {
        "q": query,
        "tbm": "isch",
        "hl": "en",
        "ijn": "0",
    }

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

    snake_case    :      Optional[Any]      					=    json.dumps(lowercase )
    snake_case    :      str      					=    json.loads(lowercase )

    snake_case    :      List[str]      					=    re.findall(
        R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","						,							lowercase						,							)
    if not matched_google_image_data:
        return 0

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

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


if __name__ == "__main__":
  try:
    __snake_case   			=    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





