


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


__lowercase     :						str	=     logging.get_logger(__name__)





class 							__UpperCamelCase    :




	def __init__(       self						,    __a = None						,    __a = None						,    __a=None						,    __a=None							):

						'''simple docstring'''





						if not conversation_id:
											__a      :	Optional[int]    =  uuid.uuida()
						if past_user_inputs is None:
											__a      :	Any    =  []
						if generated_responses is None:
											__a      :	List[str]    =  []

						__a      :	uuid.UUID    =  conversation_id
						__a      :	List[str]    =  past_user_inputs
						__a      :	List[str]    =  generated_responses
						__a      :	Optional[str]    =  text




	def __eq__(       self						,    __a							):

						'''simple docstring'''





						if not isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_							):
											return False
						if self.uuid == other.uuid:
											return True
						return (
						    self.new_user_input == other.new_user_input
						    and self.past_user_inputs == other.past_user_inputs
						    and self.generated_responses == other.generated_responses
						)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = False							):

						'''simple docstring'''





						if self.new_user_input:
											if overwrite:
																logger.warning(
																    f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
																    f"""with: \"{text}\"."""							)
																__a      :	Union[str, Any]    =  text
											else:
																logger.warning(
																    f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
																    f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""							)
						else:
											__a      :	str    =  text




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if self.new_user_input:
											self.past_user_inputs.append(self.new_user_input							)
						__a      :	Optional[int]    =  None




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						self.generated_responses.append(SCREAMING_SNAKE_CASE_							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for user_input, generated_response in zip(self.past_user_inputs						,    self.generated_responses							):
											yield True, user_input
											yield False, generated_response
						if self.new_user_input:
											yield True, self.new_user_input





	def __repr__(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  f"""Conversation id: {self.uuid} \n"""
						for is_user, text in self.iter_texts():
											__a      :	Dict    =  """user""" if is_user else """bot"""
											output += f"""{name} >> {text} \n"""
						return output





@add_end_docstrings(
    _UpperCAmelCase		,				r"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		,				)
class 							__UpperCamelCase    (		_UpperCAmelCase ):




	def __init__(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						super().__init__(*SCREAMING_SNAKE_CASE_						,    **SCREAMING_SNAKE_CASE_							)
						if self.tokenizer.pad_token_id is None:
											__a      :	str    =  self.tokenizer.eos_token




	def 				__UpperCAmelCase							(       self						,    __a=None						,    __a=None						,    __a=None						,    **__a							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  {}
						__a      :	Optional[Any]    =  {}
						__a      :	Tuple    =  {}

						if min_length_for_response is not None:
											__a      :	Union[str, Any]    =  min_length_for_response
						if minimum_tokens is not None:
											__a      :	Tuple    =  minimum_tokens

						if "max_length" in generate_kwargs:
											__a      :	Optional[int]    =  generate_kwargs["""max_length"""]
											# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
						if clean_up_tokenization_spaces is not None:
											__a      :	Optional[Any]    =  clean_up_tokenization_spaces

						if generate_kwargs:
											forward_params.update(SCREAMING_SNAKE_CASE_							)
						return preprocess_params, forward_params, postprocess_params




	def __call__(       self						,    __a						,    __a=0						,    **__a							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  super().__call__(SCREAMING_SNAKE_CASE_						,    num_workers=SCREAMING_SNAKE_CASE_						,    **SCREAMING_SNAKE_CASE_							)
						if isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_							) and len(SCREAMING_SNAKE_CASE_							) == 1:
											return outputs[0]
						return outputs




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=32							):

						'''simple docstring'''





						if not isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_							):
											raise ValueError('ConversationalPipeline, expects Conversation as inputs'							)
						if conversation.new_user_input is None:
											raise ValueError(
											    f"""Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. """
											    'Add user inputs with the conversation\'s `add_user_input` method'							)
						if hasattr(self.tokenizer						,    '_build_conversation_input_ids'							):
											__a      :	int    =  self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE_							)
						else:
											# If the tokenizer cannot handle conversations, we default to only the old version
											__a      :	Dict    =  self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE_							)

						if self.framework == "pt":
											__a      :	List[str]    =  torch.LongTensor([input_ids]							)
						elif self.framework == "tf":
											__a      :	Any    =  tf.constant([input_ids]							)
						return {"input_ids": input_ids, "conversation": conversation}




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=10						,    **__a							):

						'''simple docstring'''





						__a      :	Any    =  generate_kwargs.get('max_length'						,    self.model.config.max_length							)

						__a      :	Optional[Any]    =  model_inputs["""input_ids"""].shape[1]
						if max_length - minimum_tokens < n:
											logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""							)
											__a      :	Any    =  max_length - minimum_tokens
											__a      :	Union[str, Any]    =  model_inputs["""input_ids"""][:, -trim:]
											if "attention_mask" in model_inputs:
																__a      :	str    =  model_inputs["""attention_mask"""][:, -trim:]
						__a      :	Union[str, Any]    =  model_inputs.pop('conversation'							)
						__a      :	int    =  max_length
						__a      :	int    =  self.model.generate(**SCREAMING_SNAKE_CASE_						,    **SCREAMING_SNAKE_CASE_							)
						if self.model.config.is_encoder_decoder:
											__a      :	List[str]    =  1
						else:
											__a      :	Optional[int]    =  n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=True							):

						'''simple docstring'''





						__a      :	Dict    =  model_outputs["""output_ids"""]
						__a      :	Union[str, Any]    =  self.tokenizer.decode(
						    output_ids[0]						,    skip_special_tokens=SCREAMING_SNAKE_CASE_						,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_						,    )
						__a      :	Dict    =  model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(SCREAMING_SNAKE_CASE_							)
						return conversation





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Dict    =  self.tokenizer.eos_token_id
						__a      :	Union[str, Any]    =  []
						for is_user, text in conversation.iter_texts():
											if eos_token_id is not None:
																input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_						,    add_special_tokens=SCREAMING_SNAKE_CASE_							) + [eos_token_id]							)
											else:
																input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_						,    add_special_tokens=SCREAMING_SNAKE_CASE_							)							)

						if len(SCREAMING_SNAKE_CASE_							) > self.tokenizer.model_max_length:
											__a      :	Optional[int]    =  input_ids[-self.tokenizer.model_max_length :]
						return input_ids







'''simple docstring'''



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

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    '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							(_SCREAMING_SNAKE_CASE				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

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

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

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

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


if __name__ == "__main__":
	try:
		__lowercase     :						Optional[int]	=     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



