




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__							:	Optional[Any]											=		logging.get_logger(__name__)
class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	List[str]			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Optional[Any]=None		):






							"""simple docstring"""






							if not conversation_id:
								snake_case_						       =	uuid.uuida()
							if past_user_inputs is None:
								snake_case_						       =	[]
							if generated_responses is None:
								snake_case_						       =	[]

							snake_case_						       =	conversation_id
							snake_case_						       =	past_user_inputs
							snake_case_						       =	generated_responses
							snake_case_						       =	text






						def __eq__(		self				:	Optional[int]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							if not isinstance(A_			,      A_		):
								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 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	int			,      __lowercase				:	Any = 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}\"."		)
									snake_case_						       =	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:
								snake_case_						       =	text






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							if self.new_user_input:
								self.past_user_inputs.append(self.new_user_input		)
							snake_case_						       =	None






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							self.generated_responses.append(A_		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""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				:	Any		):






							"""simple docstring"""






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







@add_end_docstrings(
    snake_case_			,  r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''			,  )
class 						UpperCAmelCase  (							snake_case_     ):







						'''simple docstring'''





						def __init__(		self				:	Optional[Any]			,      *__lowercase				:	List[str]			,      **__lowercase				:	str		):






							"""simple docstring"""






							super().__init__(*A_			,      **A_		)
							if self.tokenizer.pad_token_id is None:
								snake_case_						       =	self.tokenizer.eos_token






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	int=None			,      __lowercase				:	List[str]=None			,      __lowercase				:	Dict=None			,      **__lowercase				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	{}
							snake_case_						       =	{}
							snake_case_						       =	{}

							if min_length_for_response is not None:
								snake_case_						       =	min_length_for_response
							if minimum_tokens is not None:
								snake_case_						       =	minimum_tokens

							if "max_length" in generate_kwargs:
								snake_case_						       =	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:
								snake_case_						       =	clean_up_tokenization_spaces

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






						def __call__(		self				:	Optional[Any]			,      __lowercase				:	str			,      __lowercase				:	str=0			,      **__lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	super().__call__(A_			,      num_workers=A_			,      **A_		)
							if isinstance(A_			,      A_		) and len(A_		) == 1:
								return outputs[0]
							return outputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	int			,      __lowercase				:	Optional[Any]=32		):






							"""simple docstring"""






							if not isinstance(A_			,      A_		):
								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"		):
								snake_case_						       =	self.tokenizer._build_conversation_input_ids(A_		)
							else:
								# If the tokenizer cannot handle conversations, we default to only the old version
								snake_case_						       =	self._legacy_parse_and_tokenize(A_		)

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






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	int			,      __lowercase				:	Tuple=10			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	generate_kwargs.get("max_length"			,      self.model.config.max_length		)

							snake_case_						       =	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})"		)
								snake_case_						       =	max_length - minimum_tokens
								snake_case_						       =	model_inputs["input_ids"][:, -trim:]
								if "attention_mask" in model_inputs:
									snake_case_						       =	model_inputs["attention_mask"][:, -trim:]
							snake_case_						       =	model_inputs.pop("conversation"		)
							snake_case_						       =	max_length
							snake_case_						       =	self.model.generate(**A_			,      **A_		)
							if self.model.config.is_encoder_decoder:
								snake_case_						       =	1
							else:
								snake_case_						       =	n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Tuple=True		):






							"""simple docstring"""






							snake_case_						       =	model_outputs["output_ids"]
							snake_case_						       =	self.tokenizer.decode(
							    output_ids[0]			,      skip_special_tokens=A_			,      clean_up_tokenization_spaces=A_			,      )
							snake_case_						       =	model_outputs["conversation"]
							conversation.mark_processed()
							conversation.append_response(A_		)
							return conversation






						def 			snake_case__		(		self				:	str			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.tokenizer.eos_token_id
							snake_case_						       =	[]
							for is_user, text in conversation.iter_texts():
								if eos_token_id is not None:
									input_ids.extend(self.tokenizer.encode(A_			,      add_special_tokens=A_		) + [eos_token_id]		)
								else:
									input_ids.extend(self.tokenizer.encode(A_			,      add_special_tokens=A_		)		)

							if len(A_		) > self.tokenizer.model_max_length:
								snake_case_						       =	input_ids[-self.tokenizer.model_max_length :]
							return input_ids


'''simple docstring'''
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




