"""simple docstring"""




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


__lowerCamelCase							 =     logging.get_logger(__name__)
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase=None						,__UpperCAmelCase=None   )    -> Any:
													if not conversation_id:
																				A__		   =							uuid.uuida()
													if past_user_inputs is None:
																				A__		   =							[]
													if generated_responses is None:
																				A__		   =							[]

													A__		   =							conversation_id
													A__		   =							past_user_inputs
													A__		   =							generated_responses
													A__		   =							text





						def __eq__(				self						,__UpperCAmelCase   )    -> Optional[int]:
													if not isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				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						,__UpperCAmelCase						,__UpperCAmelCase = False   )    -> Optional[int]:
													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__		   =							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__		   =							text





						def  snake_case__		(				self   )    -> str:
													if self.new_user_input:
																				self.past_user_inputs.append(self.new_user_input   )
													A__		   =							None





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Any:
													self.generated_responses.append(__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													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   )    -> Optional[Any]:
													A__		   =							f'''Conversation id: {self.uuid} \n'''
													for is_user, text in self.iter_texts():
																				A__		   =							'user' if is_user else 'bot'
																				output += f'''{name} >> {text} \n'''
													return output






@add_end_docstrings(
    __A						,  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__(	__A       ):





						def __init__(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Union[str, Any]:
													super().__init__(*__UpperCAmelCase						,**__UpperCAmelCase   )
													if self.tokenizer.pad_token_id is None:
																				A__		   =							self.tokenizer.eos_token





						def  snake_case__		(				self						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Any:
													A__		   =							{}
													A__		   =							{}
													A__		   =							{}

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

													if "max_length" in generate_kwargs:
																				A__		   =							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__		   =							clean_up_tokenization_spaces

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





						def __call__(				self						,__UpperCAmelCase						,__UpperCAmelCase=0						,**__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							super().__call__(__UpperCAmelCase						,num_workers=__UpperCAmelCase						,**__UpperCAmelCase   )
													if isinstance(__UpperCAmelCase						,__UpperCAmelCase   ) and len(__UpperCAmelCase   ) == 1:
																				return outputs[0]
													return outputs





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=32   )    -> Dict[str, Any]:
													if not isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				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__		   =							self.tokenizer._build_conversation_input_ids(__UpperCAmelCase   )
													else:
																				# If the tokenizer cannot handle conversations, we default to only the old version
																				A__		   =							self._legacy_parse_and_tokenize(__UpperCAmelCase   )

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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=10						,**__UpperCAmelCase   )    -> List[Any]:
													A__		   =							generate_kwargs.get('max_length'						,self.model.config.max_length   )

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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=True   )    -> Any:
													A__		   =							model_outputs['output_ids']
													A__		   =							self.tokenizer.decode(
													    output_ids[0]						,skip_special_tokens=__UpperCAmelCase						,clean_up_tokenization_spaces=__UpperCAmelCase						,)
													A__		   =							model_outputs['conversation']
													conversation.mark_processed()
													conversation.append_response(__UpperCAmelCase   )
													return conversation





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.tokenizer.eos_token_id
													A__		   =							[]
													for is_user, text in conversation.iter_texts():
																				if eos_token_id is not None:
																											input_ids.extend(self.tokenizer.encode(__UpperCAmelCase						,add_special_tokens=__UpperCAmelCase   ) + [eos_token_id]   )
																				else:
																											input_ids.extend(self.tokenizer.encode(__UpperCAmelCase						,add_special_tokens=__UpperCAmelCase   )   )

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






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''   )
													return resize(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													A__		   =							make_list_of_images(__UpperCAmelCase   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_color_quantize and clusters is None:
																				raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

													# All transformations expect numpy arrays.
													A__		   =							[to_numpy_array(__UpperCAmelCase   ) for image in images]

													if do_resize:
																				A__		   =							[self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   ) for image in images]

													if do_normalize:
																				A__		   =							[self.normalize(image=__UpperCAmelCase   ) for image in images]

													if do_color_quantize:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,ChannelDimension.LAST   ) for image in images]
																				# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																				A__		   =							np.array(__UpperCAmelCase   )
																				A__		   =							color_quantize(__UpperCAmelCase						,__UpperCAmelCase   ).reshape(images.shape[:-1]   )

																				# flatten to (batch_size, height*width)
																				A__		   =							images.shape[0]
																				A__		   =							images.reshape(__UpperCAmelCase						,-1   )

																				# We need to convert back to a list of images to keep consistent behaviour across processors.
																				A__		   =							list(__UpperCAmelCase   )
													else:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   ) for image in images]

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





