'''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


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A = None		,						__A = None		,						__A=None		,						__A=None				) ->     Tuple:
								if not conversation_id:
														_lowerCAmelCase										=uuid.uuida()
								if past_user_inputs is None:
														_lowerCAmelCase										=[]
								if generated_responses is None:
														_lowerCAmelCase										=[]

								_lowerCAmelCase										=conversation_id
								_lowerCAmelCase										=past_user_inputs
								_lowerCAmelCase										=generated_responses
								_lowerCAmelCase										=text






		def __eq__(   self		,						__A				) ->     int:
								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 		UpperCamelCase__       (   self		,						__A		,						__A = False				) ->     Any:
								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}".'''				)
																				_lowerCAmelCase										=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:
														_lowerCAmelCase										=text






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input				)
								_lowerCAmelCase										=None






		def 		UpperCamelCase__       (   self		,						__A				) ->     Optional[Any]:
								self.generated_responses.append(__A				)






		def 		UpperCamelCase__       (   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				) ->     Dict:
								_lowerCAmelCase										=F'''Conversation id: {self.uuid} \n'''
								for is_user, text in self.iter_texts():
														_lowerCAmelCase										='user' if is_user else 'bot'
														output += F'''{name} >> {text} \n'''
								return output

@add_end_docstrings(
    __lowercase    ,			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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						*__A		,						**__A				) ->     Tuple:
								super().__init__(*__A		,						**__A				)
								if self.tokenizer.pad_token_id is None:
														_lowerCAmelCase										=self.tokenizer.eos_token






		def 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[Any]:
								_lowerCAmelCase										={}
								_lowerCAmelCase										={}
								_lowerCAmelCase										={}

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

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

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






		def __call__(   self		,						__A		,						__A=0		,						**__A				) ->     List[str]:
								_lowerCAmelCase										=super().__call__(__A		,						num_workers=__A		,						**__A				)
								if isinstance(__A		,						__A				) and len(__A				) == 1:
														return outputs[0]
								return outputs






		def 		UpperCamelCase__       (   self		,						__A		,						__A=32				) ->     Dict[str, Any]:
								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'				):
														_lowerCAmelCase										=self.tokenizer._build_conversation_input_ids(__A				)
								else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														_lowerCAmelCase										=self._legacy_parse_and_tokenize(__A				)

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A=10		,						**__A				) ->     int:
								_lowerCAmelCase										=generate_kwargs.get('max_length'		,						self.model.config.max_length				)

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A=True				) ->     Dict:
								_lowerCAmelCase										=model_outputs['output_ids']
								_lowerCAmelCase										=self.tokenizer.decode(
								    output_ids[0]		,						skip_special_tokens=__A		,						clean_up_tokenization_spaces=__A		,						)
								_lowerCAmelCase										=model_outputs['conversation']
								conversation.mark_processed()
								conversation.append_response(__A				)
								return conversation






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								_lowerCAmelCase										=self.tokenizer.eos_token_id
								_lowerCAmelCase										=[]
								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:
														_lowerCAmelCase										=input_ids[-self.tokenizer.model_max_length :]
								return input_ids






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

							lowercase_     		= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





