
'''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


A			      =      logging.get_logger(__name__)




class     __snake_case		:
					def __init__(     self,    A = None,    A = None,    A=None,    A=None      ):

									"""simple docstring"""


									if not conversation_id:
													lowerCamelCase					:			Optional[Any]									= uuid.uuida()
									if past_user_inputs is None:
													lowerCamelCase					:			Tuple									= []
									if generated_responses is None:
													lowerCamelCase					:			List[Any]									= []

									lowerCamelCase					:			uuid.UUID									= conversation_id
									lowerCamelCase					:			List[str]									= past_user_inputs
									lowerCamelCase					:			List[str]									= generated_responses
									lowerCamelCase					:			Optional[str]									= text
					def __eq__(     self,    A      ):

									"""simple docstring"""


									if not isinstance(lowerCamelCase__,    lowerCamelCase__      ):
													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}\".'''      )
																	lowerCamelCase					:			Optional[int]									= 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					:			str									= text
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if self.new_user_input:
													self.past_user_inputs.append(self.new_user_input      )
									lowerCamelCase					:			int									= None
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									self.generated_responses.append(lowerCamelCase__      )
					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"""


									lowerCamelCase					:			int									= F'''Conversation id: {self.uuid} \n'''
									for is_user, text in self.iter_texts():
													lowerCamelCase					:			str									= '''user''' if is_user else '''bot'''
													output += F'''{name} >> {text} \n'''
									return output





@add_end_docstrings(
    __lowerCAmelCase    ,   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     __snake_case		(   __lowerCAmelCase):
					def __init__(     self,    *A,    **A      ):

									"""simple docstring"""


									super().__init__(*lowerCamelCase__,    **lowerCamelCase__      )
									if self.tokenizer.pad_token_id is None:
													lowerCamelCase					:			Dict									= self.tokenizer.eos_token
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A=None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= {}
									lowerCamelCase					:			Optional[int]									= {}
									lowerCamelCase					:			List[str]									= {}

									if min_length_for_response is not None:
													lowerCamelCase					:			Optional[int]									= min_length_for_response
									if minimum_tokens is not None:
													lowerCamelCase					:			List[Any]									= minimum_tokens

									if "max_length" in generate_kwargs:
													lowerCamelCase					:			Union[str, Any]									= 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					:			Dict									= clean_up_tokenization_spaces

									if generate_kwargs:
													forward_params.update(lowerCamelCase__      )
									return preprocess_params, forward_params, postprocess_params
					def __call__(     self,    A,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= super().__call__(lowerCamelCase__,    num_workers=lowerCamelCase__,    **lowerCamelCase__      )
									if isinstance(lowerCamelCase__,    lowerCamelCase__      ) and len(lowerCamelCase__      ) == 1:
													return outputs[0]
									return outputs
					def     UpperCAmelCase_							(     self,    A,    A=32      ):

									"""simple docstring"""


									if not isinstance(lowerCamelCase__,    lowerCamelCase__      ):
													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					:			Any									= self.tokenizer._build_conversation_input_ids(lowerCamelCase__      )
									else:
													# If the tokenizer cannot handle conversations, we default to only the old version
													lowerCamelCase					:			Tuple									= self._legacy_parse_and_tokenize(lowerCamelCase__      )

									if self.framework == "pt":
													lowerCamelCase					:			Union[str, Any]									= torch.LongTensor([input_ids]      )
									elif self.framework == "tf":
													lowerCamelCase					:			List[str]									= tf.constant([input_ids]      )
									return {"input_ids": input_ids, "conversation": conversation}
					def     UpperCAmelCase_							(     self,    A,    A=10,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= generate_kwargs.get('max_length',    self.model.config.max_length      )

									lowerCamelCase					:			Optional[int]									= 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					:			int									= max_length - minimum_tokens
													lowerCamelCase					:			List[Any]									= model_inputs['''input_ids'''][:, -trim:]
													if "attention_mask" in model_inputs:
																	lowerCamelCase					:			List[Any]									= model_inputs['''attention_mask'''][:, -trim:]
									lowerCamelCase					:			Union[str, Any]									= model_inputs.pop('conversation'      )
									lowerCamelCase					:			str									= max_length
									lowerCamelCase					:			List[Any]									= self.model.generate(**lowerCamelCase__,    **lowerCamelCase__      )
									if self.model.config.is_encoder_decoder:
													lowerCamelCase					:			Any									= 1
									else:
													lowerCamelCase					:			Dict									= n
									return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
					def     UpperCAmelCase_							(     self,    A,    A=True      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= model_outputs['''output_ids''']
									lowerCamelCase					:			List[str]									= self.tokenizer.decode(
									    output_ids[0],    skip_special_tokens=lowerCamelCase__,    clean_up_tokenization_spaces=lowerCamelCase__,    )
									lowerCamelCase					:			Optional[int]									= model_outputs['''conversation''']
									conversation.mark_processed()
									conversation.append_response(lowerCamelCase__      )
									return conversation




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


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

									if len(lowerCamelCase__      ) > self.tokenizer.model_max_length:
													lowerCamelCase					:			str									= input_ids[-self.tokenizer.model_max_length :]
									return input_ids


'''simple docstring'''






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			      =      logging.get_logger(__name__)

A			      =      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''segformer'''
					def __init__(     self,    A=3,    A=4,    A=[2, 2, 2, 2],    A=[8, 4, 2, 1],    A=[32, 64, 160, 256],    A=[7, 3, 3, 3],    A=[4, 2, 2, 2],    A=[1, 2, 5, 8],    A=[4, 4, 4, 4],    A="gelu",    A=0.0,    A=0.0,    A=0.1,    A=0.02,    A=0.1,    A=1e-6,    A=256,    A=255,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.',    A,    )

									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			Dict									= num_encoder_blocks
									lowerCamelCase					:			Dict									= depths
									lowerCamelCase					:			Any									= sr_ratios
									lowerCamelCase					:			List[Any]									= hidden_sizes
									lowerCamelCase					:			List[Any]									= patch_sizes
									lowerCamelCase					:			Tuple									= strides
									lowerCamelCase					:			int									= mlp_ratios
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Tuple									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[str]									= classifier_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= initializer_range
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			int									= layer_norm_eps
									lowerCamelCase					:			Any									= decoder_hidden_size
									lowerCamelCase					:			Optional[int]									= kwargs.get('reshape_last_stage',    A      )
									lowerCamelCase					:			Any									= semantic_loss_ignore_index





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	version.parse('''1.11''')
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
