
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowercase  :					str													=       logging.get_logger(__name__)

# General docstring
lowercase  :					int													=       'RegNetConfig'

# Base docstring
lowercase  :					Tuple													=       'facebook/regnet-y-040'
lowercase  :					Any													=       [1, 1088, 7, 7]

# Image classification docstring
lowercase  :					Union[str, Any]													=       'facebook/regnet-y-040'
lowercase  :					Any													=       'tabby, tabby cat'

lowercase  :					int													=       [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]     ,   a		:int     ,   a		:int = 3     ,   a		:int = 1     ,   a		:int = 1     ,   a		:Optional[str] = "relu"     ,   **a		:Dict     ,   )		-> List[Any]:
								super().__init__(**a						)
								# The padding and conv has been verified in
								# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
								__UpperCamelCase	:   List[str]			   =					tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
								__UpperCamelCase	:   Union[str, Any]			   =					tf.keras.layers.ConvaD(
								    filters=a     ,   kernel_size=a     ,   strides=a     ,   padding="VALID"     ,   groups=a     ,   use_bias=a     ,   name="convolution"     ,   )
								__UpperCamelCase	:   Any			   =					tf.keras.layers.BatchNormalization(epsilon=1E-5     ,   momentum=0.9     ,   name="normalization"						)
								__UpperCamelCase	:   Any			   =					ACTaFN[activation] if activation is not None else tf.identity







	def    _lowerCamelCase				(					self		:int     ,   a		:int						)		-> Optional[int]:
								__UpperCamelCase	:   List[str]			   =					self.convolution(self.padding(a						)						)
								__UpperCamelCase	:   Dict			   =					self.normalization(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					self.activation(a						)
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:Any     ,   a		:RegNetConfig     ,   **a		:int						)		-> Any:
								super().__init__(**a						)
								__UpperCamelCase	:   Dict			   =					config.num_channels
								__UpperCamelCase	:   str			   =					TFRegNetConvLayer(
								    out_channels=config.embedding_size     ,   kernel_size=3     ,   stride=2     ,   activation=config.hidden_act     ,   name="embedder"     ,   )







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   int			   =					shape_list(a						)[1]
								if tf.executing_eagerly() and num_channels != self.num_channels:
															raise ValueError(
															    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."						)

								# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
								# So change the input format from `NCHW` to `NHWC`.
								# shape = (batch_size, in_height, in_width, in_channels=num_channels)
								__UpperCamelCase	:   List[str]			   =					tf.transpose(a     ,   perm=(0, 2, 3, 1)						)
								__UpperCamelCase	:   List[str]			   =					self.embedder(a						)
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   a		:int     ,   a		:int = 2     ,   **a		:Union[str, Any]						)		-> Optional[Any]:
								super().__init__(**a						)
								__UpperCamelCase	:   Tuple			   =					tf.keras.layers.ConvaD(
								    filters=a     ,   kernel_size=1     ,   strides=a     ,   use_bias=a     ,   name="convolution"						)
								__UpperCamelCase	:   List[str]			   =					tf.keras.layers.BatchNormalization(epsilon=1E-5     ,   momentum=0.9     ,   name="normalization"						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:tf.Tensor     ,   a		:bool = False						)		-> tf.Tensor:
								return self.normalization(self.convolution(a						)     ,   training=a						)




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:Any     ,   a		:int     ,   a		:int     ,   **a		:Optional[Any]						)		-> List[str]:
								super().__init__(**a						)
								__UpperCamelCase	:   Dict			   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=a     ,   name="pooler"						)
								__UpperCamelCase	:   Tuple			   =					[
								    tf.keras.layers.ConvaD(filters=a     ,   kernel_size=1     ,   activation="relu"     ,   name="attention.0"						),
								    tf.keras.layers.ConvaD(filters=a     ,   kernel_size=1     ,   activation="sigmoid"     ,   name="attention.2"						),
								]







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Any						)		-> Tuple:
								# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
								__UpperCamelCase	:   Optional[int]			   =					self.pooler(a						)
								for layer_module in self.attention:
															__UpperCamelCase	:   str			   =					layer_module(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					hidden_state * pooled
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:RegNetConfig     ,   a		:int     ,   a		:int     ,   a		:int = 1     ,   **a		:Any						)		-> List[Any]:
								super().__init__(**a						)
								__UpperCamelCase	:   Union[str, Any]			   =					in_channels != out_channels or stride != 1
								__UpperCamelCase	:   Union[str, Any]			   =					max(1     ,   out_channels // config.groups_width						)
								__UpperCamelCase	:   Any			   =					(
								    TFRegNetShortCut(a     ,   stride=a     ,   name="shortcut"						)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation("linear"     ,   name="shortcut"						)
								)
								# `self.layers` instead of `self.layer` because that is a reserved argument.
								__UpperCamelCase	:   Tuple			   =					[
								    TFRegNetConvLayer(a     ,   kernel_size=1     ,   activation=config.hidden_act     ,   name="layer.0"						),
								    TFRegNetConvLayer(
								        a     ,   stride=a     ,   groups=a     ,   activation=config.hidden_act     ,   name="layer.1"						),
								    TFRegNetConvLayer(a     ,   kernel_size=1     ,   activation=a     ,   name="layer.2"						),
								]
								__UpperCamelCase	:   List[Any]			   =					ACTaFN[config.hidden_act]







	def    _lowerCamelCase				(					self		:int     ,   a		:List[str]						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[int]			   =					hidden_state
								for layer_module in self.layers:
															__UpperCamelCase	:   Any			   =					layer_module(a						)
								__UpperCamelCase	:   Optional[Any]			   =					self.shortcut(a						)
								hidden_state += residual
								__UpperCamelCase	:   List[Any]			   =					self.activation(a						)
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:RegNetConfig     ,   a		:int     ,   a		:int     ,   a		:int = 1     ,   **a		:str						)		-> int:
								super().__init__(**a						)
								__UpperCamelCase	:   Dict			   =					in_channels != out_channels or stride != 1
								__UpperCamelCase	:   Any			   =					max(1     ,   out_channels // config.groups_width						)
								__UpperCamelCase	:   Optional[int]			   =					(
								    TFRegNetShortCut(a     ,   stride=a     ,   name="shortcut"						)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation("linear"     ,   name="shortcut"						)
								)
								__UpperCamelCase	:   List[Any]			   =					[
								    TFRegNetConvLayer(a     ,   kernel_size=1     ,   activation=config.hidden_act     ,   name="layer.0"						),
								    TFRegNetConvLayer(
								        a     ,   stride=a     ,   groups=a     ,   activation=config.hidden_act     ,   name="layer.1"						),
								    TFRegNetSELayer(a     ,   reduced_channels=int(round(in_channels / 4						)						)     ,   name="layer.2"						),
								    TFRegNetConvLayer(a     ,   kernel_size=1     ,   activation=a     ,   name="layer.3"						),
								]
								__UpperCamelCase	:   str			   =					ACTaFN[config.hidden_act]







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> Any:
								__UpperCamelCase	:   Union[str, Any]			   =					hidden_state
								for layer_module in self.layers:
															__UpperCamelCase	:   str			   =					layer_module(a						)
								__UpperCamelCase	:   str			   =					self.shortcut(a						)
								hidden_state += residual
								__UpperCamelCase	:   List[str]			   =					self.activation(a						)
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   a		:RegNetConfig     ,   a		:int     ,   a		:int     ,   a		:int = 2     ,   a		:int = 2     ,   **a		:int						)		-> List[Any]:
								super().__init__(**a						)

								__UpperCamelCase	:   Optional[int]			   =					TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
								__UpperCamelCase	:   Optional[Any]			   =					[
								    # downsampling is done in the first layer with stride of 2
								    layer(a     ,   a     ,   a     ,   stride=a     ,   name="layers.0"						),
								    *[layer(a     ,   a     ,   a     ,   name=f'layers.{i+1}'						) for i in range(depth - 1						)],
								]







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Any						)		-> Dict:
								for layer_module in self.layers:
															__UpperCamelCase	:   str			   =					layer_module(a						)
								return hidden_state




class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:RegNetConfig     ,   **a		:str						)		-> List[str]:
								super().__init__(**a						)
								__UpperCamelCase	:   Optional[Any]			   =					[]
								# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    TFRegNetStage(
								        a     ,   config.embedding_size     ,   config.hidden_sizes[0]     ,   stride=2 if config.downsample_in_first_stage else 1     ,   depth=config.depths[0]     ,   name="stages.0"     ,   )						)
								__UpperCamelCase	:   Optional[Any]			   =					zip(config.hidden_sizes     ,   config.hidden_sizes[1:]						)
								for i, ((in_channels, out_channels), depth) in enumerate(zip(a     ,   config.depths[1:]						)						):
															self.stages.append(TFRegNetStage(a     ,   a     ,   a     ,   depth=a     ,   name=f'stages.{i+1}'						)						)







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:tf.Tensor     ,   a		:bool = False     ,   a		:bool = True						)		-> TFBaseModelOutputWithNoAttention:
								__UpperCamelCase	:   Dict			   =					() if output_hidden_states else None

								for stage_module in self.stages:
															if output_hidden_states:
																						__UpperCamelCase	:   str			   =					hidden_states + (hidden_state,)

															__UpperCamelCase	:   Tuple			   =					stage_module(a						)

								if output_hidden_states:
															__UpperCamelCase	:   Union[str, Any]			   =					hidden_states + (hidden_state,)

								if not return_dict:
															return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

								return TFBaseModelOutputWithNoAttention(last_hidden_state=a     ,   hidden_states=a						)




@keras_serializable
class  lowerCamelCase__							(				tf.keras.layers.Layer):




	'''simple docstring'''




	_A								=     RegNetConfig







	def __init__(					self		:str     ,   a		:Optional[Any]     ,   **a		:int						)		-> Optional[Any]:
								super().__init__(**a						)
								__UpperCamelCase	:   Optional[Any]			   =					config
								__UpperCamelCase	:   List[str]			   =					TFRegNetEmbeddings(a     ,   name="embedder"						)
								__UpperCamelCase	:   str			   =					TFRegNetEncoder(a     ,   name="encoder"						)
								__UpperCamelCase	:   int			   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=a     ,   name="pooler"						)







	@unpack_inputs
	def    _lowerCamelCase				(					self		:str     ,   a		:tf.Tensor     ,   a		:Optional[bool] = None     ,   a		:Optional[bool] = None     ,   a		:bool = False     ,   )		-> TFBaseModelOutputWithPoolingAndNoAttention:
								__UpperCamelCase	:   int			   =					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__UpperCamelCase	:   Tuple			   =					return_dict if return_dict is not None else self.config.use_return_dict

								__UpperCamelCase	:   Tuple			   =					self.embedder(a     ,   training=a						)

								__UpperCamelCase	:   int			   =					self.encoder(
								    a     ,   output_hidden_states=a     ,   return_dict=a     ,   training=a						)

								__UpperCamelCase	:   Tuple			   =					encoder_outputs[0]
								__UpperCamelCase	:   Tuple			   =					self.pooler(a						)

								# Change to NCHW output format have uniformity in the modules
								__UpperCamelCase	:   List[Any]			   =					tf.transpose(a     ,   perm=(0, 3, 1, 2)						)
								__UpperCamelCase	:   int			   =					tf.transpose(a     ,   perm=(0, 3, 1, 2)						)

								# Change the other hidden state outputs to NCHW as well
								if output_hidden_states:
															__UpperCamelCase	:   Optional[int]			   =					tuple([tf.transpose(a     ,   perm=(0, 3, 1, 2)						) for h in encoder_outputs[1]]						)

								if not return_dict:
															return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return TFBaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=a     ,   pooler_output=a     ,   hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states     ,   )




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     RegNetConfig
	_A								=     'regnet'
	_A								=     'pixel_values'







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Dict:
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)     ,   dtype=tf.floataa						)}
lowercase  :					int													=       R'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase  :					Tuple													=       R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'




@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'     ,						__lowercase     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Dict     ,   a		:RegNetConfig     ,   *a		:Tuple     ,   **a		:Dict						)		-> Dict:
								super().__init__(a     ,   *a     ,   **a						)
								__UpperCamelCase	:   int			   =					TFRegNetMainLayer(a     ,   name="regnet"						)







	@unpack_inputs
	@add_start_docstrings_to_model_forward(a						)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC     ,   output_type=a     ,   config_class=_CONFIG_FOR_DOC     ,   modality="vision"     ,   expected_output=_EXPECTED_OUTPUT_SHAPE     ,   )
	def    _lowerCamelCase				(					self		:Tuple     ,   a		:tf.Tensor     ,   a		:Optional[bool] = None     ,   a		:Optional[bool] = None     ,   a		:Dict=False     ,   )		-> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
								__UpperCamelCase	:   Optional[Any]			   =					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__UpperCamelCase	:   str			   =					return_dict if return_dict is not None else self.config.use_return_dict

								__UpperCamelCase	:   List[str]			   =					self.regnet(
								    pixel_values=a     ,   output_hidden_states=a     ,   return_dict=a     ,   training=a     ,   )
								if not return_dict:
															return (outputs[0],) + outputs[1:]

								return TFBaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=outputs.last_hidden_state     ,   pooler_output=outputs.pooler_output     ,   hidden_states=outputs.hidden_states     ,   )




@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '     ,						__lowercase     ,						)
class  lowerCamelCase__							(				__lowercase     ,						__lowercase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   a		:RegNetConfig     ,   *a		:str     ,   **a		:List[Any]						)		-> str:
								super().__init__(a     ,   *a     ,   **a						)
								__UpperCamelCase	:   Tuple			   =					config.num_labels
								__UpperCamelCase	:   List[Any]			   =					TFRegNetMainLayer(a     ,   name="regnet"						)
								# classification head
								__UpperCamelCase	:   Any			   =					[
								    tf.keras.layers.Flatten(),
								    tf.keras.layers.Dense(config.num_labels     ,   name="classifier.1"						) if config.num_labels > 0 else tf.identity,
								]







	@unpack_inputs
	@add_start_docstrings_to_model_forward(a						)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,   output_type=a     ,   config_class=_CONFIG_FOR_DOC     ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,   )
	def    _lowerCamelCase				(					self		:Tuple     ,   a		:tf.Tensor = None     ,   a		:tf.Tensor = None     ,   a		:bool = None     ,   a		:bool = None     ,   a		:Optional[Any]=False     ,   )		-> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
								__UpperCamelCase	:   Optional[Any]			   =					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__UpperCamelCase	:   str			   =					return_dict if return_dict is not None else self.config.use_return_dict

								__UpperCamelCase	:   str			   =					self.regnet(
								    a     ,   output_hidden_states=a     ,   return_dict=a     ,   training=a						)

								__UpperCamelCase	:   int			   =					outputs.pooler_output if return_dict else outputs[1]

								__UpperCamelCase	:   str			   =					self.classifier[0](a						)
								__UpperCamelCase	:   int			   =					self.classifier[1](a						)

								__UpperCamelCase	:   Any			   =					None if labels is None else self.hf_compute_loss(labels=a     ,   logits=a						)

								if not return_dict:
															__UpperCamelCase	:   Tuple			   =					(logits,) + outputs[2:]
															return ((loss,) + output) if loss is not None else output

								return TFSequenceClassifierOutput(loss=a     ,   logits=a     ,   hidden_states=outputs.hidden_states						)

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  :					List[str]													=       logging.get_logger(__name__)




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:str     ,   a		:str = None     ,   a		:uuid.UUID = None     ,   a		:Tuple=None     ,   a		:Optional[Any]=None						)		-> str:
								if not conversation_id:
															__UpperCamelCase	:   Dict			   =					uuid.uuida()
								if past_user_inputs is None:
															__UpperCamelCase	:   List[Any]			   =					[]
								if generated_responses is None:
															__UpperCamelCase	:   Any			   =					[]

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







	def __eq__(					self		:Optional[int]     ,   a		:Optional[int]						)		-> Union[str, Any]:
								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    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str     ,   a		:bool = False						)		-> str:
								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}".'						)
																						__UpperCamelCase	:   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:
															__UpperCamelCase	:   int			   =					text







	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								if self.new_user_input:
															self.past_user_inputs.append(self.new_user_input						)
								__UpperCamelCase	:   Dict			   =					None







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> Optional[int]:
								self.generated_responses.append(a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								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		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					f'Conversation id: {self.uuid} \n'
								for is_user, text in self.iter_texts():
															__UpperCamelCase	:   str			   =					"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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   *a		:Tuple     ,   **a		:List[str]						)		-> Tuple:
								super().__init__(*a     ,   **a						)
								if self.tokenizer.pad_token_id is None:
															__UpperCamelCase	:   int			   =					self.tokenizer.eos_token







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[Any]=None     ,   a		:str=None     ,   a		:int=None     ,   **a		:str						)		-> List[str]:
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   str			   =					{}

								if min_length_for_response is not None:
															__UpperCamelCase	:   Optional[Any]			   =					min_length_for_response
								if minimum_tokens is not None:
															__UpperCamelCase	:   List[str]			   =					minimum_tokens

								if "max_length" in generate_kwargs:
															__UpperCamelCase	:   List[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:
															__UpperCamelCase	:   List[Any]			   =					clean_up_tokenization_spaces

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







	def __call__(					self		:Dict     ,   a		:Union[Conversation, List[Conversation]]     ,   a		:List[Any]=0     ,   **a		:Any						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					super().__call__(a     ,   num_workers=a     ,   **a						)
								if isinstance(a     ,   a						) and len(a						) == 1:
															return outputs[0]
								return outputs







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Conversation     ,   a		:Dict=3_2						)		-> 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"						):
															__UpperCamelCase	:   str			   =					self.tokenizer._build_conversation_input_ids(a						)
								else:
															# If the tokenizer cannot handle conversations, we default to only the old version
															__UpperCamelCase	:   Optional[Any]			   =					self._legacy_parse_and_tokenize(a						)

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







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]     ,   a		:Optional[Any]=1_0     ,   **a		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					generate_kwargs.get("max_length"     ,   self.model.config.max_length						)

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







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str     ,   a		:Optional[int]=True						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[str]			   =					model_outputs["output_ids"]
								__UpperCamelCase	:   Any			   =					self.tokenizer.decode(
								    output_ids[0]     ,   skip_special_tokens=a     ,   clean_up_tokenization_spaces=a     ,   )
								__UpperCamelCase	:   int			   =					model_outputs["conversation"]
								conversation.mark_processed()
								conversation.append_response(a						)
								return conversation







	def    _lowerCamelCase				(					self		:str     ,   a		:Conversation						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.tokenizer.eos_token_id
								__UpperCamelCase	:   Any			   =					[]
								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:
															__UpperCamelCase	:   Union[str, Any]			   =					input_ids[-self.tokenizer.model_max_length :]
								return input_ids