



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 			__lowerCAmelCase  :




 def __init__(    self						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None					):



    '''simple docstring'''
    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						,					__UpperCAmelCase					):



    '''simple docstring'''
    if not isinstance(__snake_case						,					__snake_case					):
       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						,					__UpperCAmelCase						,					__UpperCAmelCase = 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    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    if self.new_user_input:
       self.past_user_inputs.append(self.new_user_input					)
    __lowerCamelCase    				=     None




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    self.generated_responses.append(__snake_case					)




 def 							lowerCamelCase   (    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    				=     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(
    SCREAMING_SNAKE_CASE_	,						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  (						SCREAMING_SNAKE_CASE_ ):




 def __init__(    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__(*__snake_case						,					**__snake_case					)
    if self.tokenizer.pad_token_id is None:
       __lowerCamelCase    				=     self.tokenizer.eos_token




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __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(__snake_case					)
    return preprocess_params, forward_params, postprocess_params




 def __call__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=0						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     super().__call__(__snake_case						,					num_workers=__snake_case						,					**__snake_case					)
    if isinstance(__snake_case						,					__snake_case					) and len(__snake_case					) == 1:
       return outputs[0]
    return outputs




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=32					):



    '''simple docstring'''
    if not isinstance(__snake_case						,					__snake_case					):
       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(__snake_case					)
    else:
       # If the tokenizer cannot handle conversations, we default to only the old version
       __lowerCamelCase    				=     self._legacy_parse_and_tokenize(__snake_case					)

    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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=10						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __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(**__snake_case						,					**__snake_case					)
    if self.model.config.is_encoder_decoder:
       __lowerCamelCase    				=     1
    else:
       __lowerCamelCase    				=     n
    return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=True					):



    '''simple docstring'''
    __lowerCamelCase    				=     model_outputs["""output_ids"""]
    __lowerCamelCase    				=     self.tokenizer.decode(
        output_ids[0]						,					skip_special_tokens=__snake_case						,					clean_up_tokenization_spaces=__snake_case						,					)
    __lowerCamelCase    				=     model_outputs["""conversation"""]
    conversation.mark_processed()
    conversation.append_response(__snake_case					)
    return conversation




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __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(__snake_case						,					add_special_tokens=__snake_case					) + [eos_token_id]					)
       else:
          input_ids.extend(self.tokenizer.encode(__snake_case						,					add_special_tokens=__snake_case					)					)

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














"""simple docstring"""





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"efficientnet"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 6_0_0     ,		__snake_case  :      float = 2.0     ,		__snake_case  :      float = 3.1     ,		__snake_case  :      int = 8     ,		__snake_case  :      List[int] = [3, 3, 5, 3, 5, 5, 3]     ,		__snake_case  :      List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,		__snake_case  :      List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,		__snake_case  :      List[int] = []     ,		__snake_case  :      List[int] = [1, 2, 2, 2, 1, 2, 1]     ,		__snake_case  :      List[int] = [1, 2, 2, 3, 3, 4, 1]     ,		__snake_case  :      List[int] = [1, 6, 6, 6, 6, 6, 6]     ,		__snake_case  :      float = 0.25     ,		__snake_case  :      str = "swish"     ,		__snake_case  :      int = 2_5_6_0     ,		__snake_case  :      str = "mean"     ,		__snake_case  :      float = 0.02     ,		__snake_case  :      float = 0.001     ,		__snake_case  :      float = 0.99     ,		__snake_case  :      float = 0.5     ,		__snake_case  :      float = 0.2     ,		**__snake_case  :      List[Any]     ,		)			->							List[Any]:
											super().__init__(**__snake_case					)

											__magic_name__:	str							=       num_channels
											__magic_name__:	List[str]							=       image_size
											__magic_name__:	List[str]							=       width_coefficient
											__magic_name__:	Optional[Any]							=       depth_coefficient
											__magic_name__:	Tuple							=       depth_divisor
											__magic_name__:	Dict							=       kernel_sizes
											__magic_name__:	int							=       in_channels
											__magic_name__:	str							=       out_channels
											__magic_name__:	Dict							=       depthwise_padding
											__magic_name__:	Union[str, Any]							=       strides
											__magic_name__:	Dict							=       num_block_repeats
											__magic_name__:	Tuple							=       expand_ratios
											__magic_name__:	List[str]							=       squeeze_expansion_ratio
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Tuple							=       hidden_dim
											__magic_name__:	int							=       pooling_type
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[str]							=       batch_norm_eps
											__magic_name__:	str							=       batch_norm_momentum
											__magic_name__:	List[str]							=       dropout_rate
											__magic_name__:	Dict							=       drop_connect_rate
											__magic_name__:	Optional[Any]							=       sum(__snake_case					) * 4



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							float:
											return 1E-5



