



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


__snake_case     :Dict			       =logging.get_logger(__name__)



class        lowerCAmelCase__					:


				def __init__(							self      :  str						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  uuid.UUID = None						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  str=None       )		->     Optional[int]:
											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      :  Tuple						, __UpperCamelCase      :  int       )		->     Union[str, Any]:
											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 __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  bool = False       )		->     Union[str, 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}".'''       )
																									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 __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											A      						=    None


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  str       )		->     List[Any]:
											self.generated_responses.append(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Union[str, 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      :  Dict       )		->     Dict:
											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(
    _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        lowerCAmelCase__					(  _lowerCamelCase       ):


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


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  Optional[int]=None						, __UpperCamelCase      :  Optional[Any]=None						, **__UpperCamelCase      :  int       )		->     Optional[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      :  List[str]						, __UpperCamelCase      :  Union[Conversation, List[Conversation]]						, __UpperCamelCase      :  List[str]=0						, **__UpperCamelCase      :  str       )		->     Any:
											A      						=    super().__call__(__UpperCamelCase						, num_workers=__UpperCamelCase						, **__UpperCamelCase       )
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ) and len(__UpperCamelCase       ) == 1:
																		return outputs[0]
											return outputs


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Conversation						, __UpperCamelCase      :  Union[str, Any]=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 __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Union[str, Any]=10						, **__UpperCamelCase      :  Dict       )		->     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 __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Any=True       )		->     int:
											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 __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  Conversation       )		->     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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     42
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @register_to_config
     def __init__(			self   ,						snake_case__ = 6_5536   ,						snake_case__ = None   ,						snake_case__ = 2   ,						snake_case__ = 2   ,						snake_case__ = 0   ,						snake_case__ = "fourier"   ,						snake_case__ = True   ,						snake_case__ = False   ,						snake_case__ = 0.0   ,						snake_case__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,						snake_case__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,						snake_case__ = "UNetMidBlock1D"   ,						snake_case__ = None   ,						snake_case__ = (32, 32, 64)   ,						snake_case__ = None   ,						snake_case__ = 8   ,						snake_case__ = 1   ,						snake_case__ = False   ,						):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase     :				Optional[int]       =							sample_size

            # time
            if time_embedding_type == "fourier":
                   _lowerCAmelCase     :				Tuple       =							GaussianFourierProjection(
                       embedding_size=8   ,						set_W_to_weight=snake_case__   ,						log=snake_case__   ,						flip_sin_to_cos=snake_case__ )
                   _lowerCAmelCase     :				List[Any]       =							2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                   _lowerCAmelCase     :				Optional[Any]       =							Timesteps(
                       block_out_channels[0]   ,						flip_sin_to_cos=snake_case__   ,						downscale_freq_shift=snake_case__ )
                   _lowerCAmelCase     :				Optional[int]       =							block_out_channels[0]

            if use_timestep_embedding:
                   _lowerCAmelCase     :				Any       =							block_out_channels[0] * 4
                   _lowerCAmelCase     :				List[str]       =							TimestepEmbedding(
                       in_channels=snake_case__   ,						time_embed_dim=snake_case__   ,						act_fn=snake_case__   ,						out_dim=block_out_channels[0]   ,						)

            _lowerCAmelCase     :				Tuple       =							nn.ModuleList([] )
            _lowerCAmelCase     :				Tuple       =							None
            _lowerCAmelCase     :				Optional[Any]       =							nn.ModuleList([] )
            _lowerCAmelCase     :				str       =							None

            # down
            _lowerCAmelCase     :				List[str]       =							in_channels
            for i, down_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				str       =							output_channel
                   _lowerCAmelCase     :				Any       =							block_out_channels[i]

                   if i == 0:
                          input_channel += extra_in_channels

                   _lowerCAmelCase     :				Tuple       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				List[str]       =							get_down_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_downsample=not is_final_block or downsample_each_block   ,						)
                   self.down_blocks.append(snake_case__ )

            # mid
            _lowerCAmelCase     :				List[str]       =							get_mid_block(
                snake_case__   ,						in_channels=block_out_channels[-1]   ,						mid_channels=block_out_channels[-1]   ,						out_channels=block_out_channels[-1]   ,						embed_dim=block_out_channels[0]   ,						num_layers=snake_case__   ,						add_downsample=snake_case__   ,						)

            # up
            _lowerCAmelCase     :				Union[str, Any]       =							list(reversed(snake_case__ ) )
            _lowerCAmelCase     :				List[Any]       =							reversed_block_out_channels[0]
            if out_block_type is None:
                   _lowerCAmelCase     :				Tuple       =							out_channels
            else:
                   _lowerCAmelCase     :				Tuple       =							block_out_channels[0]

            for i, up_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				int       =							output_channel
                   _lowerCAmelCase     :				Dict       =							(
                       reversed_block_out_channels[i + 1] if i < len(snake_case__ ) - 1 else final_upsample_channels
                   )

                   _lowerCAmelCase     :				Union[str, Any]       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				Tuple       =							get_up_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_upsample=not is_final_block   ,						)
                   self.up_blocks.append(snake_case__ )
                   _lowerCAmelCase     :				List[str]       =							output_channel

            # out
            _lowerCAmelCase     :				Tuple       =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,						32 )
            _lowerCAmelCase     :				Any       =							get_out_block(
                out_block_type=snake_case__   ,						num_groups_out=snake_case__   ,						embed_dim=block_out_channels[0]   ,						out_channels=snake_case__   ,						act_fn=snake_case__   ,						fc_dim=block_out_channels[-1] // 4   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							timestep
            if not torch.is_tensor(snake_case__ ):
                   _lowerCAmelCase     :				Optional[int]       =							torch.tensor([timesteps]   ,						dtype=torch.long   ,						device=sample.device )
            elif torch.is_tensor(snake_case__ ) and len(timesteps.shape ) == 0:
                   _lowerCAmelCase     :				List[Any]       =							timesteps[None].to(sample.device )

            _lowerCAmelCase     :				int       =							self.time_proj(snake_case__ )
            if self.config.use_timestep_embedding:
                   _lowerCAmelCase     :				Optional[int]       =							self.time_mlp(snake_case__ )
            else:
                   _lowerCAmelCase     :				List[str]       =							timestep_embed[..., None]
                   _lowerCAmelCase     :				Dict       =							timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
                   _lowerCAmelCase     :				Tuple       =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

            # 2. down
            _lowerCAmelCase     :				str       =							()
            for downsample_block in self.down_blocks:
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							downsample_block(hidden_states=snake_case__   ,						temb=snake_case__ )
                   down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                   _lowerCAmelCase     :				Any       =							self.mid_block(snake_case__   ,						snake_case__ )

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks ):
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[-1:]
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[:-1]
                   _lowerCAmelCase     :				Union[str, Any]       =							upsample_block(snake_case__   ,						res_hidden_states_tuple=snake_case__   ,						temb=snake_case__ )

            # 5. post-process
            if self.out_block:
                   _lowerCAmelCase     :				Optional[Any]       =							self.out_block(snake_case__   ,						snake_case__ )

            if not return_dict:
                   return (sample,)

            return UNetaDOutput(sample=snake_case__ )

