




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  StableDiffusionPanoramaPipeline
  A__=  TEXT_TO_IMAGE_PARAMS
  A__=  TEXT_TO_IMAGE_BATCH_PARAMS
  A__=  TEXT_TO_IMAGE_IMAGE_PARAMS
  A__=  TEXT_TO_IMAGE_IMAGE_PARAMS






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         torch.manual_seed(0 )
         UpperCAmelCase__										=     UNetaDConditionModel(
             block_out_channels=(32, 64)		,						layers_per_block=1		,						sample_size=32		,						in_channels=4		,						out_channels=4		,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,						cross_attention_dim=32		,						)
         UpperCAmelCase__										=     DDIMScheduler()
         torch.manual_seed(0 )
         UpperCAmelCase__										=     AutoencoderKL(
             block_out_channels=[32, 64]		,						in_channels=3		,						out_channels=3		,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,						latent_channels=4		,						)
         torch.manual_seed(0 )
         UpperCAmelCase__										=     CLIPTextConfig(
             bos_token_id=0		,						eos_token_id=2		,						hidden_size=32		,						intermediate_size=37		,						layer_norm_eps=1E-05		,						num_attention_heads=4		,						num_hidden_layers=5		,						pad_token_id=1		,						vocab_size=10_00		,						)
         UpperCAmelCase__										=     CLIPTextModel(_lowercase )
         UpperCAmelCase__										=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase__										=     {
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					int=0 ):




         """simple docstring"""







         UpperCAmelCase__										=     torch.manual_seed(_lowercase )
         UpperCAmelCase__										=     {
             "prompt": "a photo of the dolomites",
             "generator": generator,
             # Setting height and width to None to prevent OOMs on CPU.
             "height": None,
             "width": None,
             "num_inference_steps": 1,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }
         return inputs






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__										=     self.get_dummy_components()
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline(**_lowercase )
         UpperCAmelCase__										=     sd_pipe.to(_lowercase )
         sd_pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     self.get_dummy_inputs(_lowercase )
         UpperCAmelCase__										=     sd_pipe(**_lowercase ).images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__										=     np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         super().test_inference_batch_consistent(batch_sizes=[1, 2] )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         super().test_inference_batch_single_identical(batch_size=2		,						expected_max_diff=3.25E-3 )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__										=     self.get_dummy_components()
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline(**_lowercase )
         UpperCAmelCase__										=     sd_pipe.to(_lowercase )
         sd_pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     self.get_dummy_inputs(_lowercase )
         UpperCAmelCase__										=     "french fries"
         UpperCAmelCase__										=     sd_pipe(**_lowercase		,						negative_prompt=_lowercase )
         UpperCAmelCase__										=     output.images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__										=     np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__										=     self.get_dummy_components()
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline(**_lowercase )
         UpperCAmelCase__										=     sd_pipe.to(_lowercase )
         sd_pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     self.get_dummy_inputs(_lowercase )
         UpperCAmelCase__										=     sd_pipe(**_lowercase		,						view_batch_size=2 )
         UpperCAmelCase__										=     output.images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__										=     np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__										=     self.get_dummy_components()
         UpperCAmelCase__										=     EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5		,						beta_end=0.0_1_2		,						beta_schedule="scaled_linear" )
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline(**_lowercase )
         UpperCAmelCase__										=     sd_pipe.to(_lowercase )
         sd_pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     self.get_dummy_inputs(_lowercase )
         UpperCAmelCase__										=     sd_pipe(**_lowercase ).images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__										=     np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__										=     self.get_dummy_components()
         UpperCAmelCase__										=     PNDMScheduler(
             beta_start=0.0_0_0_8_5		,						beta_end=0.0_1_2		,						beta_schedule="scaled_linear"		,						skip_prk_steps=_lowercase )
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline(**_lowercase )
         UpperCAmelCase__										=     sd_pipe.to(_lowercase )
         sd_pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     self.get_dummy_inputs(_lowercase )
         UpperCAmelCase__										=     sd_pipe(**_lowercase ).images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__										=     np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




@slow
@require_torch_gpu
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					List[str]=0 ):




         """simple docstring"""







         UpperCAmelCase__										=     torch.manual_seed(_lowercase )
         UpperCAmelCase__										=     {
             "prompt": "a photo of the dolomites",
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "stabilityai/stable-diffusion-2-base"
         UpperCAmelCase__										=     DDIMScheduler.from_pretrained(_lowercase		,						subfolder="scheduler" )
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline.from_pretrained(_lowercase		,						scheduler=_lowercase		,						safety_checker=_lowercase )
         pipe.to(_lowercase )
         pipe.set_progress_bar_config(disable=_lowercase )
         pipe.enable_attention_slicing()

         UpperCAmelCase__										=     self.get_inputs()
         UpperCAmelCase__										=     pipe(**_lowercase ).images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         UpperCAmelCase__										=     np.array(
             [
                 0.3_6_9_6_8_3_9_2,
                 0.2_7_0_2_5_3_7_2,
                 0.3_2_4_4_6_7_6_6,
                 0.2_8_3_7_9_3_8_7,
                 0.3_6_3_6_3_2_7_4,
                 0.3_0_7_3_3_3_4_7,
                 0.2_7_1_0_0_0_2_7,
                 0.2_7_0_5_4_1_2_5,
                 0.2_5_5_3_6_0_9_6,
             ] )

         assert np.abs(expected_slice - image_slice ).max() < 1E-2






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline.from_pretrained(
             "stabilityai/stable-diffusion-2-base"		,						safety_checker=_lowercase )
         UpperCAmelCase__										=     LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.to(_lowercase )
         pipe.set_progress_bar_config(disable=_lowercase )
         pipe.enable_attention_slicing()

         UpperCAmelCase__										=     self.get_inputs()
         UpperCAmelCase__										=     pipe(**_lowercase ).images
         UpperCAmelCase__										=     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         UpperCAmelCase__										=     np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ] )

         assert np.abs(expected_slice - image_slice ).max() < 1E-3






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     0

         def callback_fn(_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					torch.FloatTensor ) -> None:
                UpperCAmelCase__										=     True
                nonlocal number_of_steps
                number_of_steps += 1
                if step == 1:
                       UpperCAmelCase__										=     latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 2_56)
                       UpperCAmelCase__										=     latents[0, -3:, -3:, -1]

                       UpperCAmelCase__										=     np.array(
                           [
                               0.1_8_6_8_1_8_6_9,
                               0.3_3_9_0_7_8_1_6,
                               0.5_3_6_1_2_7_6,
                               0.1_4_4_3_2_8_6_5,
                               -0.0_2_8_5_6_6_1_1,
                               -0.7_3_9_4_1_1_2_3,
                               0.2_3_3_9_7_9_8_7,
                               0.4_7_3_2_2_6_8_2,
                               -0.3_7_8_2_3_1_6_4,
                           ] )
                       assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
                elif step == 2:
                       UpperCAmelCase__										=     latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 2_56)
                       UpperCAmelCase__										=     latents[0, -3:, -3:, -1]

                       UpperCAmelCase__										=     np.array(
                           [
                               0.1_8_5_3_9_6_4_5,
                               0.3_3_9_8_7_2_4_8,
                               0.5_3_7_8_5_5_9,
                               0.1_4_4_3_7_1_4_2,
                               -0.0_2_4_5_5_2_6_1,
                               -0.7_3_3_8_3_1_7,
                               0.2_3_9_9_0_7_5_5,
                               0.4_7_3_5_6_2_7_2,
                               -0.3_7_8_6_5_0_5,
                           ] )

                       assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

         UpperCAmelCase__										=     False

         UpperCAmelCase__										=     "stabilityai/stable-diffusion-2-base"
         UpperCAmelCase__										=     DDIMScheduler.from_pretrained(_lowercase		,						subfolder="scheduler" )
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline.from_pretrained(_lowercase		,						scheduler=_lowercase		,						safety_checker=_lowercase )
         UpperCAmelCase__										=     pipe.to(_lowercase )
         pipe.set_progress_bar_config(disable=_lowercase )
         pipe.enable_attention_slicing()

         UpperCAmelCase__										=     self.get_inputs()
         pipe(**_lowercase		,						callback=_lowercase		,						callback_steps=1 )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase__										=     "stabilityai/stable-diffusion-2-base"
         UpperCAmelCase__										=     DDIMScheduler.from_pretrained(_lowercase		,						subfolder="scheduler" )
         UpperCAmelCase__										=     StableDiffusionPanoramaPipeline.from_pretrained(_lowercase		,						scheduler=_lowercase		,						safety_checker=_lowercase )
         UpperCAmelCase__										=     pipe.to(_lowercase )
         pipe.set_progress_bar_config(disable=_lowercase )
         pipe.enable_attention_slicing(1 )
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase__										=     self.get_inputs()
         UpperCAmelCase__										=     pipe(**_lowercase )

         UpperCAmelCase__										=     torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 10**9









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     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					str = None		,						_lowercase      :					uuid.UUID = None		,						_lowercase      :					Dict=None		,						_lowercase      :					Any=None ):




         """simple docstring"""







         if not conversation_id:
                UpperCAmelCase__										=     uuid.uuida()
         if past_user_inputs is None:
                UpperCAmelCase__										=     []
         if generated_responses is None:
                UpperCAmelCase__										=     []

         UpperCAmelCase__										=     conversation_id
         UpperCAmelCase__										=     past_user_inputs
         UpperCAmelCase__										=     generated_responses
         UpperCAmelCase__										=     text






  def __eq__(					self      :					Any		,						_lowercase      :					str ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                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      :					Any		,						_lowercase      :					str		,						_lowercase      :					bool = 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}\".""" )
                       UpperCAmelCase__										=     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__										=     text






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input )
         UpperCAmelCase__										=     None






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					str ):




         """simple docstring"""







         self.generated_responses.append(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """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      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     F"""Conversation id: {self.uuid} \n"""
         for is_user, text in self.iter_texts():
                UpperCAmelCase__										=     "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     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					Any ):




         """simple docstring"""







         super().__init__(*_lowercase		,						**_lowercase )
         if self.tokenizer.pad_token_id is None:
                UpperCAmelCase__										=     self.tokenizer.eos_token






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					int=None		,						_lowercase      :					Any=None		,						_lowercase      :					List[str]=None		,						**_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}

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

         if "max_length" in generate_kwargs:
                UpperCAmelCase__										=     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__										=     clean_up_tokenization_spaces

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






  def __call__(					self      :					Dict		,						_lowercase      :					Union[Conversation, List[Conversation]]		,						_lowercase      :					List[str]=0		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     super().__call__(_lowercase		,						num_workers=_lowercase		,						**_lowercase )
         if isinstance(_lowercase		,						_lowercase ) and len(_lowercase ) == 1:
                return outputs[0]
         return outputs






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Conversation		,						_lowercase      :					List[Any]=32 ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                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__										=     self.tokenizer._build_conversation_input_ids(_lowercase )
         else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                UpperCAmelCase__										=     self._legacy_parse_and_tokenize(_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[Any]=10		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     generate_kwargs.get("max_length"		,						self.model.config.max_length )

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






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     model_outputs["output_ids"]
         UpperCAmelCase__										=     self.tokenizer.decode(
             output_ids[0]		,						skip_special_tokens=_lowercase		,						clean_up_tokenization_spaces=_lowercase		,						)
         UpperCAmelCase__										=     model_outputs["conversation"]
         conversation.mark_processed()
         conversation.append_response(_lowercase )
         return conversation






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Conversation ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer.eos_token_id
         UpperCAmelCase__										=     []
         for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) + [eos_token_id] )
                else:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) )

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





