






'''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      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Dict					,      __lowercase	:							str = None					,      __lowercase	:							uuid.UUID = None					,      __lowercase	:							Dict=None					,      __lowercase	:							Dict=None      )	->						Dict:
       if not conversation_id:
             SCREAMING_SNAKE_CASE__     :				int									=uuid.uuida()
       if past_user_inputs is None:
             SCREAMING_SNAKE_CASE__     :				Any									=[]
       if generated_responses is None:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[]

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




 def __eq__(      self	:							List[str]					,      __lowercase	:							List[Any]      )	->						str:
       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      __magic_name__	(      self	:							Any					,      __lowercase	:							str					,      __lowercase	:							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}\"."      )
                   SCREAMING_SNAKE_CASE__     :				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:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=text




 def      __magic_name__	(      self	:							int      )	->						int:
       if self.new_user_input:
             self.past_user_inputs.append(self.new_user_input      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=None




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							str      )	->						List[str]:
       self.generated_responses.append(__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Tuple:
       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	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=F"Conversation id: {self.uuid} \n"
       for is_user, text in self.iter_texts():
             SCREAMING_SNAKE_CASE__     :				List[str]									='''user''' if is_user else '''bot'''
             output += F"{name} >> {text} \n"
       return output






@add_end_docstrings(
    lowerCamelCase	,				R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """	,				)
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							Optional[Any]					,      *__lowercase	:							str					,      **__lowercase	:							Dict      )	->						Tuple:
       super().__init__(*__lowercase					,      **__lowercase      )
       if self.tokenizer.pad_token_id is None:
             SCREAMING_SNAKE_CASE__     :				Dict									=self.tokenizer.eos_token




 def      __magic_name__	(      self	:							Optional[Any]					,      __lowercase	:							List[str]=None					,      __lowercase	:							List[str]=None					,      __lowercase	:							Optional[int]=None					,      **__lowercase	:							Dict      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={}
       SCREAMING_SNAKE_CASE__     :				Optional[int]									={}
       SCREAMING_SNAKE_CASE__     :				Any									={}

       if min_length_for_response is not None:
             SCREAMING_SNAKE_CASE__     :				List[str]									=min_length_for_response
       if minimum_tokens is not None:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=minimum_tokens

       if "max_length" in generate_kwargs:
             SCREAMING_SNAKE_CASE__     :				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:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=clean_up_tokenization_spaces

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




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




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							Conversation					,      __lowercase	:							Tuple=32      )	->						Dict[str, Any]:
       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'''      ):
             SCREAMING_SNAKE_CASE__     :				Any									=self.tokenizer._build_conversation_input_ids(__lowercase      )
       else:
             # If the tokenizer cannot handle conversations, we default to only the old version
             SCREAMING_SNAKE_CASE__     :				List[str]									=self._legacy_parse_and_tokenize(__lowercase      )

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




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							str					,      __lowercase	:							Dict=10					,      **__lowercase	:							Optional[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=generate_kwargs.get('''max_length'''					,      self.model.config.max_length      )

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




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							Optional[int]					,      __lowercase	:							Dict=True      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=model_outputs['''output_ids''']
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.tokenizer.decode(
           output_ids[0]					,      skip_special_tokens=__lowercase					,      clean_up_tokenization_spaces=__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=model_outputs['''conversation''']
       conversation.mark_processed()
       conversation.append_response(__lowercase      )
       return conversation




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Conversation      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.tokenizer.eos_token_id
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[]
       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:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=input_ids[-self.tokenizer.model_max_length :]
       return input_ids







'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




 @property
 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       return self.time_input_dim




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Any:
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe(
           **self.get_dummy_inputs(__lowercase      )					,      return_dict=__lowercase					,      )[0]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       assert (
           np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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




 def      __magic_name__	(      self	:							int      )	->						Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )