




'''simple docstring'''




import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Dict   		=['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






       def __init__(							self,	lowerCAmelCase						):



              """simple docstring"""







              assert Path(hparams.data_dir						).exists()
              lowerCamelCase_							=Path(hparams.output_dir						)
              self.output_dir.mkdir(exist_ok=lowerCAmelCase						)

              lowerCamelCase_							=self.output_dir.joinpath('''student'''						)

              lowerCamelCase_							=str(lowerCAmelCase						)  # Tell lightning we are training the student
              lowerCamelCase_							=AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher						).eval()
              use_task_specific_params(lowerCAmelCase,	hparams.task						)  # We copy good generation parameters to student by default
              if hparams.student is not None:
                     lowerCamelCase_							=AutoModelForSeqaSeqLM.from_pretrained(hparams.student						)
                     use_task_specific_params(lowerCAmelCase,	hparams.task						)
                     lowerCamelCase_,   lowerCamelCase_							=None, None
              else:
                     lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=create_student_by_copying_alternating_layers(
                         lowerCAmelCase,	e=hparams.student_encoder_layers,	d=hparams.student_decoder_layers,	save_path=lowerCAmelCase						)

              if hparams.length_penalty != -1:
                     lowerCamelCase_							=hparams.length_penalty
              lowerCamelCase_							=hparams.teacher  # Use teacher's tokenizer
              super().__init__(lowerCAmelCase,	model=lowerCAmelCase,	config=student.config						)
              assert student.config.model_type == teacher.config.model_type, (
                  f'''teacher, student model types should be the same, got {student.config.model_type} !='''
                  f''' {teacher.config.model_type}'''
              )

              if student.config.model_type == "t5":
                     lowerCamelCase_							=len(student.get_encoder().block						)
                     lowerCamelCase_							=len(student.get_decoder().block						)
                     lowerCamelCase_							=len(teacher.get_encoder().block						)
                     lowerCamelCase_							=len(teacher.get_decoder().block						)
              else:
                     lowerCamelCase_							=student.config.encoder_layers
                     lowerCamelCase_							=student.config.decoder_layers
                     lowerCamelCase_							=teacher.config.encoder_layers
                     lowerCamelCase_							=teacher.config.decoder_layers

              lowerCamelCase_							=not (hparams.student is None or hparams.teacher == hparams.student)
              lowerCamelCase_							=(not self.different_base_models) and hparams.alpha_hid > 0
              lowerCamelCase_							=self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
              # self.different_encoder determines whether we need to run the teacher encoder
              lowerCamelCase_							=teacher
              freeze_params(self.teacher						)

              if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
                     try:
                            del self.teacher.model.encoder
                     except AttributeError:  # T5
                            del self.teacher.encoder

              if e_layer_ids is None:
                     lowerCamelCase_							=list(range(lowerCAmelCase						)						)
              if d_layer_ids is None:
                     lowerCamelCase_							=list(range(lowerCAmelCase						)						)

              lowerCamelCase_,   lowerCamelCase_							=e_layer_ids, d_layer_ids  # type: List[int], List[int]

              if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
                     if hparams.supervise_forward:
                            lowerCamelCase_							=get_layers_to_supervise(
                                n_student=len(self.e_layer_ids						),	n_teacher=lowerCAmelCase						)
                            lowerCamelCase_							=get_layers_to_supervise(
                                n_student=len(self.d_layer_ids						),	n_teacher=lowerCAmelCase						)
                     else:  # student layer should emulate hidden states of the teacher layer it was copied from
                            lowerCamelCase_							=self.e_layer_ids
                            lowerCamelCase_							=self.d_layer_ids
              else:
                     lowerCamelCase_							=None
                     lowerCamelCase_							=None

              lowerCamelCase_							=nn.KLDivLoss(reduction='''batchmean'''						)
              lowerCamelCase_							=2.0
              lowerCamelCase_							=hparams.alpha_mlm
              lowerCamelCase_							=hparams.alpha_ce
              lowerCamelCase_							=hparams.alpha_hid
              gc.collect()
              torch.cuda.empty_cache()






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=mask[:, :, None].expand_as(lowerCAmelCase						)
              lowerCamelCase_							=s_logits.size(-1						)
              lowerCamelCase_							=torch.masked_select(lowerCAmelCase,	lowerCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
              lowerCamelCase_							=torch.masked_select(lowerCAmelCase,	lowerCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
              lowerCamelCase_							=s_logits_slct.view(-1,	lowerCAmelCase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
              lowerCamelCase_							=t_logits_slct.view(-1,	lowerCAmelCase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
              assert t_logits_slct.size() == s_logits_slct.size()
              lowerCamelCase_							=(
                  self.ce_loss_fct(
                      nn.functional.log_softmax(s_logits_slct / self.temperature,	dim=-1						),	nn.functional.softmax(t_logits_slct / self.temperature,	dim=-1						),	)
                  * (self.temperature) ** 2
              )
              return loss_ce






       @staticmethod
       def   lowercase__  (							lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              SummarizationModule.add_model_specific_args(lowerCAmelCase,	lowerCAmelCase						)
              add_distill_args(lowerCAmelCase						)
              return parser






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.tokenizer.pad_token_id
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
              if isinstance(self.model,	lowerCAmelCase						):
                     lowerCamelCase_							=self.model._shift_right(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=shift_tokens_right(lowerCAmelCase,	lowerCAmelCase						)

              # noinspection PyCallingNonCallable
              lowerCamelCase_							=self(
                  lowerCAmelCase,	attention_mask=lowerCAmelCase,	decoder_input_ids=lowerCAmelCase,	output_hidden_states=self.do_calc_hidden_loss,	output_attentions=lowerCAmelCase,	use_cache=lowerCAmelCase,	)
              lowerCamelCase_							=student_outputs['''logits''']

              # Same cross entropy vs. label smoothing logic as finetune.py
              assert lm_logits.shape[-1] == self.model.config.vocab_size
              if self.hparams.label_smoothing == 0:
                     # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                     lowerCamelCase_							=nn.CrossEntropyLoss(ignore_index=lowerCAmelCase						)
                     lowerCamelCase_							=loss_fct(lm_logits.view(-1,	lm_logits.shape[-1]						),	labels.view(-1						)						)
              else:
                     lowerCamelCase_							=nn.functional.log_softmax(lowerCAmelCase,	dim=-1						)
                     lowerCamelCase_,   lowerCamelCase_							=label_smoothed_nll_loss(
                         lowerCAmelCase,	lowerCAmelCase,	self.hparams.label_smoothing,	ignore_index=lowerCAmelCase						)

              def zero_tensor():
                     return torch.tensor(0.0						).type_as(lowerCAmelCase						)

              lowerCamelCase_							=student_outputs[
                  '''encoder_last_hidden_state'''
              ]  # use this unless self.different_base_models
              lowerCamelCase_,   lowerCamelCase_							=zero_tensor(), zero_tensor()
              if self.different_encoder:  # compute encoder hidden state loss
                     lowerCamelCase_							=self.teacher.get_encoder()(
                         lowerCAmelCase,	attention_mask=lowerCAmelCase,	output_hidden_states=self.do_calc_hidden_loss,	)
                     if self.different_base_models:
                            lowerCamelCase_							=all_teacher_encoder_outputs['''last_hidden_state''']
                     elif self.do_calc_hidden_loss:
                            lowerCamelCase_							=self.calc_hidden_loss(
                                lowerCAmelCase,	student_outputs['''encoder_hidden_states'''],	all_teacher_encoder_outputs['''hidden_states'''],	self.e_matches,	normalize_hidden=self.hparams.normalize_hidden,	)

              lowerCamelCase_							=self.teacher(
                  lowerCAmelCase,	attention_mask=lowerCAmelCase,	encoder_outputs=(teacher_enc_outputs,),	decoder_input_ids=lowerCAmelCase,	output_hidden_states=self.do_calc_hidden_loss,	use_cache=lowerCAmelCase,	)
              lowerCamelCase_							=decoder_input_ids.ne(lowerCAmelCase						)
              lowerCamelCase_							=self.calc_ce_loss(lowerCAmelCase,	lowerCAmelCase,	teacher_outputs['''logits''']						)
              if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                     lowerCamelCase_							=self.calc_hidden_loss(
                         lowerCAmelCase,	student_outputs['''decoder_hidden_states'''],	teacher_outputs['''decoder_hidden_states'''],	self.d_matches,	normalize_hidden=self.hparams.normalize_hidden,	)

              lowerCamelCase_							=(
                  self.alpha_ce * loss_ce
                  + self.alpha_mlm * student_lm_loss
                  + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
              )
              return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec






       @staticmethod
       def   lowercase__  (							lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							='''expected list or tuple for hidden_states, got tensor of shape: '''
              assert not isinstance(lowerCAmelCase,	torch.Tensor						), f'''{msg}{hidden_states.shape}'''
              assert not isinstance(lowerCAmelCase,	torch.Tensor						), f'''{msg}{hidden_states_T.shape}'''
              lowerCamelCase_							=attention_mask.to(hidden_states[0]						)
              lowerCamelCase_							=mask.sum() * hidden_states[0].size(-1						)
              lowerCamelCase_							=torch.stack([hidden_states[i] for i in range(len(lowerCAmelCase						)						)]						)
              lowerCamelCase_							=torch.stack([hidden_states_T[j] for j in matches]						)
              assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
              if normalize_hidden:
                     lowerCamelCase_							=nn.functional.layer_norm(lowerCAmelCase,	student_states.shape[1:]						)
                     lowerCamelCase_							=nn.functional.layer_norm(lowerCAmelCase,	teacher_states.shape[1:]						)
              lowerCamelCase_							=nn.functional.mse_loss(lowerCAmelCase,	lowerCAmelCase,	reduction='''none'''						)
              lowerCamelCase_							=(mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
              return masked_mse





def   a_							(   __snake_case							:    Dict     )							->		Any:



       """simple docstring"""
       parser.add_argument('''--teacher'''     , type=__snake_case     )
       parser.add_argument('''--alpha_ce'''     , default=0.8     , type=__snake_case     )
       parser.add_argument('''--alpha_mlm'''     , default=0.2     , type=__snake_case     )
       parser.add_argument('''--alpha_hid'''     , default=0.0     , type=__snake_case     , required=__snake_case     )
       parser.add_argument('''--student'''     , type=__snake_case     , required=__snake_case     )
       parser.add_argument('''--student_decoder_layers'''     , default=12     , type=__snake_case     , required=__snake_case     )
       parser.add_argument('''--student_encoder_layers'''     , default=12     , type=__snake_case     , required=__snake_case     )
       parser.add_argument('''--no_teacher'''     , action='''store_true'''     , default=__snake_case     )
       parser.add_argument('''--length_penalty'''     , type=__snake_case     , default=-1     )
       parser.add_argument('''--supervise_forward'''     , action='''store_true'''     , default=__snake_case     )
       parser.add_argument('''--normalize_hidden'''     , action='''store_true'''     , default=__snake_case     )


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					List[Any]   		='translation'
       lowercase			:					Optional[Any]   		=['bleu']
       lowercase			:					Any   		='bleu'






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



              """simple docstring"""







              super().__init__(lowerCAmelCase,	**lowerCAmelCase						)
              assert hparams.src_lang is not None
              assert hparams.tgt_lang is not None
              lowerCamelCase_							=hparams.src_lang
              lowerCamelCase_							=hparams.tgt_lang
              if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,	lowerCAmelCase						):
                     lowerCamelCase_							=self.tokenizer.lang_code_to_id[hparams.tgt_lang]






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              return calculate_bleu(lowerCAmelCase,	lowerCAmelCase						)






       @staticmethod
       def   lowercase__  (							lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              TranslationModule.add_model_specific_args(lowerCAmelCase,	lowerCAmelCase						)
              add_distill_args(lowerCAmelCase						)
              return parser





def   a_							(   __snake_case							:    List[str]     )							->		Optional[Any]:



       """simple docstring"""
       if args.no_teacher:
              lowerCamelCase_							=TranslationModule if '''translation''' in args.task else SummarizationModule
       else:  # DISTILL WITH TEACHER
              lowerCamelCase_							=TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
       lowerCamelCase_							=module_cls.__name__
       print(F'''using module {args.setup_cls}'''     )
       lowerCamelCase_							=module_cls(__snake_case     )
       return model





def   a_							(   __snake_case							:    Union[str, Any]     )							->		str:



       """simple docstring"""
       Path(args.output_dir     ).mkdir(exist_ok=__snake_case     )
       check_output_dir(__snake_case     , expected_items=3     )

       lowerCamelCase_							=create_module(__snake_case     )
       return ft_main(__snake_case     , model=__snake_case     )


if __name__ == "__main__":
  a_   :	int							   = argparse.ArgumentParser()
  a_   :	Tuple							   = pl.Trainer.add_argparse_args(parser)
  a_   :	Optional[Any]							   = SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  a_   :	int							   = parser.parse_args()

  distill_main(args)










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       lowercase			:					Dict   		=[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=MCLIPConfig(
                  numDims=self.cross_attention_dim,	transformerDimensions=self.text_embedder_hidden_size,	hidden_size=self.text_embedder_hidden_size,	intermediate_size=37,	num_attention_heads=4,	num_hidden_layers=5,	vocab_size=1_005,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''in_channels''': 4,
                  # Out channels is double in channels because predicts mean and variance
                  '''out_channels''': 8,
                  '''addition_embed_type''': '''text_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''': '''text_image_proj''',
                  '''cross_attention_dim''': self.cross_attention_dim,
                  '''attention_head_dim''': 4,
                  '''resnet_time_scale_shift''': '''scale_shift''',
                  '''class_embed_type''': None,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              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   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=VQModel(**self.dummy_movq_kwargs						)
              return model






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''prompt''': '''horse''',
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=image_from_tuple[0, -3:, -3:, -1]

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

              lowerCamelCase_							=np.array(
                  [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

              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 						__UpperCamelCase (				unittest.TestCase       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

              lowerCamelCase_							=KandinskyPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-prior''',	torch_dtype=torch.floataa						)
              pipe_prior.to(lowerCAmelCase						)

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)




