



"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:     Union[str, Any]								=  logging.get_logger(__name__)

lowerCamelCase				:     Union[str, Any]								=  {}




class    __snake_case(					__A       ):
    _A       = '''fsmt_decoder'''




    def __init__(   self    ,							A_=0    ,							A_=0			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			vocab_size
      _SCREAMING_SNAKE_CASE        =			bos_token_id




class    __snake_case(					__A       ):
    _A       = '''fsmt'''
    _A       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




    def __init__(   self    ,							A_=["en", "de"]    ,							A_=42_024    ,							A_=42_024    ,							A_="relu"    ,							A_=1_024    ,							A_=200    ,							A_=1_024    ,							A_=4_096    ,							A_=12    ,							A_=16    ,							A_=0.0    ,							A_=4_096    ,							A_=12    ,							A_=16    ,							A_=0.0    ,							A_=0.0    ,							A_=0.1    ,							A_=0.0    ,							A_=0.02    ,							A_=2    ,							A_=True    ,							A_=True    ,							A_=False    ,							A_=5    ,							A_=1.0    ,							A_=False    ,							A_=True    ,							A_=1    ,							A_=0    ,							A_=2    ,							A_=2    ,							**A_    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			langs
      _SCREAMING_SNAKE_CASE        =			src_vocab_size
      _SCREAMING_SNAKE_CASE        =			tgt_vocab_size
      _SCREAMING_SNAKE_CASE        =			d_model  # encoder_embed_dim and decoder_embed_dim

      _SCREAMING_SNAKE_CASE        =			encoder_ffn_dim
      _SCREAMING_SNAKE_CASE        =			_SCREAMING_SNAKE_CASE        =			encoder_layers
      _SCREAMING_SNAKE_CASE        =			encoder_attention_heads
      _SCREAMING_SNAKE_CASE        =			encoder_layerdrop
      _SCREAMING_SNAKE_CASE        =			decoder_layerdrop
      _SCREAMING_SNAKE_CASE        =			decoder_ffn_dim
      _SCREAMING_SNAKE_CASE        =			decoder_layers
      _SCREAMING_SNAKE_CASE        =			decoder_attention_heads
      _SCREAMING_SNAKE_CASE        =			max_position_embeddings
      _SCREAMING_SNAKE_CASE        =			init_std  # Normal(0, this parameter)
      _SCREAMING_SNAKE_CASE        =			activation_function

      _SCREAMING_SNAKE_CASE        =			DecoderConfig(vocab_size=A_    ,							bos_token_id=A_			)
      if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

      _SCREAMING_SNAKE_CASE        =			scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      _SCREAMING_SNAKE_CASE        =			attention_dropout
      _SCREAMING_SNAKE_CASE        =			activation_dropout
      _SCREAMING_SNAKE_CASE        =			dropout

      _SCREAMING_SNAKE_CASE        =			use_cache
      super().__init__(
          pad_token_id=A_    ,							bos_token_id=A_    ,							eos_token_id=A_    ,							decoder_start_token_id=A_    ,							is_encoder_decoder=A_    ,							tie_word_embeddings=A_    ,							forced_eos_token_id=A_    ,							max_length=A_    ,							num_beams=A_    ,							length_penalty=A_    ,							early_stopping=A_    ,							**A_    ,							)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			copy.deepcopy(self.__dict__			)
      _SCREAMING_SNAKE_CASE        =			self.decoder.to_dict()
      _SCREAMING_SNAKE_CASE        =			self.__class__.model_type
      return output





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

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

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
