
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A      :	Optional[Any]       		=	logging.get_logger(__name__)

_A      :	Any       		=	{
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """flaubert"""
   __lowerCAmelCase						=    {
       """hidden_size""": """emb_dim""",
       """num_attention_heads""": """n_heads""",
       """num_hidden_layers""": """n_layers""",
       """n_words""": """vocab_size""",  # For backward compatibility
   }
   def __init__(      self     ,   _a=False     ,   _a=0.0     ,   _a=30_145     ,   _a=2_048     ,   _a=12     ,   _a=16     ,   _a=0.1     ,   _a=0.1     ,   _a=True     ,   _a=False     ,   _a=False     ,   _a=False     ,   _a=1     ,   _a=True     ,   _a=512     ,   _a=2_048**-0.5     ,   _a=1E-12     ,   _a=0.0_2     ,   _a=0     ,   _a=1     ,   _a=2     ,   _a=3     ,   _a=5     ,   _a=True     ,   _a="first"     ,   _a=True     ,   _a=None     ,   _a=True     ,   _a=0.1     ,   _a=5     ,   _a=5     ,   _a=0     ,   _a=0     ,   _a=2     ,   _a=0     ,   **_a     ,   ):
          lowercase   :    Union[str, Any]			=							pre_norm
          lowercase   :    int			=							layerdrop
          lowercase   :    List[Any]			=							vocab_size
          lowercase   :    int			=							emb_dim
          lowercase   :    Union[str, Any]			=							n_layers
          lowercase   :    List[str]			=							n_heads
          lowercase   :    int			=							dropout
          lowercase   :    Optional[int]			=							attention_dropout
          lowercase   :    Dict			=							gelu_activation
          lowercase   :    List[str]			=							sinusoidal_embeddings
          lowercase   :    List[str]			=							causal
          lowercase   :    Optional[Any]			=							asm
          lowercase   :    Tuple			=							n_langs
          lowercase   :    Optional[Any]			=							use_lang_emb
          lowercase   :    List[str]			=							layer_norm_eps
          lowercase   :    str			=							bos_index
          lowercase   :    Dict			=							eos_index
          lowercase   :    Union[str, Any]			=							pad_index
          lowercase   :    int			=							unk_index
          lowercase   :    str			=							mask_index
          lowercase   :    Union[str, Any]			=							is_encoder
          lowercase   :    List[str]			=							max_position_embeddings
          lowercase   :    Union[str, Any]			=							embed_init_std
          lowercase   :    Any			=							init_std
          lowercase   :    Tuple			=							summary_type
          lowercase   :    Tuple			=							summary_use_proj
          lowercase   :    int			=							summary_activation
          lowercase   :    Optional[Any]			=							summary_proj_to_labels
          lowercase   :    Optional[Any]			=							summary_first_dropout
          lowercase   :    List[Any]			=							start_n_top
          lowercase   :    Tuple			=							end_n_top
          lowercase   :    List[str]			=							mask_token_id
          lowercase   :    Union[str, Any]			=							lang_id

          if "n_words" in kwargs:
                 lowercase   :    Any			=							kwargs["n_words"]

          super().__init__(pad_token_id=_a     ,   bos_token_id=_a     ,   **_a    )


class 		a__						(				a_						):
   @property
   def       __magic_name__      (      self    ):
          if self.task == "multiple-choice":
                 lowercase   :    Dict			=							{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase   :    Any			=							{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]    )




"""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 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							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       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

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

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 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       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							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 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


