
"""simple docstring"""




from typing import Mapping

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


_A      :	Any       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   __lowerCAmelCase						=    """mt5"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a=250_112     ,   _a=512     ,   _a=64     ,   _a=1_024     ,   _a=8     ,   _a=None     ,   _a=6     ,   _a=32     ,   _a=128     ,   _a=0.1     ,   _a=1E-6     ,   _a=1.0     ,   _a="gated-gelu"     ,   _a=True     ,   _a=True     ,   _a="T5Tokenizer"     ,   _a=False     ,   _a=0     ,   _a=1     ,   _a=0     ,   **_a     ,   ):
          super().__init__(
              is_encoder_decoder=_a     ,   tokenizer_class=_a     ,   tie_word_embeddings=_a     ,   pad_token_id=_a     ,   eos_token_id=_a     ,   decoder_start_token_id=_a     ,   **_a     ,   )
          lowercase   :    Dict			=							vocab_size
          lowercase   :    Optional[int]			=							d_model
          lowercase   :    Optional[int]			=							d_kv
          lowercase   :    Optional[Any]			=							d_ff
          lowercase   :    Dict			=							num_layers
          lowercase   :    Dict			=							(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          lowercase   :    List[str]			=							num_heads
          lowercase   :    Optional[Any]			=							relative_attention_num_buckets
          lowercase   :    Dict			=							relative_attention_max_distance
          lowercase   :    Optional[int]			=							dropout_rate
          lowercase   :    Any			=							layer_norm_epsilon
          lowercase   :    Union[str, Any]			=							initializer_factor
          lowercase   :    Optional[int]			=							feed_forward_proj
          lowercase   :    int			=							use_cache

          lowercase   :    List[Any]			=							self.feed_forward_proj.split("-"    )
          lowercase   :    Any			=							act_info[-1]
          lowercase   :    Tuple			=							act_info[0] == "gated"

          if len(_a    ) > 1 and act_info[0] != "gated" or len(_a    ) > 2:
                 raise ValueError(
                     f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                     "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                     "'gated-gelu' or 'relu'"    )

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
                 lowercase   :    Optional[int]			=							"gelu_new"
   @property
   def       __magic_name__      (      self    ):
          return self.d_model
   @property
   def       __magic_name__      (      self    ):
          return self.num_heads

   @property
   def       __magic_name__      (      self    ):
          return self.num_layers


class 		a__						(				a_						):
   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							{
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
                 lowercase   :    Any			=							"past_encoder_sequence + sequence"
                 lowercase   :    List[str]			=							{0: "batch"}
                 lowercase   :    Union[str, Any]			=							{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
                 lowercase   :    int			=							{0: "batch", 1: "decoder_sequence"}
                 lowercase   :    List[Any]			=							{0: "batch", 1: "decoder_sequence"}

          if self.use_past:
                 self.fill_with_past_key_values_(_a     ,   direction="inputs"    )

          return common_inputs
   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def       __magic_name__      (      self    ):
          return 13

   @property
   def       __magic_name__      (      self    ):
          return 5E-4




"""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


