




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class    lowercase_		(_snake_case ):



						def __init__(			self	:   int					,							snake_case__	:   List[Any]					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								super().__init__()

								# make sure scheduler can always be converted to DDIM
								SCREAMING_SNAKE_CASE_					      =							DDIMScheduler.from_config(scheduler.config  )

								self.register_modules(unet=__lowerCamelCase					,							scheduler=__lowerCamelCase  )






						@torch.no_grad()
						def __call__(			self	:   str					,							snake_case__	:   int = 1					,							snake_case__	:   Optional[Union[torch.Generator, List[torch.Generator]]] = None					,							snake_case__	:   float = 0.0					,							snake_case__	:   int = 50					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[str] = "pil"					,							snake_case__	:   bool = True					,							):

								"""simple docstring"""

								if isinstance(self.unet.config.sample_size					,							__lowerCamelCase  ):
										SCREAMING_SNAKE_CASE_					      =							(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
								else:
										SCREAMING_SNAKE_CASE_					      =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(__lowerCamelCase					,							__lowerCamelCase  ) and len(__lowerCamelCase  ) != batch_size:
										raise ValueError(
										    f'''You have passed a list of generators of length {len(__lowerCamelCase  )}, but requested an effective batch'''
										    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

								SCREAMING_SNAKE_CASE_					      =							randn_tensor(__lowerCamelCase					,							generator=__lowerCamelCase					,							device=self.device					,							dtype=self.unet.dtype  )

								# set step values
								self.scheduler.set_timesteps(__lowerCamelCase  )

								for t in self.progress_bar(self.scheduler.timesteps  ):
										# 1. predict noise model_output
										SCREAMING_SNAKE_CASE_					      =							self.unet(__lowerCamelCase					,							__lowerCamelCase  ).sample

										# 2. predict previous mean of image x_t-1 and add variance depending on eta
										# eta corresponds to η in paper and should be between [0, 1]
										# do x_t -> x_t-1
										SCREAMING_SNAKE_CASE_					      =							self.scheduler.step(
										    __lowerCamelCase					,							__lowerCamelCase					,							__lowerCamelCase					,							eta=__lowerCamelCase					,							use_clipped_model_output=__lowerCamelCase					,							generator=__lowerCamelCase  ).prev_sample

								SCREAMING_SNAKE_CASE_					      =							(image / 2 + 0.5).clamp(0					,							1  )
								SCREAMING_SNAKE_CASE_					      =							image.cpu().permute(0					,							2					,							3					,							1  ).numpy()
								if output_type == "pil":
										SCREAMING_SNAKE_CASE_					      =							self.numpy_to_pil(__lowerCamelCase  )

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=__lowerCamelCase  )



'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



