
'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     DDIMPipeline
					lowerCAmelCase_        =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					lowerCAmelCase_        =     PipelineTesterMixin.required_optional_params - {
					    '''num_images_per_prompt''',
					    '''latents''',
					    '''callback''',
					    '''callback_steps''',
					}
					lowerCAmelCase_        =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											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''')				,							)
											lowercase_	:     List[str]													=       DDIMScheduler()
											lowercase_	:     Dict													=       {'''unet''': unet, '''scheduler''': scheduler}
											return components






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_	:     List[str]													=       torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     List[Any]													=       torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       {
											    '''batch_size''': 1,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       '''cpu'''

											lowercase_	:     Optional[int]													=       self.get_dummy_components()
											lowercase_	:     Union[str, Any]													=       self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Dict													=       self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       pipe(**__SCREAMING_SNAKE_CASE		).images
											lowercase_	:     int													=       image[0, -3:, -3:, -1]

											self.assertEqual(image.shape				,							(1, 32, 32, 3)		)
											lowercase_	:     Union[str, Any]													=       np.array(
											    [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4]		)
											lowercase_	:     Tuple													=       np.abs(image_slice.flatten() - expected_slice		).max()
											self.assertLessEqual(__SCREAMING_SNAKE_CASE				,							1E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_save_load_local(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_save_load_optional_components(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       '''google/ddpm-cifar10-32'''

											lowercase_	:     Union[str, Any]													=       UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       DDIMScheduler()

											lowercase_	:     Union[str, Any]													=       DDIMPipeline(unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE		)
											ddim.to(__SCREAMING_SNAKE_CASE		)
											ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       torch.manual_seed(0		)
											lowercase_	:     Tuple													=       ddim(generator=__SCREAMING_SNAKE_CASE				,							eta=0.0				,							output_type='''numpy'''		).images

											lowercase_	:     Union[str, Any]													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											lowercase_	:     Union[str, Any]													=       np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]		)

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       '''google/ddpm-ema-bedroom-256'''

											lowercase_	:     Dict													=       UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       DDIMPipeline(unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE		)
											ddpm.to(__SCREAMING_SNAKE_CASE		)
											ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       torch.manual_seed(0		)
											lowercase_	:     str													=       ddpm(generator=__SCREAMING_SNAKE_CASE				,							output_type='''numpy'''		).images

											lowercase_	:     Optional[Any]													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_56, 2_56, 3)
											lowercase_	:     Tuple													=       np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]		)

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




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.base_model(
             input_ids=__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  token_type_ids=__UpperCAmelCase       ,  position_ids=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  inputs_embeds=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  encoder_attention_mask=__UpperCAmelCase       ,  output_attentions=__UpperCAmelCase       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=__UpperCAmelCase       ,  )

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
         else:
               UpperCAmelCase__								=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



