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    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						DDIMPipeline
			__a  : int			 =						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			__a  : Tuple			 =						PipelineTesterMixin.required_optional_params - {
			    '''num_images_per_prompt''',
			    '''latents''',
			    '''callback''',
			    '''callback_steps''',
			}
			__a  : str			 =						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			__a  : Tuple			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							torch.manual_seed(0						)
							__lowercase 			=							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 			=							DDIMScheduler()
							__lowercase 			=							{'''unet''': unet, '''scheduler''': scheduler}
							return components





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=0						)     ->					Union[str, Any]:

							'''simple docstring'''


							if str(lowerCAmelCase__						).startswith('''mps'''						):
											__lowercase 			=							torch.manual_seed(lowerCAmelCase__						)
							else:
											__lowercase 			=							torch.Generator(device=lowerCAmelCase__						).manual_seed(lowerCAmelCase__						)
							__lowercase 			=							{
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							'''cpu'''

							__lowercase 			=							self.get_dummy_components()
							__lowercase 			=							self.pipeline_class(**lowerCAmelCase__						)
							pipe.to(lowerCAmelCase__						)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__						)

							__lowercase 			=							self.get_dummy_inputs(lowerCAmelCase__						)
							__lowercase 			=							pipe(**lowerCAmelCase__						).images
							__lowercase 			=							image[0, -3:, -3:, -1]

							self.assertEqual(image.shape      ,   (1, 32, 32, 3)						)
							__lowercase 			=							np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]						)
							__lowercase 			=							np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(lowerCAmelCase__      ,   1E-3						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


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




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


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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

							__lowercase 			=							UNetaDModel.from_pretrained(lowerCAmelCase__						)
							__lowercase 			=							DDIMScheduler()

							__lowercase 			=							DDIMPipeline(unet=lowerCAmelCase__      ,   scheduler=lowerCAmelCase__						)
							ddim.to(lowerCAmelCase__						)
							ddim.set_progress_bar_config(disable=lowerCAmelCase__						)

							__lowercase 			=							torch.manual_seed(0						)
							__lowercase 			=							ddim(generator=lowerCAmelCase__      ,   eta=0.0      ,   output_type='''numpy'''						).images

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

							assert image.shape == (1, 32, 32, 3)
							__lowercase 			=							np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]						)

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							'''google/ddpm-ema-bedroom-256'''

							__lowercase 			=							UNetaDModel.from_pretrained(lowerCAmelCase__						)
							__lowercase 			=							DDIMScheduler.from_pretrained(lowerCAmelCase__						)

							__lowercase 			=							DDIMPipeline(unet=lowerCAmelCase__      ,   scheduler=lowerCAmelCase__						)
							ddpm.to(lowerCAmelCase__						)
							ddpm.set_progress_bar_config(disable=lowerCAmelCase__						)

							__lowercase 			=							torch.manual_seed(0						)
							__lowercase 			=							ddpm(generator=lowerCAmelCase__      ,   output_type='''numpy'''						).images

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

							assert image.shape == (1, 2_56, 2_56, 3)
							__lowercase 			=							np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)