import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:]							)




class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : List[Any]			 =						StableDiffusionLatentUpscalePipeline
			__a  : Any			 =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    '''height''',
			    '''width''',
			    '''cross_attention_kwargs''',
			    '''negative_prompt_embeds''',
			    '''prompt_embeds''',
			}
			__a  : Dict			 =						PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
			__a  : Any			 =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__a  : Union[str, Any]			 =						frozenset(
			    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__a  : int			 =						frozenset([] )

			__a  : List[Any]			 =						True





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							1
							__lowercase 			=							4
							__lowercase 			=							(16, 16)

							__lowercase 			=							floats_tensor((batch_size, num_channels) + sizes      ,   rng=random.Random(0						)						).to(lowerCAmelCase__						)
							return image





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							torch.manual_seed(0						)
							__lowercase 			=							UNetaDConditionModel(
							    act_fn='''gelu'''      ,   attention_head_dim=8      ,   norm_num_groups=lowerCAmelCase__      ,   block_out_channels=[32, 32, 64, 64]      ,   time_cond_proj_dim=1_60      ,   conv_in_kernel=1      ,   conv_out_kernel=1      ,   cross_attention_dim=32      ,   down_block_types=(
							        '''KDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							    )      ,   in_channels=8      ,   mid_block_type=lowerCAmelCase__      ,   only_cross_attention=lowerCAmelCase__      ,   out_channels=5      ,   resnet_time_scale_shift='''scale_shift'''      ,   time_embedding_type='''fourier'''      ,   timestep_post_act='''gelu'''      ,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')      ,   )
							__lowercase 			=							AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=[
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							    ]      ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,   latent_channels=4      ,   )
							__lowercase 			=							EulerDiscreteScheduler(prediction_type='''sample'''						)
							__lowercase 			=							CLIPTextConfig(
							    bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1E-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=10_00      ,   hidden_act='''quick_gelu'''      ,   projection_dim=5_12      ,   )
							__lowercase 			=							CLIPTextModel(lowerCAmelCase__						)
							__lowercase 			=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

							__lowercase 			=							{
							    '''unet''': model.eval(),
							    '''vae''': vae.eval(),
							    '''scheduler''': scheduler,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							}

							return components





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=0						)     ->					str:

							'''simple docstring'''


							if str(lowerCAmelCase__						).startswith('''mps'''						):
											__lowercase 			=							torch.manual_seed(lowerCAmelCase__						)
							else:
											__lowercase 			=							torch.Generator(device=lowerCAmelCase__						).manual_seed(lowerCAmelCase__						)
							__lowercase 			=							{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': self.dummy_image.cpu(),
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs





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

							'''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, 2_56, 2_56, 3)						)
							__lowercase 			=							np.array(
							    [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]						)
							__lowercase 			=							np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(lowerCAmelCase__      ,   1E-3						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3						)





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					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_inference_batch_single_identical(expected_max_diff=7E-3						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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





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

							'''simple docstring'''


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





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

							'''simple docstring'''


							__lowercase 			=							[
							    '''DDIMScheduler''',
							    '''DDPMScheduler''',
							    '''PNDMScheduler''',
							    '''HeunDiscreteScheduler''',
							    '''EulerAncestralDiscreteScheduler''',
							    '''KDPM2DiscreteScheduler''',
							    '''KDPM2AncestralDiscreteScheduler''',
							    '''DPMSolverSDEScheduler''',
							]
							__lowercase 			=							self.get_dummy_components()
							__lowercase 			=							self.pipeline_class(**lowerCAmelCase__						)

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=lowerCAmelCase__						)

							pipe.to(lowerCAmelCase__						)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__						)
							__lowercase 			=							self.get_dummy_inputs(lowerCAmelCase__						)
							__lowercase 			=							2

							__lowercase 			=							[]
							for scheduler_enum in KarrasDiffusionSchedulers:
											if scheduler_enum.name in skip_schedulers:
															# no sigma schedulers are not supported
															# no schedulers
															continue

											__lowercase 			=							getattr(lowerCAmelCase__      ,   scheduler_enum.name						)
											__lowercase 			=							scheduler_cls.from_config(pipe.scheduler.config						)
											__lowercase 			=							pipe(**lowerCAmelCase__						)[0]
											outputs.append(lowerCAmelCase__						)

							assert check_same_shape(lowerCAmelCase__						)




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


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





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

							'''simple docstring'''


							__lowercase 			=							torch.manual_seed(33						)

							__lowercase 			=							StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''      ,   torch_dtype=torch.floataa						)
							pipe.to('''cuda'''						)

							__lowercase 			=							StableDiffusionLatentUpscalePipeline.from_pretrained(
							    '''stabilityai/sd-x2-latent-upscaler'''      ,   torch_dtype=torch.floataa						)
							upscaler.to('''cuda'''						)

							__lowercase 			=							'''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

							__lowercase 			=							pipe(lowerCAmelCase__      ,   generator=lowerCAmelCase__      ,   output_type='''latent'''						).images

							__lowercase 			=							upscaler(
							    prompt=lowerCAmelCase__      ,   image=lowerCAmelCase__      ,   num_inference_steps=20      ,   guidance_scale=0      ,   generator=lowerCAmelCase__      ,   output_type='''np'''      ,   ).images[0]

							__lowercase 			=							load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''						)
							assert np.abs((expected_image - image).mean()						) < 5E-2





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

							'''simple docstring'''


							__lowercase 			=							torch.manual_seed(33						)

							__lowercase 			=							StableDiffusionLatentUpscalePipeline.from_pretrained(
							    '''stabilityai/sd-x2-latent-upscaler'''      ,   torch_dtype=torch.floataa						)
							upscaler.to('''cuda'''						)

							__lowercase 			=							'''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

							__lowercase 			=							load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''						)

							__lowercase 			=							upscaler(
							    prompt=lowerCAmelCase__      ,   image=lowerCAmelCase__      ,   num_inference_steps=20      ,   guidance_scale=0      ,   generator=lowerCAmelCase__      ,   output_type='''np'''      ,   ).images[0]

							__lowercase 			=							load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''						)
							assert np.abs((expected_image - image).max()						) < 5E-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),
							    ]						)