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 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = [tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]     )
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






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

 UpperCAmelCase_     			= True




 @property
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = 1
        lowerCAmelCase__					     = 4
        lowerCAmelCase__					     = (16, 16)

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




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__					     = UNetaDConditionModel(
            act_fn='gelu'      ,   attention_head_dim=8      ,   norm_num_groups=lowercase__      ,   block_out_channels=[32, 32, 64, 64]      ,   time_cond_proj_dim=160      ,   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=lowercase__      ,   only_cross_attention=lowercase__      ,   out_channels=5      ,   resnet_time_scale_shift='scale_shift'      ,   time_embedding_type='fourier'      ,   timestep_post_act='gelu'      ,   up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')      ,   )
        lowerCAmelCase__					     = 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      ,   )
        lowerCAmelCase__					     = EulerDiscreteScheduler(prediction_type='sample')
        lowerCAmelCase__					     = 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=1_000      ,   hidden_act='quick_gelu'      ,   projection_dim=512      ,   )
        lowerCAmelCase__					     = CLIPTextModel(lowercase__)
        lowerCAmelCase__					     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

        return components




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  str      ,   lowercase__ :  int=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__					     = torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__					     = torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__					     = {
            '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 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = 'cpu'

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

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

        self.assertEqual(image.shape      ,   (1, 256, 256, 3))
        lowerCAmelCase__					     = np.array(
            [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055])
        lowerCAmelCase__					     = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(lowercase__      ,   1e-3)




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=7e-3)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



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

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

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

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

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

        assert check_same_shape(lowercase__)






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






 '''simple docstring'''






 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = torch.manual_seed(33)

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

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

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

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

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

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = torch.manual_seed(33)

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

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

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

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

        lowerCAmelCase__					     = 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase__     						=			TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase__     						=			TaTokenizerFast

lowerCAmelCase__     						=			{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'MT5EncoderModel',
        'MT5ForConditionalGeneration',
        'MT5ForQuestionAnswering',
        'MT5Model',
        'MT5PreTrainedModel',
        'MT5Stack',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(
        __name__,
        globals()['__file__'],
        _import_structure,
        extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
        module_spec=__spec__,
    )
