import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Dict      )					->				Tuple:



												'''simple docstring'''

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




							@property
							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       1
												SCREAMING_SNAKE_CASE    			=       3
												SCREAMING_SNAKE_CASE    			=       (32, 32)

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




							@property
							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       UNetaDConditionModel(
												    block_out_channels=(32, 64)	,							layers_per_block=2	,							sample_size=32	,							in_channels=4	,							out_channels=4	,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,							cross_attention_dim=32	,							)
												return model




							@property
							def 	__snake_case(						self   :					Tuple      )					->				Any:



												'''simple docstring'''

												torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       AutoencoderKL(
												    block_out_channels=[32, 64]	,							in_channels=3	,							out_channels=3	,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,							latent_channels=4	,							)
												return model




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       RobertaSeriesConfig(
												    hidden_size=32	,							project_dim=32	,							intermediate_size=37	,							layer_norm_eps=1e-05	,							num_attention_heads=4	,							num_hidden_layers=5	,							pad_token_id=1	,							vocab_size=5_006	,							)
												return RobertaSeriesModelWithTransformation(_UpperCamelCase      )




							@property
							def 	__snake_case(						self   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												def extract(*_UpperCamelCase   :					List[str]	,							**_UpperCamelCase   :					Dict      ):


																	class 						lowercase    :




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



																													'''simple docstring'''

																													SCREAMING_SNAKE_CASE    			=       torch.ones([0]      )





																								def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Optional[Any]      )					->				Optional[int]:



																													'''simple docstring'''

																													self.pixel_values.to(_UpperCamelCase      )
																													return self



																	return Out()

												return extract




							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE    			=       self.dummy_cond_unet
												SCREAMING_SNAKE_CASE    			=       PNDMScheduler(skip_prk_steps=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.dummy_vae
												SCREAMING_SNAKE_CASE    			=       self.dummy_text_encoder
												SCREAMING_SNAKE_CASE    			=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
												SCREAMING_SNAKE_CASE    			=       77

												SCREAMING_SNAKE_CASE    			=       self.dummy_image.to(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       init_image / 2 + 0.5

												# make sure here that pndm scheduler skips prk
												SCREAMING_SNAKE_CASE    			=       AltDiffusionImgaImgPipeline(
												    unet=_UpperCamelCase	,							scheduler=_UpperCamelCase	,							vae=_UpperCamelCase	,							text_encoder=_UpperCamelCase	,							tokenizer=_UpperCamelCase	,							safety_checker=_UpperCamelCase	,							feature_extractor=self.dummy_extractor	,							)
												SCREAMING_SNAKE_CASE    			=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,							do_normalize=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       alt_pipe.to(_UpperCamelCase      )
												alt_pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "A painting of a squirrel eating a burger"
												SCREAMING_SNAKE_CASE    			=       torch.Generator(device=_UpperCamelCase      ).manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       alt_pipe(
												    [prompt]	,							generator=_UpperCamelCase	,							guidance_scale=6.0	,							num_inference_steps=2	,							output_type="np"	,							image=_UpperCamelCase	,							)

												SCREAMING_SNAKE_CASE    			=       output.images

												SCREAMING_SNAKE_CASE    			=       torch.Generator(device=_UpperCamelCase      ).manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       alt_pipe(
												    [prompt]	,							generator=_UpperCamelCase	,							guidance_scale=6.0	,							num_inference_steps=2	,							output_type="np"	,							image=_UpperCamelCase	,							return_dict=_UpperCamelCase	,							)[0]

												SCREAMING_SNAKE_CASE    			=       image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE    			=       image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]      )

												assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5e-3




							@unittest.skipIf(torch_device != "cuda"	,							"This test requires a GPU"      )
							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.dummy_cond_unet
												SCREAMING_SNAKE_CASE    			=       PNDMScheduler(skip_prk_steps=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.dummy_vae
												SCREAMING_SNAKE_CASE    			=       self.dummy_text_encoder
												SCREAMING_SNAKE_CASE    			=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
												SCREAMING_SNAKE_CASE    			=       77

												SCREAMING_SNAKE_CASE    			=       self.dummy_image.to(_UpperCamelCase      )

												# put models in fp16
												SCREAMING_SNAKE_CASE    			=       unet.half()
												SCREAMING_SNAKE_CASE    			=       vae.half()
												SCREAMING_SNAKE_CASE    			=       bert.half()

												# make sure here that pndm scheduler skips prk
												SCREAMING_SNAKE_CASE    			=       AltDiffusionImgaImgPipeline(
												    unet=_UpperCamelCase	,							scheduler=_UpperCamelCase	,							vae=_UpperCamelCase	,							text_encoder=_UpperCamelCase	,							tokenizer=_UpperCamelCase	,							safety_checker=_UpperCamelCase	,							feature_extractor=self.dummy_extractor	,							)
												SCREAMING_SNAKE_CASE    			=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,							do_normalize=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       alt_pipe.to(_UpperCamelCase      )
												alt_pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "A painting of a squirrel eating a burger"
												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       alt_pipe(
												    [prompt]	,							generator=_UpperCamelCase	,							num_inference_steps=2	,							output_type="np"	,							image=_UpperCamelCase	,							).images

												assert image.shape == (1, 32, 32, 3)





							@unittest.skipIf(torch_device != "cuda"	,							"This test requires a GPU"      )
							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"      )
												# resize to resolution that is divisible by 8 but not 16 or 32
												SCREAMING_SNAKE_CASE    			=       init_image.resize((760, 504)      )

												SCREAMING_SNAKE_CASE    			=       "BAAI/AltDiffusion"
												SCREAMING_SNAKE_CASE    			=       AltDiffusionImgaImgPipeline.from_pretrained(
												    _UpperCamelCase	,							safety_checker=_UpperCamelCase	,							)
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE    			=       "A fantasy landscape, trending on artstation"

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(
												    prompt=_UpperCamelCase	,							image=_UpperCamelCase	,							strength=0.7_5	,							guidance_scale=7.5	,							generator=_UpperCamelCase	,							output_type="np"	,							)
												SCREAMING_SNAKE_CASE    			=       output.images[0]

												SCREAMING_SNAKE_CASE    			=       image[255:258, 383:386, -1]

												assert image.shape == (504, 760, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]      )

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



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




							def 	__snake_case(						self   :					Tuple      )					->				Optional[int]:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"      )
												SCREAMING_SNAKE_CASE    			=       init_image.resize((768, 512)      )
												SCREAMING_SNAKE_CASE    			=       load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"      )

												SCREAMING_SNAKE_CASE    			=       "BAAI/AltDiffusion"
												SCREAMING_SNAKE_CASE    			=       AltDiffusionImgaImgPipeline.from_pretrained(
												    _UpperCamelCase	,							safety_checker=_UpperCamelCase	,							)
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE    			=       "A fantasy landscape, trending on artstation"

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(
												    prompt=_UpperCamelCase	,							image=_UpperCamelCase	,							strength=0.7_5	,							guidance_scale=7.5	,							generator=_UpperCamelCase	,							output_type="np"	,							)
												SCREAMING_SNAKE_CASE    			=       output.images[0]

												assert image.shape == (512, 768, 3)
												# img2img is flaky across GPUs even in fp32, so using MAE here
												assert np.abs(expected_image - image      ).max() < 1e-2





from typing import TYPE_CHECKING

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


_lowerCamelCase					:   Optional[int]							=		{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[Any]							=		['''BlenderbotTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Tuple							=		[
							    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BlenderbotForCausalLM''',
							    '''BlenderbotForConditionalGeneration''',
							    '''BlenderbotModel''',
							    '''BlenderbotPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[str]							=		[
							    '''TFBlenderbotForConditionalGeneration''',
							    '''TFBlenderbotModel''',
							    '''TFBlenderbotPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		[
							    '''FlaxBlenderbotForConditionalGeneration''',
							    '''FlaxBlenderbotModel''',
							    '''FlaxBlenderbotPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							_lowerCamelCase					:   Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




