

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	StableDiffusionXLImgaImgPipeline
			__A    	=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
			__A    	=	PipelineTesterMixin.required_optional_params - {"""latents"""}
			__A    	=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__A    	=	IMAGE_TO_IMAGE_IMAGE_PARAMS
			__A    	=	IMAGE_TO_IMAGE_IMAGE_PARAMS


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				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')			,					attention_head_dim=(2, 4)			,					use_linear_projection=__UpperCamelCase			,					addition_embed_type='text_time'			,					addition_time_embed_dim=8			,					transformer_layers_per_block=(1, 2)			,					projection_class_embeddings_input_dim=80			,					cross_attention_dim=64			,					)
				snake_case_   		=			EulerDiscreteScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					steps_offset=1			,					beta_schedule='scaled_linear'			,					timestep_spacing='leading'			,					)
				torch.manual_seed(0				)
				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			,					sample_size=1_28			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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='gelu'			,					projection_dim=32			,					)
				snake_case_   		=			CLIPTextModel(__UpperCamelCase				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,					local_files_only=__UpperCamelCase				)

				snake_case_   		=			CLIPTextModelWithProjection(__UpperCamelCase				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,					local_files_only=__UpperCamelCase				)

				snake_case_   		=			{
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'text_encoder_2': text_encoder_a,
				    'tokenizer_2': tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image / 2 + 0.5
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 5.0,
				    'output_type': 'numpy',
				    'strength': 0.75,
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			StableDiffusionXLImgaImgPipeline(**__UpperCamelCase				)
				snake_case_   		=			sd_pipe.to(__UpperCamelCase				)
				sd_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			sd_pipe(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -3:, -3:, -1]

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

				snake_case_   		=			np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]				)

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			StableDiffusionXLImgaImgPipeline(**__UpperCamelCase				)
				snake_case_   		=			sd_pipe.to(__UpperCamelCase				)
				snake_case_   		=			sd_pipe.to(__UpperCamelCase				)
				sd_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				# forward without prompt embeds
				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * ['this is a negative prompt']
				snake_case_   		=			negative_prompt
				snake_case_   		=			3 * [inputs['prompt']]

				snake_case_   		=			sd_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * ['this is a negative prompt']
				snake_case_   		=			3 * [inputs.pop('prompt'				)]

				(
				    (
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							
				)   		=			sd_pipe.encode_prompt(__UpperCamelCase			,					negative_prompt=__UpperCamelCase				)

				snake_case_   		=			sd_pipe(
				    **__UpperCamelCase			,					prompt_embeds=__UpperCamelCase			,					negative_prompt_embeds=__UpperCamelCase			,					pooled_prompt_embeds=__UpperCamelCase			,					negative_pooled_prompt_embeds=__UpperCamelCase			,					)
				snake_case_   		=			output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase="cpu"			,					__UpperCamelCase=torch.floataa			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			np.random.RandomState(__UpperCamelCase				).standard_normal((1, 4, 64, 64)				)
				snake_case_   		=			torch.from_numpy(__UpperCamelCase				).to(device=__UpperCamelCase			,					dtype=__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'a photograph of an astronaut riding a horse',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 7.5,
				    'output_type': 'numpy',
				}
				return inputs







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				snake_case_   		=			np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]				)
				assert np.abs(image_slice - expected_slice				).max() < 7E-3



from typing import TYPE_CHECKING

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


A									=  {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextTokenizer']

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextFeatureExtractor']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSpeech2TextForConditionalGeneration',
		    'TFSpeech2TextModel',
		    'TFSpeech2TextPreTrainedModel',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2TextForConditionalGeneration',
		    'Speech2TextModel',
		    'Speech2TextPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
		from .processing_speech_to_text import SpeechaTextProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speech_to_text import SpeechaTextTokenizer

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_speech_to_text import (
				    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSpeechaTextForConditionalGeneration,
				    TFSpeechaTextModel,
				    TFSpeechaTextPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text import (
				    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextForConditionalGeneration,
				    SpeechaTextModel,
				    SpeechaTextPreTrainedModel,
				)

else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
