





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   UpperCamelCase_       (     __lowercase				,  __lowercase				,  unittest.TestCase    ):
							lowerCamelCase_           =					StableDiffusionXLImgaImgPipeline
							lowerCamelCase_           =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
							lowerCamelCase_           =					PipelineTesterMixin.required_optional_params - {'''latents'''}
							lowerCamelCase_           =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCamelCase_           =					IMAGE_TO_IMAGE_IMAGE_PARAMS
							lowerCamelCase_           =					IMAGE_TO_IMAGE_IMAGE_PARAMS


							def      _snake_case  (							self    :List[str]		)   ->   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""")							,				attention_head_dim=(2, 4)							,				use_linear_projection=__a							,				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							,				)
									SCREAMING_SNAKE_CASE__			=      EulerDiscreteScheduler(
									    beta_start=0.0_0_0_8_5							,				beta_end=0.0_1_2							,				steps_offset=1							,				beta_schedule="""scaled_linear"""							,				timestep_spacing="""leading"""							,				)
									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							,				sample_size=128							,				)
									torch.manual_seed(0		)
									SCREAMING_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=1000							,				hidden_act="""gelu"""							,				projection_dim=32							,				)
									SCREAMING_SNAKE_CASE__			=      CLIPTextModel(__a		)
									SCREAMING_SNAKE_CASE__			=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,				local_files_only=__a		)

									SCREAMING_SNAKE_CASE__			=      CLIPTextModelWithProjection(__a		)
									SCREAMING_SNAKE_CASE__			=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,				local_files_only=__a		)

									SCREAMING_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      _snake_case  (							self    :Any							,				__A    :str							,				__A    :Tuple=0		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      floats_tensor((1, 3, 32, 32)							,				rng=random.Random(__a		)		).to(__a		)
									SCREAMING_SNAKE_CASE__			=      image / 2 + 0.5
									if str(__a		).startswith("""mps"""		):
											SCREAMING_SNAKE_CASE__			=      torch.manual_seed(__a		)
									else:
											SCREAMING_SNAKE_CASE__			=      torch.Generator(device=__a		).manual_seed(__a		)
									SCREAMING_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""": 0.7_5,
									}
									return inputs


							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=      StableDiffusionXLImgaImgPipeline(**__a		)
									SCREAMING_SNAKE_CASE__			=      sd_pipe.to(__a		)
									sd_pipe.set_progress_bar_config(disable=__a		)

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

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

									SCREAMING_SNAKE_CASE__			=      np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]		)

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


							def      _snake_case  (							self    :Optional[int]		)   ->   Union[str, Any]:



									"""simple docstring"""



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


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



									"""simple docstring"""



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


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



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=      StableDiffusionXLImgaImgPipeline(**__a		)
									SCREAMING_SNAKE_CASE__			=      sd_pipe.to(__a		)
									SCREAMING_SNAKE_CASE__			=      sd_pipe.to(__a		)
									sd_pipe.set_progress_bar_config(disable=__a		)

									# forward without prompt embeds
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_inputs(__a		)
									SCREAMING_SNAKE_CASE__			=      3 * ["""this is a negative prompt"""]
									SCREAMING_SNAKE_CASE__			=      negative_prompt
									SCREAMING_SNAKE_CASE__			=      3 * [inputs["""prompt"""]]

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

									# forward with prompt embeds
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_inputs(__a		)
									SCREAMING_SNAKE_CASE__			=      3 * ["""this is a negative prompt"""]
									SCREAMING_SNAKE_CASE__			=      3 * [inputs.pop("""prompt"""		)]

									(
									    SCREAMING_SNAKE_CASE__			
									)			=      sd_pipe.encode_prompt(__a							,				negative_prompt=__a		)

									SCREAMING_SNAKE_CASE__			=      sd_pipe(
									    **__a							,				prompt_embeds=__a							,				negative_prompt_embeds=__a							,				pooled_prompt_embeds=__a							,				negative_pooled_prompt_embeds=__a							,				)
									SCREAMING_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   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :Dict		)   ->   str:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :Any							,				__A    :Any="cpu"							,				__A    :str=torch.floataa							,				__A    :Any=0		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      torch.Generator(device=__a		).manual_seed(__a		)
									SCREAMING_SNAKE_CASE__			=      np.random.RandomState(__a		).standard_normal((1, 4, 64, 64)		)
									SCREAMING_SNAKE_CASE__			=      torch.from_numpy(__a		).to(device=__a							,				dtype=__a		)
									SCREAMING_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      _snake_case  (							self    :Optional[int]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""		)
									pipe.to(__a		)
									pipe.set_progress_bar_config(disable=__a		)

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

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__			=      np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]		)
									assert np.abs(image_slice - expected_slice		).max() < 7E-3









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

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