
'''simple docstring'''







import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	StableDiffusionXLPipeline
					lowerCAmelCase_	  =	TEXT_TO_IMAGE_PARAMS
					lowerCAmelCase_	  =	TEXT_TO_IMAGE_BATCH_PARAMS
					lowerCAmelCase_	  =	TEXT_TO_IMAGE_IMAGE_PARAMS
					lowerCAmelCase_	  =	TEXT_TO_IMAGE_IMAGE_PARAMS






					def       _snake_case       (  self						):


											"""simple docstring"""





											torch.manual_seed(0						)
											lowercase_      :	Tuple						=			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=__SCREAMING_SNAKE_CASE							,				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							,				)
											lowercase_      :	int						=			EulerDiscreteScheduler(
											    beta_start=0.00_085							,				beta_end=0.012							,				steps_offset=1							,				beta_schedule='''scaled_linear'''							,				timestep_spacing='''leading'''							,				)
											torch.manual_seed(0						)
											lowercase_      :	Optional[Any]						=			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						)
											lowercase_      :	Optional[Any]						=			CLIPTextConfig(
											    bos_token_id=0							,				eos_token_id=2							,				hidden_size=32							,				intermediate_size=37							,				layer_norm_eps=1E-0_5							,				num_attention_heads=4							,				num_hidden_layers=5							,				pad_token_id=1							,				vocab_size=10_00							,				hidden_act='''gelu'''							,				projection_dim=32							,				)
											lowercase_      :	Tuple						=			CLIPTextModel(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,				local_files_only=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[str]						=			CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,				local_files_only=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[Any]						=			{
											    '''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							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=0						):


											"""simple docstring"""





											if str(__SCREAMING_SNAKE_CASE						).startswith('''mps'''						):
																	lowercase_      :	Optional[Any]						=			torch.manual_seed(__SCREAMING_SNAKE_CASE						)
											else:
																	lowercase_      :	List[Any]						=			torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[int]						=			{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 5.0,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowercase_      :	Dict						=			self.get_dummy_components()
											lowercase_      :	Optional[Any]						=			StableDiffusionXLPipeline(**__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	int						=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			sd_pipe(**__SCREAMING_SNAKE_CASE						).images
											lowercase_      :	List[str]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											lowercase_      :	Optional[int]						=			np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]						)

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






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			self.get_dummy_components()
											lowercase_      :	int						=			StableDiffusionXLPipeline(**__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Any						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											# forward without prompt embeds
											lowercase_      :	Dict						=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Tuple						=			3 * ['''this is a negative prompt''']
											lowercase_      :	List[Any]						=			negative_prompt
											lowercase_      :	str						=			3 * [inputs['''prompt''']]

											lowercase_      :	Optional[int]						=			sd_pipe(**__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			output.images[0, -3:, -3:, -1]

											# forward with prompt embeds
											lowercase_      :	Tuple						=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			3 * ['''this is a negative prompt''']
											lowercase_      :	Union[str, Any]						=			3 * [inputs.pop('''prompt'''						)]

											(
											    (
											    lowercase_
											)		,					(
											    lowercase_
											)		,					(
											    lowercase_
											)		,					(
											    lowercase_
											)		,					
											)      :	Optional[int]						=			sd_pipe.encode_prompt(__SCREAMING_SNAKE_CASE							,				negative_prompt=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[int]						=			sd_pipe(
											    **__SCREAMING_SNAKE_CASE							,				prompt_embeds=__SCREAMING_SNAKE_CASE							,				negative_prompt_embeds=__SCREAMING_SNAKE_CASE							,				pooled_prompt_embeds=__SCREAMING_SNAKE_CASE							,				negative_pooled_prompt_embeds=__SCREAMING_SNAKE_CASE							,				)
											lowercase_      :	Optional[Any]						=			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






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self						):


											"""simple docstring"""





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







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






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE="cpu"							,				__SCREAMING_SNAKE_CASE=torch.floataa							,				__SCREAMING_SNAKE_CASE=0						):


											"""simple docstring"""





											lowercase_      :	str						=			torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			np.random.RandomState(__SCREAMING_SNAKE_CASE						).standard_normal((1, 4, 64, 64)						)
											lowercase_      :	Optional[int]						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE							,				dtype=__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Tuple						=			{
											    '''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						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''						)
											pipe.to(__SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	str						=			self.get_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Dict						=			pipe(**__SCREAMING_SNAKE_CASE						).images
											lowercase_      :	str						=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)
											lowercase_      :	Any						=			np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]						)
											assert np.abs(image_slice - expected_slice						).max() < 7E-3





'''simple docstring'''







from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


_lowercase					:    Tuple       				=			{
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase					:    List[Any]       				=			[
							    "Constraint",
							    "ConstraintListState",
							    "DisjunctiveConstraint",
							    "PhrasalConstraint",
							]
							_lowercase					:    int       				=			[
							    "BeamHypotheses",
							    "BeamScorer",
							    "BeamSearchScorer",
							    "ConstrainedBeamSearchScorer",
							]
							_lowercase					:    str       				=			[
							    "EpsilonLogitsWarper",
							    "EtaLogitsWarper",
							    "ForcedBOSTokenLogitsProcessor",
							    "ForcedEOSTokenLogitsProcessor",
							    "HammingDiversityLogitsProcessor",
							    "InfNanRemoveLogitsProcessor",
							    "LogitsProcessor",
							    "LogitsProcessorList",
							    "LogitsWarper",
							    "MinLengthLogitsProcessor",
							    "MinNewTokensLengthLogitsProcessor",
							    "NoBadWordsLogitsProcessor",
							    "NoRepeatNGramLogitsProcessor",
							    "PrefixConstrainedLogitsProcessor",
							    "RepetitionPenaltyLogitsProcessor",
							    "SequenceBiasLogitsProcessor",
							    "EncoderRepetitionPenaltyLogitsProcessor",
							    "TemperatureLogitsWarper",
							    "TopKLogitsWarper",
							    "TopPLogitsWarper",
							    "TypicalLogitsWarper",
							    "EncoderNoRepeatNGramLogitsProcessor",
							    "ExponentialDecayLengthPenalty",
							    "LogitNormalization",
							]
							_lowercase					:    int       				=			[
							    "MaxNewTokensCriteria",
							    "MaxLengthCriteria",
							    "MaxTimeCriteria",
							    "StoppingCriteria",
							    "StoppingCriteriaList",
							    "validate_stopping_criteria",
							]
							_lowercase					:    List[Any]       				=			[
							    "GenerationMixin",
							    "top_k_top_p_filtering",
							    "GreedySearchEncoderDecoderOutput",
							    "GreedySearchDecoderOnlyOutput",
							    "SampleEncoderDecoderOutput",
							    "SampleDecoderOnlyOutput",
							    "BeamSearchEncoderDecoderOutput",
							    "BeamSearchDecoderOnlyOutput",
							    "BeamSampleEncoderDecoderOutput",
							    "BeamSampleDecoderOnlyOutput",
							    "ContrastiveSearchEncoderDecoderOutput",
							    "ContrastiveSearchDecoderOnlyOutput",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase					:    Optional[Any]       				=			[
							    "TFForcedBOSTokenLogitsProcessor",
							    "TFForcedEOSTokenLogitsProcessor",
							    "TFLogitsProcessor",
							    "TFLogitsProcessorList",
							    "TFLogitsWarper",
							    "TFMinLengthLogitsProcessor",
							    "TFNoBadWordsLogitsProcessor",
							    "TFNoRepeatNGramLogitsProcessor",
							    "TFRepetitionPenaltyLogitsProcessor",
							    "TFTemperatureLogitsWarper",
							    "TFTopKLogitsWarper",
							    "TFTopPLogitsWarper",
							    "TFForceTokensLogitsProcessor",
							    "TFSuppressTokensAtBeginLogitsProcessor",
							    "TFSuppressTokensLogitsProcessor",
							]
							_lowercase					:    List[Any]       				=			[
							    "TFGenerationMixin",
							    "tf_top_k_top_p_filtering",
							    "TFGreedySearchDecoderOnlyOutput",
							    "TFGreedySearchEncoderDecoderOutput",
							    "TFSampleEncoderDecoderOutput",
							    "TFSampleDecoderOnlyOutput",
							    "TFBeamSearchEncoderDecoderOutput",
							    "TFBeamSearchDecoderOnlyOutput",
							    "TFBeamSampleEncoderDecoderOutput",
							    "TFBeamSampleDecoderOnlyOutput",
							    "TFContrastiveSearchEncoderDecoderOutput",
							    "TFContrastiveSearchDecoderOnlyOutput",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase					:    List[str]       				=			[
							    "FlaxForcedBOSTokenLogitsProcessor",
							    "FlaxForcedEOSTokenLogitsProcessor",
							    "FlaxLogitsProcessor",
							    "FlaxLogitsProcessorList",
							    "FlaxLogitsWarper",
							    "FlaxMinLengthLogitsProcessor",
							    "FlaxTemperatureLogitsWarper",
							    "FlaxTopKLogitsWarper",
							    "FlaxTopPLogitsWarper",
							]
							_lowercase					:    Optional[int]       				=			[
							    "FlaxGenerationMixin",
							    "FlaxGreedySearchOutput",
							    "FlaxSampleOutput",
							    "FlaxBeamSearchOutput",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							_lowercase					:    Tuple       				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



