
"""simple docstring"""


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  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      List[Any]          =    StableDiffusionXLImgaImgPipeline
				a__:      Any          =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				a__:      Dict          =    PipelineTesterMixin.required_optional_params - {'latents'}
				a__:      int          =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				a__:      Optional[int]          =    IMAGE_TO_IMAGE_IMAGE_PARAMS
				a__:      str          =    IMAGE_TO_IMAGE_IMAGE_PARAMS




				def        UpperCAmelCase__						(				self   ):
							torch.manual_seed(0   )
							lowerCamelCase_					=  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							,						)
							lowerCamelCase_					=  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   )
							lowerCamelCase_					=  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   )
							lowerCamelCase_					=  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=1000							,						hidden_act='''gelu'''							,						projection_dim=32							,						)
							lowerCamelCase_					=  CLIPTextModel(UpperCAmelCase   )
							lowerCamelCase_					=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,						local_files_only=UpperCAmelCase   )

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

							lowerCamelCase_					=  {
							    '''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        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  image / 2 + 0.5
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  StableDiffusionXLImgaImgPipeline(**UpperCAmelCase   )
							lowerCamelCase_					=  sd_pipe.to(UpperCAmelCase   )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

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

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

							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )




				def        UpperCAmelCase__						(				self   ):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3   )




				def        UpperCAmelCase__						(				self   ):
							pass





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  StableDiffusionXLImgaImgPipeline(**UpperCAmelCase   )
							lowerCamelCase_					=  sd_pipe.to(UpperCAmelCase   )
							lowerCamelCase_					=  sd_pipe.to(UpperCAmelCase   )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

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

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

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

							(
							    (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, 
							)					=  sd_pipe.encode_prompt(UpperCAmelCase							,						negative_prompt=UpperCAmelCase   )

							lowerCamelCase_					=  sd_pipe(
							    **UpperCAmelCase							,						prompt_embeds=UpperCAmelCase							,						negative_prompt_embeds=UpperCAmelCase							,						pooled_prompt_embeds=UpperCAmelCase							,						negative_pooled_prompt_embeds=UpperCAmelCase							,						)
							lowerCamelCase_					=  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  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase="cpu"							,						UpperCAmelCase=torch.floataa							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  np.random.RandomState(UpperCAmelCase   ).standard_normal((1, 4, 64, 64)   )
							lowerCamelCase_					=  torch.from_numpy(UpperCAmelCase   ).to(device=UpperCAmelCase							,						dtype=UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase_					=  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







"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

