





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()







@skip_mps
class     lowerCamelCase(	__snake_case ,   __snake_case ,   __snake_case ,   unittest.TestCase  ):
					'''simple docstring'''


					__magic_name__        = StableDiffusionModelEditingPipeline
					__magic_name__        = TEXT_TO_IMAGE_PARAMS
					__magic_name__        = TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__        = TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__        = TEXT_TO_IMAGE_IMAGE_PARAMS




					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )
									_A    =       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							,    )
									_A    =       DDIMScheduler()
									torch.manual_seed(0       )
									_A    =       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							,    )
									torch.manual_seed(0       )
									_A    =       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							,    )
									_A    =       CLIPTextModel(A_       )
									_A    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

									_A    =       {
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=0       ):
									_A    =       torch.manual_seed(A_       )
									_A    =       {
									    'prompt': 'A field of roses',
									    'generator': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    'height': None,
									    'width': None,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
									_A    =       self.get_dummy_components()
									_A    =       StableDiffusionModelEditingPipeline(**A_       )
									_A    =       sd_pipe.to(A_       )
									sd_pipe.set_progress_bar_config(disable=A_       )

									_A    =       self.get_dummy_inputs(A_       )
									_A    =       sd_pipe(**A_       ).images
									_A    =       image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									_A    =       np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]       )

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




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
									_A    =       self.get_dummy_components()
									_A    =       StableDiffusionModelEditingPipeline(**A_       )
									_A    =       sd_pipe.to(A_       )
									sd_pipe.set_progress_bar_config(disable=A_       )

									_A    =       self.get_dummy_inputs(A_       )
									_A    =       'french fries'
									_A    =       sd_pipe(**A_							,    negative_prompt=A_       )
									_A    =       output.images
									_A    =       image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_A    =       np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]       )

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




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
									_A    =       self.get_dummy_components()
									_A    =       EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085							,    beta_end=0.012							,    beta_schedule='scaled_linear'       )
									_A    =       StableDiffusionModelEditingPipeline(**A_       )
									_A    =       sd_pipe.to(A_       )
									sd_pipe.set_progress_bar_config(disable=A_       )

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

									assert image.shape == (1, 64, 64, 3)

									_A    =       np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]       )

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




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
									_A    =       self.get_dummy_components()
									_A    =       PNDMScheduler()
									_A    =       StableDiffusionModelEditingPipeline(**A_       )
									_A    =       sd_pipe.to(A_       )
									sd_pipe.set_progress_bar_config(disable=A_       )

									_A    =       self.get_dummy_inputs(A_       )
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(A_       ):
													_A    =       sd_pipe(**A_       ).images




					def 					lowerCAmelCase__		(						self       ):
									super().test_inference_batch_single_identical(expected_max_diff=5E-3       )




					def 					lowerCAmelCase__		(						self       ):
									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )







@slow
@require_torch_gpu
class     lowerCamelCase(	unittest.TestCase  ):
					'''simple docstring'''


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




					def 					lowerCAmelCase__		(						self							,    snake_case_=0       ):
									_A    =       torch.manual_seed(A_       )
									_A    =       {
									    'prompt': 'A field of roses',
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'CompVis/stable-diffusion-v1-4'
									_A    =       StableDiffusionModelEditingPipeline.from_pretrained(A_							,    safety_checker=A_       )
									pipe.to(A_       )
									pipe.set_progress_bar_config(disable=A_       )
									pipe.enable_attention_slicing()

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

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

									_A    =       np.array(
									    [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]       )

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

									# make sure image changes after editing
									pipe.edit_model('A pack of roses'							,    'A pack of blue roses'       )

									_A    =       pipe(**A_       ).images
									_A    =       image[0, -3:, -3:, -1].flatten()

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

									assert np.abs(expected_slice - image_slice       ).max() > 1E-1




					def 					lowerCAmelCase__		(						self       ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_A    =       'CompVis/stable-diffusion-v1-4'
									_A    =       DDIMScheduler.from_pretrained(A_							,    subfolder='scheduler'       )
									_A    =       StableDiffusionModelEditingPipeline.from_pretrained(
									    A_							,    scheduler=A_							,    safety_checker=A_       )
									_A    =       pipe.to(A_       )
									pipe.set_progress_bar_config(disable=A_       )
									pipe.enable_attention_slicing(1       )
									pipe.enable_sequential_cpu_offload()

									_A    =       self.get_inputs()
									_A    =       pipe(**A_       )

									_A    =       torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 10**9












from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = SqueezeBertTokenizer
     __magic_name__        = SqueezeBertTokenizerFast
     __magic_name__        = True




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'       )

         _A    =       tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]





