





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							StableDiffusionInstructPixaPixPipeline
      a						       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
      a						       =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      a						       =							IMAGE_TO_IMAGE_IMAGE_PARAMS
      a						       =							IMAGE_TO_IMAGE_IMAGE_PARAMS
      def 		_lowerCamelCase      (		self       ):
         torch.manual_seed(0       )
         A_     :				Optional[Any]			      =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=8      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       )
         A_     :				int			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				int			      =	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_     :				List[Any]			      =	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_     :				List[Any]			      =	CLIPTextModel(a__       )
         A_     :				int			      =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

         A_     :				Tuple			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         A_     :				Tuple			      =	floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(a__       )       ).to(a__       )
         A_     :				List[Any]			      =	image.cpu().permute(0      ,       2      ,       3      ,       1       )[0]
         A_     :				Union[str, Any]			      =	Image.fromarray(np.uinta(a__       )       ).convert("""RGB"""       )
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Dict			      =	torch.manual_seed(a__       )
         else:
            A_     :				List[Any]			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Optional[int]			      =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """image_guidance_scale""": 1,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				List[Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				List[str]			      =	self.get_dummy_inputs(a__       )
         A_     :				str			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         A_     :				List[str]			      =	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Tuple			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Tuple			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	"""french fries"""
         A_     :				str			      =	sd_pipe(**a__      ,       negative_prompt=a__       )
         A_     :				Any			      =	output.images
         A_     :				Optional[int]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_     :				List[str]			      =	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Dict			      =	self.get_dummy_components()
         A_     :				Dict			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				str			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Dict			      =	self.get_dummy_inputs(a__       )
         A_     :				int			      =	[inputs["""prompt"""]] * 2

         A_     :				Any			      =	np.array(inputs["""image"""]       ).astype(np.floataa       ) / 255.0
         A_     :				List[Any]			      =	torch.from_numpy(a__       ).unsqueeze(0       ).to(a__       )
         A_     :				List[str]			      =	image / 2 + 0.5
         A_     :				int			      =	image.permute(0      ,       3      ,       1      ,       2       )
         A_     :				Optional[Any]			      =	image.repeat(2      ,       1      ,       1      ,       1       )

         A_     :				Optional[int]			      =	sd_pipe(**a__       ).images
         A_     :				Union[str, Any]			      =	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         A_     :				Union[str, Any]			      =	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Dict			      =	EulerAncestralDiscreteScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""       )
         A_     :				Union[str, Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Union[str, Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]

         A_     :				Any			      =	[round(a__      ,       4       ) for x in image_slice.flatten().tolist()]
         print(""",""".join([str(a__       ) for x in slice]       )       )

         assert image.shape == (1, 32, 32, 3)
         A_     :				Tuple			      =	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	self.get_dummy_components()
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				str			      =	VaeImageProcessor(do_resize=a__      ,       do_normalize=a__       )
         A_     :				Union[str, Any]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[Any]			      =	pipe(**self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )       )[0]

         A_     :				Any			      =	components["""vae"""]
         A_     :				Any			      =	self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )

         for image_param in self.image_latents_params:
            if image_param in inputs.keys():
               A_     :				int			      =	vae.encode(inputs[image_param]       ).latent_dist.mode()

         A_     :				List[Any]			      =	pipe(**a__       )[0]

         A_     :				str			      =	np.abs(out - out_latents_inputs       ).max()
         self.assertLess(a__      ,       1E-4      ,       """passing latents as image input generate different result from passing image"""       )






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self      ,       a__=0       ):
         A_     :				str			      =	torch.manual_seed(a__       )
         A_     :				Dict			      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""       )
         A_     :				List[Any]			      =	{
             """prompt""": """turn him into a cyborg""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """image_guidance_scale""": 1.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Optional[int]			      =	self.get_inputs()
         A_     :				List[str]			      =	pipe(**a__       ).images
         A_     :				Union[str, Any]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				int			      =	LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	DDIMScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				str			      =	self.get_inputs()
         A_     :				List[str]			      =	pipe(**a__       ).images
         A_     :				Optional[int]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	0

         def callback_fn(a__      ,       a__      ,       a__       ) -> None:
            A_     :				str			      =	True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
               A_     :				Optional[int]			      =	latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               A_     :				str			      =	latents[0, -3:, -3:, -1]
               A_     :				int			      =	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]       )

               assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
            elif step == 2:
               A_     :				Dict			      =	latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               A_     :				List[str]			      =	latents[0, -3:, -3:, -1]
               A_     :				Union[str, Any]			      =	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]       )

               assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

         A_     :				Tuple			      =	False

         A_     :				Any			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__      ,       torch_dtype=torch.floataa       )
         A_     :				Optional[int]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Dict			      =	self.get_inputs()
         pipe(**a__      ,       callback=a__      ,       callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3
      def 		_lowerCamelCase      (		self       ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         A_     :				Optional[int]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__      ,       torch_dtype=torch.floataa       )
         A_     :				Optional[Any]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         A_     :				List[Any]			      =	self.get_inputs()
         A_     :				int			      =	pipe(**a__       )

         A_     :				Dict			      =	torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9






      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         A_     :				Dict			      =	inputs["""image"""].resize((504, 504)       )

         A_     :				int			      =	"""timbrooks/instruct-pix2pix"""
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             a__      ,       safety_checker=a__      ,       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				Optional[int]			      =	pipe(**a__       )
         A_     :				Optional[Any]			      =	output.images[0]

         A_     :				str			      =	image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         A_     :				Optional[Any]			      =	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3






