
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class 			UpperCAmelCase_     :


  '''simple docstring'''







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







    """simple docstring"""






    torch.manual_seed(0       )
    __magic_name__								=			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

    torch.manual_seed(0       )
    __magic_name__								=			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

    torch.manual_seed(0       )
    __magic_name__								=			UNetaDConditionModel(
        sample_size=32     ,				layers_per_block=1     ,				block_out_channels=[32, 64]     ,				down_block_types=[
            """ResnetDownsampleBlock2D""",
            """SimpleCrossAttnDownBlock2D""",
        ]     ,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""     ,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]     ,				in_channels=3     ,				out_channels=6     ,				cross_attention_dim=32     ,				encoder_hid_dim=32     ,				attention_head_dim=8     ,				addition_embed_type="""text"""     ,				addition_embed_type_num_heads=2     ,				cross_attention_norm="""group_norm"""     ,				resnet_time_scale_shift="""scale_shift"""     ,				act_fn="""gelu"""     ,				)
    unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

    torch.manual_seed(0       )
    __magic_name__								=			DDPMScheduler(
        num_train_timesteps=1000     ,				beta_schedule="""squaredcos_cap_v2"""     ,				beta_start=0.0001     ,				beta_end=0.02     ,				thresholding=a__     ,				dynamic_thresholding_ratio=0.95     ,				sample_max_value=1.0     ,				prediction_type="""epsilon"""     ,				variance_type="""learned_range"""     ,				)

    torch.manual_seed(0       )
    __magic_name__								=			IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }
  def 				_lowercase				(					self     :					Dict       )     ->      Tuple:







    """simple docstring"""






    torch.manual_seed(0       )
    __magic_name__								=			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

    torch.manual_seed(0       )
    __magic_name__								=			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

    torch.manual_seed(0       )
    __magic_name__								=			UNetaDConditionModel(
        sample_size=32     ,				layers_per_block=[1, 2]     ,				block_out_channels=[32, 64]     ,				down_block_types=[
            """ResnetDownsampleBlock2D""",
            """SimpleCrossAttnDownBlock2D""",
        ]     ,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""     ,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]     ,				in_channels=6     ,				out_channels=6     ,				cross_attention_dim=32     ,				encoder_hid_dim=32     ,				attention_head_dim=8     ,				addition_embed_type="""text"""     ,				addition_embed_type_num_heads=2     ,				cross_attention_norm="""group_norm"""     ,				resnet_time_scale_shift="""scale_shift"""     ,				act_fn="""gelu"""     ,				class_embed_type="""timestep"""     ,				mid_block_scale_factor=1.414     ,				time_embedding_act_fn="""gelu"""     ,				time_embedding_dim=32     ,				)
    unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

    torch.manual_seed(0       )
    __magic_name__								=			DDPMScheduler(
        num_train_timesteps=1000     ,				beta_schedule="""squaredcos_cap_v2"""     ,				beta_start=0.0001     ,				beta_end=0.02     ,				thresholding=a__     ,				dynamic_thresholding_ratio=0.95     ,				sample_max_value=1.0     ,				prediction_type="""epsilon"""     ,				variance_type="""learned_range"""     ,				)

    torch.manual_seed(0       )
    __magic_name__								=			DDPMScheduler(
        num_train_timesteps=1000     ,				beta_schedule="""squaredcos_cap_v2"""     ,				beta_start=0.0001     ,				beta_end=0.02     ,				)

    torch.manual_seed(0       )
    __magic_name__								=			IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "image_noising_scheduler": image_noising_scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			self.pipeline_class(**a__       )
    pipe.to(a__       )
    pipe.set_progress_bar_config(disable=a__       )

    __magic_name__								=			self.get_dummy_inputs(a__       )

    __magic_name__								=			inputs["prompt"]
    __magic_name__								=			inputs["generator"]
    __magic_name__								=			inputs["num_inference_steps"]
    __magic_name__								=			inputs["output_type"]

    if "image" in inputs:
      __magic_name__								=			inputs["image"]
    else:
      __magic_name__								=			None

    if "mask_image" in inputs:
      __magic_name__								=			inputs["mask_image"]
    else:
      __magic_name__								=			None

    if "original_image" in inputs:
      __magic_name__								=			inputs["original_image"]
    else:
      __magic_name__								=			None

    __magic_name__								=			pipe.encode_prompt(a__       )

    # inputs with prompt converted to embeddings
    __magic_name__								=			{
        "prompt_embeds": prompt_embeds,
        "negative_prompt_embeds": negative_prompt_embeds,
        "generator": generator,
        "num_inference_steps": num_inference_steps,
        "output_type": output_type,
    }

    if image is not None:
      __magic_name__								=			image

    if mask_image is not None:
      __magic_name__								=			mask_image

    if original_image is not None:
      __magic_name__								=			original_image

    # set all optional components to None
    for optional_component in pipe._optional_components:
      setattr(a__     ,				a__     ,				a__       )

    __magic_name__								=			pipe(**a__       )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(a__       )
      __magic_name__								=			self.pipeline_class.from_pretrained(a__       )
      pipe_loaded.to(a__       )
      pipe_loaded.set_progress_bar_config(disable=a__       )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

    for optional_component in pipe._optional_components:
      self.assertTrue(
          getattr(a__     ,				a__       ) is None     ,				F'''`{optional_component}` did not stay set to None after loading.'''     ,				)

    __magic_name__								=			self.get_dummy_inputs(a__       )

    __magic_name__								=			inputs["generator"]
    __magic_name__								=			inputs["num_inference_steps"]
    __magic_name__								=			inputs["output_type"]

    # inputs with prompt converted to embeddings
    __magic_name__								=			{
        "prompt_embeds": prompt_embeds,
        "negative_prompt_embeds": negative_prompt_embeds,
        "generator": generator,
        "num_inference_steps": num_inference_steps,
        "output_type": output_type,
    }

    if image is not None:
      __magic_name__								=			image

    if mask_image is not None:
      __magic_name__								=			mask_image

    if original_image is not None:
      __magic_name__								=			original_image

    __magic_name__								=			pipe_loaded(**a__       )[0]

    __magic_name__								=			np.abs(to_np(a__       ) - to_np(a__       )       ).max()
    self.assertLess(a__     ,				1E-4       )





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







    """simple docstring"""






    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			self.pipeline_class(**a__       )
    pipe.to(a__       )
    pipe.set_progress_bar_config(disable=a__       )

    __magic_name__								=			self.get_dummy_inputs(a__       )
    __magic_name__								=			pipe(**a__       )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(a__       )
      __magic_name__								=			self.pipeline_class.from_pretrained(a__       )
      pipe_loaded.to(a__       )
      pipe_loaded.set_progress_bar_config(disable=a__       )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

    __magic_name__								=			self.get_dummy_inputs(a__       )
    __magic_name__								=			pipe_loaded(**a__       )[0]

    __magic_name__								=			np.abs(to_np(a__       ) - to_np(a__       )       ).max()
    self.assertLess(a__     ,				1E-4       )



'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





