
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       __A:

  """simple docstring"""







  def 			UpperCAmelCase_   (self		):
       torch.manual_seed(0		)
       UpperCamelCase__												=							TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

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

       torch.manual_seed(0		)
       UpperCamelCase__												=							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		)
       UpperCamelCase__												=							DDPMScheduler(
           num_train_timesteps=10_00			,     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		)
       UpperCamelCase__												=							IFWatermarker()

       return {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "watermarker": watermarker,
           "safety_checker": None,
           "feature_extractor": None,
       }







  def 			UpperCAmelCase_   (self		):
       torch.manual_seed(0		)
       UpperCamelCase__												=							TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

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

       torch.manual_seed(0		)
       UpperCamelCase__												=							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		)
       UpperCamelCase__												=							DDPMScheduler(
           num_train_timesteps=10_00			,     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		)
       UpperCamelCase__												=							DDPMScheduler(
           num_train_timesteps=10_00			,     beta_schedule="""squaredcos_cap_v2"""			,     beta_start=0.0001			,     beta_end=0.02			,     )

       torch.manual_seed(0		)
       UpperCamelCase__												=							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 			UpperCAmelCase_   (self		):
       UpperCamelCase__												=							self.get_dummy_components()
       UpperCamelCase__												=							self.pipeline_class(**a_		)
       pipe.to(a_		)
       pipe.set_progress_bar_config(disable=a_		)

       UpperCamelCase__												=							self.get_dummy_inputs(a_		)

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

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

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

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

       UpperCamelCase__						,			UpperCamelCase__												=							pipe.encode_prompt(a_		)

       # inputs with prompt converted to embeddings
       UpperCamelCase__												=							{
           """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:
            UpperCamelCase__												=							image

       if mask_image is not None:
            UpperCamelCase__												=							mask_image

       if original_image is not None:
            UpperCamelCase__												=							original_image

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

       UpperCamelCase__												=							pipe(**a_		)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(a_		)
            UpperCamelCase__												=							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."			,     )

       UpperCamelCase__												=							self.get_dummy_inputs(a_		)

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

       # inputs with prompt converted to embeddings
       UpperCamelCase__												=							{
           """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:
            UpperCamelCase__												=							image

       if mask_image is not None:
            UpperCamelCase__												=							mask_image

       if original_image is not None:
            UpperCamelCase__												=							original_image

       UpperCamelCase__												=							pipe_loaded(**a_		)[0]

       UpperCamelCase__												=							np.abs(to_np(a_		) - to_np(a_		)		).max()
       self.assertLess(a_			,     1E-4		)







  def 			UpperCAmelCase_   (self		):
       UpperCamelCase__												=							self.get_dummy_components()
       UpperCamelCase__												=							self.pipeline_class(**a_		)
       pipe.to(a_		)
       pipe.set_progress_bar_config(disable=a_		)

       UpperCamelCase__												=							self.get_dummy_inputs(a_		)
       UpperCamelCase__												=							pipe(**a_		)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(a_		)
            UpperCamelCase__												=							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

       UpperCamelCase__												=							self.get_dummy_inputs(a_		)
       UpperCamelCase__												=							pipe_loaded(**a_		)[0]

       UpperCamelCase__												=							np.abs(to_np(a_		) - to_np(a_		)		).max()
       self.assertLess(a_			,     1E-4		)








'''simple docstring'''




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:



  class 							lowercase_						:

    '''simple docstring'''





    @staticmethod
    def     snake_case_      (  *a_ ,			**a_  )				->					Tuple:


      """simple docstring"""
      pass


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						hashlib.mda(image.tobytes()    )
  return m.hexdigest()[:10]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						np.array(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						npimg.shape
  return {"hash": hashimage(SCREAMING_SNAKE_CASE    ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  __lowerCAmelCase       :			Tuple     	= dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						MaskGenerationPipeline(model=a_ ,			image_processor=a_  )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    pass
  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    pass
  @slow
  @require_torch
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model='facebook/sam-vit-huge'  )

    UpperCAmelCase    				=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    # fmt: off
    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
            {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
            {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
            {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
            {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
            {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
            {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
            {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
            {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
            {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
            {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
            {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
            {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
            {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
            {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
            {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
            {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
            {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
            {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
            {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
            {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
            {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
            {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
            {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
        ] ,			)
    # fmt: on





  @require_torch
  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/sam-vit-huge'
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model=a_  )

    UpperCAmelCase    				=						image_segmenter(
        'http://images.cocodataset.org/val2017/000000039769.jpg' ,			pred_iou_thresh=1 ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
        ] ,			)


