
"""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 		a_						:







          @staticmethod
          def      _snake_case					(			*__UpperCamelCase			:       Any     ,					**__UpperCamelCase			:       Any     )		->str:






              '''simple docstring'''






              pass


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   hashlib.mda(image.tobytes()      )
    return m.hexdigest()[:1_0]


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   np.array(_A      )
    _UpperCAmelCase          =   npimg.shape
    return {"hash": hashimage(_A      ), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 		a_						( unittest.TestCase	):
    a      :				List[Any]     =				dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
    a      :				List[Any]     =				dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   MaskGenerationPipeline(model=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        pass







    @require_tf
    @unittest.skip("""Image segmentation not implemented in TF"""     )
    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        pass







    @slow
    @require_torch
    def      _snake_case					(			self			:       Optional[Any]     )		->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_56     )

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

        # fmt: off
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,					decimals=4     )     ,					[
                {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
                {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1},
                {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
                {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
                {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
                {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_6_7},
                {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_3},
                {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_0_9},
                {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_7_9},
                {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_3_4},
                {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9_7_1_6},
                {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9_6_1_2},
                {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_9_9},
                {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_5_2},
                {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_3_2},
                {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_1_6},
                {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_9_9},
                {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_8_3},
                {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_6_4},
                {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
                {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
                {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_0_8},
                {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_3_5},
                {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_2_6},
                {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9_2_6_2},
                {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_9_9},
                {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_6},
                {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_4},
                {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_3},
                {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_1}
            ]     ,					)
        # fmt: on







    @require_torch
    @slow
    def      _snake_case					(			self			:       Optional[int]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   """facebook/sam-vit-huge"""
        _UpperCAmelCase          =   pipeline("""mask-generation"""     ,					model=__UpperCamelCase     )

        _UpperCAmelCase          =   image_segmenter(
            """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,					pred_iou_thresh=1     ,					points_per_batch=2_56     )

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

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,					decimals=4     )     ,					[
                {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
                {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1_0},
                {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
                {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
                {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
            ]     ,					)

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )