


'''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 				UpperCAmelCase			:


     @staticmethod
     def        __SCREAMING_SNAKE_CASE					(      *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Union[str, Any] ):
        pass


def        a__     (					UpperCamelCase_						:      Image						):
   UpperCAmelCase__	:str      =      hashlib.mda(image.tobytes()						)
   return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	Optional[int] ):
    UpperCAmelCase__	:Any      =      DepthEstimationPipeline(model=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    return depth_estimator, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Tuple ):
    UpperCAmelCase__	:Union[str, Any]      =      depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )}			,       __lowerCamelCase )
    import datasets

    UpperCAmelCase__	:str      =      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,       '''image'''			,       split='''test''' )
    UpperCAmelCase__	: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 )},
        ]			,       __lowerCamelCase			,       )


 @require_tf
 @unittest.skip('''Depth estimation is not implemented in TF''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    pass


 @slow
 @require_torch
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Tuple      =      '''Intel/dpt-large'''
    UpperCAmelCase__	:str      =      pipeline('''depth-estimation'''			,       model=__lowerCamelCase )
    UpperCAmelCase__	:str      =      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
    UpperCAmelCase__	:Optional[int]      =      hashimage(outputs['''depth'''] )

    # This seems flaky.
    # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
    self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() )			,       29.3_04 )
    self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() )			,       2.6_62 )





 @require_torch
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    # This is highly irregular to have no small tests.
    self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            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__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # 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__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

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

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



