'''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  :





         '''simple docstring'''



         @staticmethod
         def _lowerCAmelCase(						*__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Optional[int]:
          pass
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					hashlib.mda(image.tobytes()     )
 return m.hexdigest()





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





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Optional[Any]:
        lowercase__				:    int  =					DepthEstimationPipeline(model=__lowerCAmelCase	,   image_processor=__lowerCAmelCase )
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    str  =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )}	,   __lowerCAmelCase )
        import datasets

        lowercase__				:    Union[str, Any]  =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''	,   '''image'''	,   split='''test''' )
        lowercase__				:    Any  =					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 _lowerCAmelCase(						self )  ->  Dict:
        pass

       @slow
       @require_torch
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Any  =					'''Intel/dpt-large'''
        lowercase__				:    List[Any]  =					pipeline('''depth-estimation'''	,   model=__lowerCAmelCase )
        lowercase__				:    int  =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
        lowercase__				:    str  =					hashimage(outputs['''depth'''] )

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() )	,   2_9.3_0_4 )
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() )	,   2.6_6_2 )


       @require_torch
       def _lowerCAmelCase(						self )  ->  Tuple:
        # 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 __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

