






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:





          """simple docstring"""







          @staticmethod
          def       _a   ( *_lowerCamelCase  ,      **_lowerCamelCase							):
            pass

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Optional[int]   				=    hashlib.mda(image.tobytes()  )
  return m.hexdigest()




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





       """simple docstring"""







       a    :		Any									=MODEL_FOR_DEPTH_ESTIMATION_MAPPING





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, 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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
         self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}  ,      _lowerCamelCase							)
         import datasets

         UpperCamelCase_: List[Any]   				=    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       _a   ( self							):
         pass





       @slow
       @require_torch
       def       _a   ( self							):
         UpperCamelCase_: int   				=    'Intel/dpt-large'
         UpperCamelCase_: List[str]   				=    pipeline('depth-estimation'  ,      model=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
         UpperCamelCase_: List[Any]   				=    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       _a   ( self							):
         # This is highly irregular to have no small tests.
         self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )