




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  SCREAMING_SNAKE_CASE							(    *UpperCAmelCase__   :			int      ,				**UpperCAmelCase__   :			Tuple)		->List[str]:





             '''simple docstring'''

             pass







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					hashlib.mda(image.tobytes()		)
       return m.hexdigest()


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

    '''simple docstring'''






    UpperCAmelCase__													=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					DepthEstimationPipeline(model=UpperCAmelCase__      ,				image_processor=UpperCAmelCase__)
           return depth_estimator, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[int])		->List[Any]:





           '''simple docstring'''

           A__   				=					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}      ,				UpperCAmelCase__)
           import datasets

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


    @require_tf
    @unittest.skip('''Depth estimation is not implemented in TF''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           A__   				=					'''Intel/dpt-large'''
           A__   				=					pipeline('''depth-estimation'''      ,				model=UpperCAmelCase__)
           A__   				=					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
           A__   				=					hashimage(outputs['''depth'''])

           # This seems flaky.
           # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
           self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())      ,				29.304)
           self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())      ,				2.662)





    @require_torch
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Union[str, Any]:





           '''simple docstring'''

           self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')








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


_lowerCamelCase			:	Tuple							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Tuple							 =     {
    """tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json""",
    """tiiuae/falcon-7b""": """https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json""",
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''falcon'''
    UpperCAmelCase__													=   ['''past_key_values''']


    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str=65_024      ,				UpperCAmelCase__   :			List[Any]=4_544      ,				UpperCAmelCase__   :			Optional[Any]=32      ,				UpperCAmelCase__   :			Dict=71      ,				UpperCAmelCase__   :			Any=1e-5      ,				UpperCAmelCase__   :			Optional[int]=0.02      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			Union[str, Any]=0.0      ,				UpperCAmelCase__   :			List[str]=0.0      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			Dict=False      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=False      ,				UpperCAmelCase__   :			Dict=11      ,				UpperCAmelCase__   :			str=11      ,				**UpperCAmelCase__   :			Dict      ,				)		->Any:





           '''simple docstring'''

           A__   				=					vocab_size
           # Backward compatibility with n_embed kwarg
           A__   				=					kwargs.pop('''n_embed'''      ,				UpperCAmelCase__)
           A__   				=					hidden_size if n_embed is None else n_embed
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					layer_norm_epsilon
           A__   				=					initializer_range
           A__   				=					use_cache
           A__   				=					hidden_dropout
           A__   				=					attention_dropout

           A__   				=					bos_token_id
           A__   				=					eos_token_id
           A__   				=					num_attention_heads if num_kv_heads is None else num_kv_heads
           A__   				=					alibi
           A__   				=					new_decoder_architecture
           A__   				=					multi_query  # Ignored when new_decoder_architecture is True
           A__   				=					parallel_attn
           A__   				=					bias

           super().__init__(bos_token_id=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__      ,				**UpperCAmelCase__)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''simple docstring'''

           return self.hidden_size // self.num_attention_heads





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           return not self.alibi


