

'''simple docstring'''






import copy
import os
from typing import Union

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


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    'git_vision_model'






  def __init__(   self     :  List[str]      ,lowercase__     :  List[str]=7_6_8      ,lowercase__     :  Optional[Any]=3_0_7_2      ,lowercase__     :  Optional[Any]=1_2      ,lowercase__     :  Any=1_2      ,lowercase__     :  Optional[int]=3      ,lowercase__     :  Dict=2_2_4      ,lowercase__     :  List[Any]=1_6      ,lowercase__     :  List[str]="quick_gelu"      ,lowercase__     :  List[str]=1e-5      ,lowercase__     :  Tuple=0.0      ,lowercase__     :  List[str]=0.0_2      ,**lowercase__     :  Tuple      ,):
   super().__init__(**lowercase__    )

   __lowercase													=				hidden_size
   __lowercase													=				intermediate_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				num_channels
   __lowercase													=				patch_size
   __lowercase													=				image_size
   __lowercase													=				initializer_range
   __lowercase													=				attention_dropout
   __lowercase													=				layer_norm_eps
   __lowercase													=				hidden_act






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   cls     :  str      ,lowercase__     :  Union[str, os.PathLike]      ,**lowercase__     :  Tuple    ):
   cls._set_token_in_kwargs(lowercase__    )

   __lowercase						,			__lowercase													=				cls.get_config_dict(lowercase__      ,**lowercase__    )

   # get the vision config dict if we are loading from GITConfig
   if config_dict.get('''model_type'''    ) == "git":
    __lowercase													=				config_dict['''vision_config''']

   if "model_type" in config_dict and hasattr(cls      ,'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
        F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."    )

   return cls.from_dict(lowercase__      ,**lowercase__    )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[Any]           =    'git'






  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Dict=None      ,lowercase__     :  str=3_0_5_2_2      ,lowercase__     :  List[str]=7_6_8      ,lowercase__     :  Any=6      ,lowercase__     :  Optional[int]=1_2      ,lowercase__     :  List[str]=3_0_7_2      ,lowercase__     :  Any="gelu"      ,lowercase__     :  Dict=0.1      ,lowercase__     :  List[Any]=0.1      ,lowercase__     :  int=1_0_2_4      ,lowercase__     :  Union[str, Any]=0.0_2      ,lowercase__     :  List[Any]=1e-1_2      ,lowercase__     :  List[Any]=0      ,lowercase__     :  Union[str, Any]="absolute"      ,lowercase__     :  Tuple=True      ,lowercase__     :  Union[str, Any]=False      ,lowercase__     :  Union[str, Any]=1_0_1      ,lowercase__     :  Union[str, Any]=1_0_2      ,lowercase__     :  Tuple=None      ,**lowercase__     :  int      ,):
   super().__init__(bos_token_id=lowercase__      ,eos_token_id=lowercase__      ,pad_token_id=lowercase__      ,**lowercase__    )

   if vision_config is None:
    __lowercase													=				{}
    logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''    )

   __lowercase													=				GitVisionConfig(**lowercase__    )
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				hidden_act
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				max_position_embeddings
   __lowercase													=				initializer_range
   __lowercase													=				layer_norm_eps
   __lowercase													=				position_embedding_type
   __lowercase													=				use_cache
   __lowercase													=				tie_word_embeddings
   __lowercase													=				num_image_with_embedding

   __lowercase													=				bos_token_id
   __lowercase													=				eos_token_id






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				copy.deepcopy(self.__dict__    )
   __lowercase													=				self.vision_config.to_dict()
   __lowercase													=				self.__class__.model_type
   return output




'''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     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



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






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






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

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






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






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

