






"""simple docstring"""






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						Dict		,			lowercase_  :						Dict=None		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Dict=None		,			**lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	parent
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	config_class
          SCREAMING_SNAKE_CASE_   :						str							=	has_text_modality
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	kwargs
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	common_properties
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.config_class(**self.inputs_dict)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	(
              ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
              if self.common_properties is None
              else self.common_properties
          )

          # Add common fields for text models
          if self.has_text_modality:
              common_properties.extend(['''vocab_size'''])

          # Test that config has the common properties as getters
          for prop in common_properties:
              self.parent.assertTrue(hasattr(lowercase_		,			lowercase_)		,			msg=F'`{prop}` does not exist')

          # Test that config has the common properties as setter
          for idx, name in enumerate(lowercase_):
              try:
                  setattr(lowercase_		,			lowercase_		,			lowercase_)
                  self.parent.assertEqual(
                      getattr(lowercase_		,			lowercase_)		,			lowercase_		,			msg=F'`{name} value {idx} expected, but was {getattr(lowercase_		,			lowercase_)}')
              except NotImplementedError:
                  # Some models might not be able to implement setters for common_properties
                  # In that case, a NotImplementedError is raised
                  pass

        # Test if config class can be called with Config(prop_name=..)
          for idx, name in enumerate(lowercase_):
              try:
                  SCREAMING_SNAKE_CASE_   :						str							=	self.config_class(**{name: idx})
                  self.parent.assertEqual(
                      getattr(lowercase_		,			lowercase_)		,			lowercase_		,			msg=F'`{name} value {idx} expected, but was {getattr(lowercase_		,			lowercase_)}')
              except NotImplementedError:
                  # Some models might not be able to implement setters for common_properties
                  # In that case, a NotImplementedError is raised
                  pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	self.config_class(**self.inputs_dict)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	json.loads(config.to_json_string())
          for key, value in self.inputs_dict.items():
              self.parent.assertEqual(obj[key]		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.config_class(**self.inputs_dict)

          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	os.path.join(lowercase_		,			'''config.json''')
              config_first.to_json_file(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Dict							=	self.config_class.from_json_file(lowercase_)

          self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict())
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.config_class(**self.inputs_dict)

          with tempfile.TemporaryDirectory() as tmpdirname:
              config_first.save_pretrained(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.config_class.from_pretrained(lowercase_)

          self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict())
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.config_class(**self.inputs_dict)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''test'''
          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	os.path.join(lowercase_		,			lowercase_)
              config_first.save_pretrained(lowercase_)
              SCREAMING_SNAKE_CASE_   :						str							=	self.config_class.from_pretrained(lowercase_		,			subfolder=lowercase_)

          self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict())
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.config_class(**self.inputs_dict		,			num_labels=5)
          self.parent.assertEqual(len(config.idalabel)		,			5)
          self.parent.assertEqual(len(config.labelaid)		,			5)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	3
          self.parent.assertEqual(len(config.idalabel)		,			3)
          self.parent.assertEqual(len(config.labelaid)		,			3)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          if self.config_class.is_composition:
              return
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.config_class()
          self.parent.assertIsNotNone(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	copy.deepcopy(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.config_class(**lowercase_)
          SCREAMING_SNAKE_CASE_   :						str							=	[]
          for key, value in config_common_kwargs.items():
              if key == "torch_dtype":
                  if not is_torch_available():
                      continue
                  else:
                      import torch

                      if config.torch_dtype != torch.floataa:
                          wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa))
              elif getattr(lowercase_		,			lowercase_) != value:
                  wrong_values.append((key, getattr(lowercase_		,			lowercase_), value))

          if len(lowercase_) > 0:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	'''\n'''.join([F'- {v[0]}: got {v[1]} instead of {v[2]}' for v in wrong_values])
              raise ValueError(F'The following keys were not properly set in the config:\n{errors}')



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.create_and_test_config_to_json_string()
          self.create_and_test_config_to_json_file()
          self.create_and_test_config_from_and_save_pretrained()
          self.create_and_test_config_from_and_save_pretrained_subfolder()
          self.create_and_test_config_with_num_labels()
          self.check_config_can_be_init_without_params()
          self.check_config_arguments_init()










"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			*lowercase_  :						Any		,			**lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


