



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}



class     lowerCAmelCase_		(  SCREAMING_SNAKE_CASE__							):
       UpperCAmelCase__    : List[str]       					=  '''blip_2_vision_model'''
       def __init__(      self,      SCREAMING_SNAKE_CASE_=1408,      SCREAMING_SNAKE_CASE_=6144,      SCREAMING_SNAKE_CASE_=39,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=14,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.0_00_01,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=1e-10,      SCREAMING_SNAKE_CASE_=True,      **SCREAMING_SNAKE_CASE_,      )					->		Dict:
          super().__init__(**A__	)

          UpperCamelCase   :  Optional[int]						     =     hidden_size
          UpperCamelCase   :  Tuple						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     num_hidden_layers
          UpperCamelCase   :  Any						     =     num_attention_heads
          UpperCamelCase   :  Optional[int]						     =     patch_size
          UpperCamelCase   :  Union[str, Any]						     =     image_size
          UpperCamelCase   :  Optional[int]						     =     initializer_range
          UpperCamelCase   :  Tuple						     =     attention_dropout
          UpperCamelCase   :  Union[str, Any]						     =     layer_norm_eps
          UpperCamelCase   :  Tuple						     =     hidden_act
          UpperCamelCase   :  Any						     =     qkv_bias






       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          cls._set_token_in_kwargs(A__	)

          UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     cls.get_config_dict(A__,      **A__	)

          # get the vision config dict if we are loading from Blip2Config
          if config_dict.get('model_type'	) == "blip-2":
             UpperCamelCase   :  Any						     =     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(A__,      **A__	)






class     lowerCAmelCase_		(  SCREAMING_SNAKE_CASE__							):
       UpperCAmelCase__    : Optional[int]       					=  '''blip_2_qformer'''
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=1408,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(pad_token_id=A__,      **A__	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  str						     =     hidden_size
          UpperCamelCase   :  Optional[int]						     =     num_hidden_layers
          UpperCamelCase   :  List[str]						     =     num_attention_heads
          UpperCamelCase   :  Union[str, Any]						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  str						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  Dict						     =     initializer_range
          UpperCamelCase   :  str						     =     layer_norm_eps
          UpperCamelCase   :  int						     =     position_embedding_type
          UpperCamelCase   :  int						     =     cross_attention_frequency
          UpperCamelCase   :  int						     =     encoder_hidden_size






       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          cls._set_token_in_kwargs(A__	)

          UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     cls.get_config_dict(A__,      **A__	)

          # get the qformer config dict if we are loading from Blip2Config
          if config_dict.get('model_type'	) == "blip-2":
             UpperCamelCase   :  Dict						     =     config_dict['qformer_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(A__,      **A__	)






class     lowerCAmelCase_		(  SCREAMING_SNAKE_CASE__							):
       UpperCAmelCase__    : List[str]       					=  '''blip-2'''
       UpperCAmelCase__    : Any       					=  True
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=32,      **SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          super().__init__(**A__	)

          if vision_config is None:
             UpperCamelCase   :  str						     =     {}
             logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'	)

          if qformer_config is None:
             UpperCamelCase   :  List[str]						     =     {}
             logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'	)

          if text_config is None:
             UpperCamelCase   :  Optional[int]						     =     {}
             logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'	)

          UpperCamelCase   :  List[str]						     =     BlipaVisionConfig(**A__	)
          UpperCamelCase   :  Optional[int]						     =     BlipaQFormerConfig(**A__	)
          UpperCamelCase   :  Tuple						     =     text_config['model_type'] if 'model_type' in text_config else 'opt'
          UpperCamelCase   :  Tuple						     =     CONFIG_MAPPING[text_model_type](**A__	)

          UpperCamelCase   :  Any						     =     self.text_config.tie_word_embeddings
          UpperCamelCase   :  Any						     =     self.text_config.is_encoder_decoder

          UpperCamelCase   :  Optional[int]						     =     num_query_tokens
          UpperCamelCase   :  List[Any]						     =     self.vision_config.hidden_size
          UpperCamelCase   :  Tuple						     =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          UpperCamelCase   :  str						     =     1.0
          UpperCamelCase   :  List[str]						     =     0.02
       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )					->		List[str]:
          return cls(
              vision_config=vision_config.to_dict(),      qformer_config=qformer_config.to_dict(),      text_config=text_config.to_dict(),      **A__,      )






       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  List[str]						     =     copy.deepcopy(self.__dict__	)
          UpperCamelCase   :  Optional[int]						     =     self.vision_config.to_dict()
          UpperCamelCase   :  Optional[Any]						     =     self.qformer_config.to_dict()
          UpperCamelCase   :  int						     =     self.text_config.to_dict()
          UpperCamelCase   :  int						     =     self.__class__.model_type
          return output




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_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     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	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          # This is highly irregular to have no small tests.
          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'	)



