


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 _a					(			*A_					,   **A_    )  ->       Union[str, Any]:
            pass

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Image				):
     __UpperCamelCase    							=hashlib.mda(image.tobytes()				)
     return m.hexdigest()






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



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=DepthEstimationPipeline(model=A_					,   image_processor=A_    )
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]






    def _a					(			self					,   A_					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
         self.assertEqual({'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )}					,   A_    )
         import datasets

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






    @require_tf
    @unittest.skip('Depth estimation is not implemented in TF'    )
    def _a					(			self    )  ->       Optional[Any]:
         pass






    @slow
    @require_torch
    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							='Intel/dpt-large'
         __UpperCamelCase    							=pipeline('depth-estimation'					,   model=A_    )
         __UpperCamelCase    							=depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'    )
         __UpperCamelCase    							=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 _a					(			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'    )









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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip_vision_model"






    def __init__(			self					,   A_=1408					,   A_=6144					,   A_=39					,   A_=16					,   A_=224					,   A_=14					,   A_="gelu"					,   A_=1E-6					,   A_=0.0					,   A_=1E-10					,   A_=True					,   **A_					,   )  ->       Tuple:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=qkv_bias






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  "instructblip_qformer"






    def __init__(			self					,   A_=30522					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=0.02					,   A_=1E-12					,   A_=0					,   A_="absolute"					,   A_=2					,   A_=1408					,   **A_					,   )  ->       Optional[Any]:
         super().__init__(pad_token_id=A_					,   **A_    )

         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=position_embedding_type
         __UpperCamelCase    							=cross_attention_frequency
         __UpperCamelCase    							=encoder_hidden_size






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip"
    UpperCAmelCase__						:		Optional[Any]      =  True






    def __init__(			self					,   A_=None					,   A_=None					,   A_=None					,   A_=32					,   **A_    )  ->       List[str]:
         super().__init__(**A_    )

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

         if qformer_config is None:
              __UpperCamelCase    							={}
              logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'    )

         if text_config is None:
              __UpperCamelCase    							={}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'    )

         __UpperCamelCase    							=InstructBlipVisionConfig(**A_    )
         __UpperCamelCase    							=InstructBlipQFormerConfig(**A_    )
         __UpperCamelCase    							=text_config['model_type'] if 'model_type' in text_config else 'opt'
         __UpperCamelCase    							=CONFIG_MAPPING[text_model_type](**A_    )

         __UpperCamelCase    							=self.text_config.tie_word_embeddings
         __UpperCamelCase    							=self.text_config.is_encoder_decoder

         __UpperCamelCase    							=num_query_tokens
         __UpperCamelCase    							=self.vision_config.hidden_size
         __UpperCamelCase    							=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         __UpperCamelCase    							=1.0
         __UpperCamelCase    							=0.02






    @classmethod
    def _a					(			cls					,   A_					,   A_					,   A_					,   **A_					,   )  ->       Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()					,   qformer_config=qformer_config.to_dict()					,   text_config=text_config.to_dict()					,   **A_					,   )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         __UpperCamelCase    							=self.vision_config.to_dict()
         __UpperCamelCase    							=self.qformer_config.to_dict()
         __UpperCamelCase    							=self.text_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output





