




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 			a__    (					snake_case__      ):
						_a							:    List[Any]					=    ["""image_processor""", """tokenizer"""]
						_a							:    int					=    """OwlViTImageProcessor"""
						_a							:    Optional[Any]					=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")






						def __init__(   self		,			_A=None		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."		,			_A		,			)
																__lowerCAmelCase				   =       kwargs.pop("feature_extractor"			)

											__lowerCAmelCase				   =       image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("You need to specify an `image_processor`."			)
											if tokenizer is None:
																raise ValueError("You need to specify a `tokenizer`."			)

											super().__init__(_A		,			_A			)






						def __call__(   self		,			_A=None		,			_A=None		,			_A=None		,			_A="max_length"		,			_A="np"		,			**_A			):




											"""simple docstring"""



											if text is None and query_images is None and images is None:
																raise ValueError(
																    "You have to specify at least one text or query image or image. All three cannot be none."			)

											if text is not None:
																if isinstance(_A		,			_A			) or (isinstance(_A		,			_A			) and not isinstance(text[0]		,			_A			)):
																					__lowerCAmelCase				   =       [self.tokenizer(_A		,			padding=_A		,			return_tensors=_A		,			**_A			)]

																elif isinstance(_A		,			_A			) and isinstance(text[0]		,			_A			):
																					__lowerCAmelCase				   =       []

																					# Maximum number of queries across batch
																					__lowerCAmelCase				   =       max([len(_A			) for t in text]			)

																					# Pad all batch samples to max number of text queries
																					for t in text:
																										if len(_A			) != max_num_queries:
																															__lowerCAmelCase				   =       t + [" "] * (max_num_queries - len(_A			))

																										__lowerCAmelCase				   =       self.tokenizer(_A		,			padding=_A		,			return_tensors=_A		,			**_A			)
																										encodings.append(_A			)
																else:
																					raise TypeError("Input text should be a string, a list of strings or a nested list of strings"			)

																if return_tensors == "np":
																					__lowerCAmelCase				   =       np.concatenate([encoding["input_ids"] for encoding in encodings]		,			axis=0			)
																					__lowerCAmelCase				   =       np.concatenate([encoding["attention_mask"] for encoding in encodings]		,			axis=0			)

																elif return_tensors == "jax" and is_flax_available():
																					import jax.numpy as jnp

																					__lowerCAmelCase				   =       jnp.concatenate([encoding["input_ids"] for encoding in encodings]		,			axis=0			)
																					__lowerCAmelCase				   =       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]		,			axis=0			)

																elif return_tensors == "pt" and is_torch_available():
																					import torch

																					__lowerCAmelCase				   =       torch.cat([encoding["input_ids"] for encoding in encodings]		,			dim=0			)
																					__lowerCAmelCase				   =       torch.cat([encoding["attention_mask"] for encoding in encodings]		,			dim=0			)

																elif return_tensors == "tf" and is_tf_available():
																					import tensorflow as tf

																					__lowerCAmelCase				   =       tf.stack([encoding["input_ids"] for encoding in encodings]		,			axis=0			)
																					__lowerCAmelCase				   =       tf.stack([encoding["attention_mask"] for encoding in encodings]		,			axis=0			)

																else:
																					raise ValueError("Target return tensor type could not be returned"			)

																__lowerCAmelCase				   =       BatchEncoding()
																__lowerCAmelCase				   =       input_ids
																__lowerCAmelCase				   =       attention_mask

											if query_images is not None:
																__lowerCAmelCase				   =       BatchEncoding()
																__lowerCAmelCase				   =       self.image_processor(
																    _A		,			return_tensors=_A		,			**_A			).pixel_values
																__lowerCAmelCase				   =       query_pixel_values

											if images is not None:
																__lowerCAmelCase				   =       self.image_processor(_A		,			return_tensors=_A		,			**_A			)

											if text is not None and images is not None:
																__lowerCAmelCase				   =       image_features.pixel_values
																return encoding
											elif query_images is not None and images is not None:
																__lowerCAmelCase				   =       image_features.pixel_values
																return encoding
											elif text is not None or query_images is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**_A			)		,			tensor_type=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process_object_detection(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process_image_guided_detection(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.tokenizer.batch_decode(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.tokenizer.decode(*_A		,			**_A			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			_A		,			)
											return self.image_processor_class






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			_A		,			)
											return self.image_processor











'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




