





'''simple docstring'''




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				TypeVar('''DatasetType''', Dataset, IterableDataset)
def 	UpperCAmelCase_							(					__lowercase							:      List[DatasetType]			,     __lowercase							:      Optional[List[float]] = None			,     __lowercase							:      Optional[int] = None			,     __lowercase							:      Optional[DatasetInfo] = None			,     __lowercase							:      Optional[NamedSplit] = None			,     __lowercase							:      Literal["first_exhausted", "all_exhausted"] = "first_exhausted"			,     )		->       DatasetType:


     '''simple docstring'''



     from .arrow_dataset import Dataset
     from .iterable_dataset import IterableDataset

     if not datasets:
          raise ValueError("Unable to interleave an empty list of datasets."       )
     for i, dataset in enumerate(__lowercase       ):
          if not isinstance(__lowercase			,     (Dataset, IterableDataset)       ):
               if isinstance(__lowercase			,     (DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
                             "is an empty dataset dictionary."       )
                    raise ValueError(
                        f'Dataset at position {i} has at least one split: {list(__lowercase       )}\n'
                        f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowercase       )       )}\']'       )
               raise ValueError(
                   f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowercase       ).__name__}.'       )
          if i == 0:
               _UpperCAmelCase							,  _UpperCAmelCase     							=   (
                   (Dataset, IterableDataset) if isinstance(__lowercase			,     __lowercase       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__lowercase			,     __lowercase       ):
               raise ValueError(
                   f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
     if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
          raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'       )
     if dataset_type is Dataset:
          return _interleave_map_style_datasets(
              __lowercase			,     __lowercase			,     __lowercase			,     info=__lowercase			,     split=__lowercase			,     stopping_strategy=__lowercase       )
     else:
          return _interleave_iterable_datasets(
              __lowercase			,     __lowercase			,     __lowercase			,     info=__lowercase			,     split=__lowercase			,     stopping_strategy=__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[DatasetType]			,     __lowercase							:      Optional[DatasetInfo] = None			,     __lowercase							:      Optional[NamedSplit] = None			,     __lowercase							:      int = 0			,     )		->       DatasetType:


     '''simple docstring'''



     if not dsets:
          raise ValueError("Unable to concatenate an empty list of datasets."       )
     for i, dataset in enumerate(__lowercase       ):
          if not isinstance(__lowercase			,     (Dataset, IterableDataset)       ):
               if isinstance(__lowercase			,     (DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
                             "is an empty dataset dictionary."       )
                    raise ValueError(
                        f'Dataset at position {i} has at least one split: {list(__lowercase       )}\n'
                        f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowercase       )       )}\']'       )
               raise ValueError(
                   f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowercase       ).__name__}.'       )
          if i == 0:
               _UpperCAmelCase							,  _UpperCAmelCase     							=   (
                   (Dataset, IterableDataset) if isinstance(__lowercase			,     __lowercase       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__lowercase			,     __lowercase       ):
               raise ValueError(
                   f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
     if dataset_type is Dataset:
          return _concatenate_map_style_datasets(__lowercase			,     info=__lowercase			,     split=__lowercase			,     axis=__lowercase       )
     else:
          return _concatenate_iterable_datasets(__lowercase			,     info=__lowercase			,     split=__lowercase			,     axis=__lowercase       )











'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()

      _UpperCAmelCase     							=   BlipImageProcessor()
      _UpperCAmelCase     							=   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

      _UpperCAmelCase     							=   BlipProcessor(snake_case_	,			snake_case_    )

      processor.save_pretrained(self.tmpdirname    )




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).tokenizer




 def      lowercase	(					self       :    Dict	,			**snake_case_       :    Any    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).image_processor




 def      lowercase	(					self       :    int    ):
      shutil.rmtree(self.tmpdirname    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   [np.random.randint(2_5_5	,			size=(3, 3_0, 4_0_0)	,			dtype=np.uinta    )]

      _UpperCAmelCase     							=   [Image.fromarray(np.moveaxis(snake_case_	,			0	,			-1    )    ) for x in image_inputs]

      return image_inputs




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   BlipProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      _UpperCAmelCase     							=   self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)"    )
      _UpperCAmelCase     							=   self.get_image_processor(do_normalize=snake_case_	,			padding_value=1.0    )

      _UpperCAmelCase     							=   BlipProcessor.from_pretrained(
          self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=snake_case_	,			padding_value=1.0    )

      self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab()    )
      self.assertIsInstance(processor.tokenizer	,			snake_case_    )

      self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string()    )
      self.assertIsInstance(processor.image_processor	,			snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="np"    )
      _UpperCAmelCase     							=   processor(images=snake_case_	,			return_tensors="np"    )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1e-2    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"

      _UpperCAmelCase     							=   processor(text=snake_case_    )

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_token_type_ids=snake_case_    )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]	,			encoded_processor[key]    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )

      # test if it raises when no input is passed
      with pytest.raises(snake_case_    ):
           processor()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _UpperCAmelCase     							=   processor.batch_decode(snake_case_    )
      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_    )

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )




