"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor




@require_vision
class  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					tempfile.mkdtemp()

            # fmt: off
            snake_case_					       =					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
            # fmt: on
            snake_case_					       =					dict(zip(lowerCAmelCase__,							range(len(lowerCAmelCase__))))
            snake_case_					       =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
            snake_case_					       =					{'unk_token': '<unk>'}

            snake_case_					       =					os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file'])
            snake_case_					       =					os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['merges_file'])
            with open(self.vocab_file,							'w',							encoding='utf-8') as fp:
                 fp.write(json.dumps(lowerCAmelCase__) + '\n')
            with open(self.merges_file,							'w',							encoding='utf-8') as fp:
                 fp.write('\n'.join(lowerCAmelCase__))

            snake_case_					       =					{
                'do_resize': True,
                'size': 20,
                'do_center_crop': True,
                'crop_size': 18,
                'do_normalize': True,
                'image_mean': [0.48145466, 0.4578275, 0.40821073],
                'image_std': [0.26862954, 0.26130258, 0.27577711],
            }
            snake_case_					       =					os.path.join(self.tmpdirname,							lowerCAmelCase__)
            with open(self.image_processor_file,							'w',							encoding='utf-8') as fp:
                 json.dump(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Optional[int]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Tuple:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            return CLIPImageProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[Any]:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					[np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta)]

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

            return image_inputs







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_rust_tokenizer()
            snake_case_					       =					self.get_image_processor()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)
            processor_slow.save_pretrained(self.tmpdirname)
            snake_case_					       =					CLIPProcessor.from_pretrained(self.tmpdirname,							use_fast=lowerCAmelCase__)

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)
            processor_fast.save_pretrained(self.tmpdirname)
            snake_case_					       =					CLIPProcessor.from_pretrained(self.tmpdirname)

            self.assertEqual(processor_slow.tokenizer.get_vocab(),							tokenizer_slow.get_vocab())
            self.assertEqual(processor_fast.tokenizer.get_vocab(),							tokenizer_fast.get_vocab())
            self.assertEqual(tokenizer_slow.get_vocab(),							tokenizer_fast.get_vocab())
            self.assertIsInstance(processor_slow.tokenizer,							lowerCAmelCase__)
            self.assertIsInstance(processor_fast.tokenizer,							lowerCAmelCase__)

            self.assertEqual(processor_slow.image_processor.to_json_string(),							image_processor.to_json_string())
            self.assertEqual(processor_fast.image_processor.to_json_string(),							image_processor.to_json_string())
            self.assertIsInstance(processor_slow.image_processor,							lowerCAmelCase__)
            self.assertIsInstance(processor_fast.image_processor,							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					CLIPProcessor(tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor())
            processor.save_pretrained(self.tmpdirname)

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

            snake_case_					       =					CLIPProcessor.from_pretrained(
                self.tmpdirname,							bos_token='(BOS)',							eos_token='(EOS)',							do_normalize=lowerCAmelCase__,							padding_value=1.0)

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

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







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					image_processor(lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					processor(images=lowerCAmelCase__,							return_tensors='np')

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







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					'lower newer'

            snake_case_					       =					processor(text=lowerCAmelCase__)

            snake_case_					       =					tokenizer(lowerCAmelCase__)

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







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					'lower newer'
            snake_case_					       =					self.prepare_image_inputs()

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

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

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







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

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

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

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()

            snake_case_					       =					CLIPProcessor(tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__)

            snake_case_					       =					'lower newer'
            snake_case_					       =					self.prepare_image_inputs()

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

            self.assertListEqual(list(inputs.keys()),							processor.model_input_names)







'''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


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    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(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (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(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            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(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

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




