



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_		 =					get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")


@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Any =  PegasusTokenizer
 a__					: Any =  PegasusTokenizerFast
 a__					: int =  True
 a__					: Union[str, Any] =  True

 def     snake_case_      (    self					:					Tuple      )   ->	List[Any]:
    super().setUp()

    # We have a SentencePiece fixture for testing
    _A      =       PegasusTokenizer(__lowerCAmelCase      )
    tokenizer.save_pretrained(self.tmpdirname      )

 @cached_property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	int:
    return PegasusTokenizer.from_pretrained('''google/pegasus-large'''      )

 def     snake_case_      (    self					:					Union[str, Any]			,						**__lowerCAmelCase					:					Any      )   ->	PegasusTokenizer:
    return PegasusTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[Any]      )   ->	List[Any]:
    return ("This is a test", "This is a test")

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    _A      =       '''</s>'''
    _A      =       1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase      )			,						__lowerCAmelCase      )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase      )			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[str]:
    _A      =       list(self.get_tokenizer().get_vocab().keys()      )

    self.assertEqual(vocab_keys[0]			,						'''<pad>'''      )
    self.assertEqual(vocab_keys[1]			,						'''</s>'''      )
    self.assertEqual(vocab_keys[-1]			,						'''v'''      )
    self.assertEqual(len(__lowerCAmelCase      )			,						11_03      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    self.assertEqual(self.get_tokenizer().vocab_size			,						11_03      )

 def     snake_case_      (    self					:					Any      )   ->	int:
    _A      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
    _A      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
    _A      =       (
        '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
        ''' </s> <pad> <pad> <pad>'''
    )
    _A      =       rust_tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids[0]
    _A      =       py_tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids[0]
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[Any]:
    _A      =       self._large_tokenizer
    # <mask_1> masks whole sentence while <mask_2> masks single word
    _A      =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
    _A      =       [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
    _A      =       tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase      ).input_ids[0]
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    _A      =       self._large_tokenizer
    # The tracebacks for the following asserts are **better** without messages or self.assertEqual
    assert tokenizer.vocab_size == 9_61_03
    assert tokenizer.pad_token_id == 0
    assert tokenizer.eos_token_id == 1
    assert tokenizer.offset == 1_03
    assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
    assert tokenizer.unk_token == "<unk>"
    assert tokenizer.model_max_length == 10_24
    _A      =       '''To ensure a smooth flow of bank resolutions.'''
    _A      =       [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
    _A      =       tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase      ).input_ids[0]
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )
    assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

 @require_torch
 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _A      =       ['''This is going to be way too long.''' * 1_50, '''short example''']
    _A      =       ['''not super long but more than 5 tokens''', '''tiny''']
    _A      =       self._large_tokenizer(__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''      )
    _A      =       self._large_tokenizer(
        text_target=__lowerCAmelCase			,						max_length=5			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''      )

    assert batch.input_ids.shape == (2, 10_24)
    assert batch.attention_mask.shape == (2, 10_24)
    assert targets["input_ids"].shape == (2, 5)
    assert len(__lowerCAmelCase      ) == 2  # input_ids, attention_mask.

 @slow
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Tuple:
    # fmt: off
    _A      =       {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__lowerCAmelCase			,						model_name='''google/bigbird-pegasus-large-arxiv'''			,						revision='''ba85d0851d708441f91440d509690f1ab6353415'''			,						)


@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: List[str] =  PegasusTokenizer
 a__					: Dict =  PegasusTokenizerFast
 a__					: Optional[int] =  True
 a__					: Any =  True

 def     snake_case_      (    self					:					int      )   ->	Any:
    super().setUp()

    # We have a SentencePiece fixture for testing
    _A      =       PegasusTokenizer(__lowerCAmelCase			,						offset=0			,						mask_token_sent=__lowerCAmelCase			,						mask_token='''[MASK]'''      )
    tokenizer.save_pretrained(self.tmpdirname      )

 @cached_property
 def     snake_case_      (    self					:					Dict      )   ->	str:
    return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''      )

 def     snake_case_      (    self					:					Any			,						**__lowerCAmelCase					:					Tuple      )   ->	PegasusTokenizer:
    return PegasusTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[int]:
    return ("This is a test", "This is a test")

 def     snake_case_      (    self					:					Any      )   ->	Optional[Any]:
    _A      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
    _A      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
    _A      =       (
        '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
        ''' <pad> <pad> <pad>'''
    )
    _A      =       rust_tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids[0]
    _A      =       py_tokenizer([raw_input_str]			,						return_tensors=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids[0]
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 @require_torch
 def     snake_case_      (    self					:					List[Any]      )   ->	str:
    _A      =       ['''This is going to be way too long.''' * 10_00, '''short example''']
    _A      =       ['''not super long but more than 5 tokens''', '''tiny''']
    _A      =       self._large_tokenizer(__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''      )
    _A      =       self._large_tokenizer(
        text_target=__lowerCAmelCase			,						max_length=5			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''      )

    assert batch.input_ids.shape == (2, 40_96)
    assert batch.attention_mask.shape == (2, 40_96)
    assert targets["input_ids"].shape == (2, 5)
    assert len(__lowerCAmelCase      ) == 2  # input_ids, attention_mask.

 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    _A      =       (
        '''This is an example string that is used to test the original TF implementation against the HF'''
        ''' implementation'''
    )

    _A      =       self._large_tokenizer(__lowerCAmelCase      ).input_ids

    self.assertListEqual(
        __lowerCAmelCase			,						[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]			,						)








from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    super().__init__(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    requires_backends(self			,						'''vision'''      )
    self.check_model_type(
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       load_image(__lowerCAmelCase      )

    if prompt is not None:
       if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          raise ValueError(
              f'''Received an invalid text input, got - {type(__lowerCAmelCase      )} - but expected a single string. '''
              '''Note also that one single text can be provided for conditional image to text generation.'''      )

       _A      =       self.model.config.model_type

       if model_type == "git":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids
          _A      =       [self.tokenizer.cls_token_id] + input_ids
          _A      =       torch.tensor(__lowerCAmelCase      ).unsqueeze(0      )
          model_inputs.update({'''input_ids''': input_ids}      )

       elif model_type == "pix2struct":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						header_text=__lowerCAmelCase			,						return_tensors=self.framework      )

       elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(__lowerCAmelCase			,						return_tensors=self.framework      )
          model_inputs.update(__lowerCAmelCase      )

       else:
          raise ValueError(f'''Model type {model_type} does not support conditional text generation'''      )

    else:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

    if self.model.config.model_type == "git" and prompt is None:
       _A      =       None

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
    # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
    if (
        "input_ids" in model_inputs
        and isinstance(model_inputs['''input_ids''']			,						__lowerCAmelCase      )
        and all(x is None for x in model_inputs['''input_ids''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
    #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
    #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
    #  in the `_prepare_model_inputs` method.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



