





'''simple docstring'''



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 __magic_name__   :						Any    =			None


__magic_name__   :						List[str]    =			logging.get_logger(__name__)

__magic_name__   :						Any    =			{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__   :						str    =			{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__magic_name__   :						str    =			{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Tuple      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						int      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Optional[int]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						int      =   ['''input_ids''', '''attention_mask''']
     UpperCAmelCase__       :						Tuple      =   TaTokenizer

     UpperCAmelCase__       :						List[int]      =   []

     def __init__(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase="</s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase=100						,   lowerCamelCase=None						,   **lowerCamelCase						,   ):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         _snake_case     		=				[F'''<extra_id_{i}>''' for i in range(lowerCamelCase	)]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         _snake_case     		=				len(set(filter(lambda lowerCamelCase	: bool("extra_id_" in str(lowerCamelCase	)	)						,   lowerCamelCase	)	)	)
         if extra_tokens != extra_ids:
           raise ValueError(
               F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
               " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
               " tokens"	)

       super().__init__(
           lowerCamelCase						,   tokenizer_file=lowerCamelCase						,   eos_token=lowerCamelCase						,   unk_token=lowerCamelCase						,   pad_token=lowerCamelCase						,   extra_ids=lowerCamelCase						,   additional_special_tokens=lowerCamelCase						,   **lowerCamelCase						,   )

       _snake_case     		=				vocab_file
       _snake_case     		=				False if not self.vocab_file else True
       _snake_case     		=				extra_ids

     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         _snake_case     		=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
           return init_max_model_length
         elif init_max_model_length is None:
           warnings.warn(
               "This tokenizer was incorrectly instantiated with a model max length of"
               F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
               " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
               " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
               F''' {pretrained_model_name_or_path} automatically truncating your input to'''
               F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
               F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
               " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
               " instantiate this tokenizer with `model_max_length` set to your preferred value."						,   lowerCamelCase						,   )

       return max_model_length

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer."	)

       if not os.path.isdir(lowerCamelCase	):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
         return
       _snake_case     		=				os.path.join(
           lowerCamelCase						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

       if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCamelCase	):
         copyfile(self.vocab_file						,   lowerCamelCase	)
         logger.info(F'''Copy vocab file to {out_vocab_file}'''	)

       return (out_vocab_file,)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
       else:
         _snake_case     		=				token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.eos_token_id]

       if token_ids_a is None:
         return len(token_ids_a + eos	) * [0]
       return len(token_ids_a + eos + token_ids_a + eos	) * [0]

     def 	UpperCamelCase(					self	):
       return list(
           set(filter(lambda lowerCamelCase	: bool(re.search(R"<extra_id_\d+>"						,   lowerCamelCase	)	) is not None						,   self.additional_special_tokens	)	)	)






     def 	UpperCamelCase(					self	):
       return [self.convert_tokens_to_ids(lowerCamelCase	) for token in self.get_sentinel_tokens()]









'''simple docstring'''



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

__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       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 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

       if generate_kwargs is not None:
         _snake_case     		=				generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _snake_case     		=				{}
         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"	)
         _snake_case     		=				max_new_tokens
       return preprocess_params, forward_kwargs, {}

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				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."	)

         _snake_case     		=				self.model.config.model_type

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

         elif model_type == "pix2struct":
           _snake_case     		=				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
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				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:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

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

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       # 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"]	)
       ):
         _snake_case     		=				None

       if generate_kwargs is None:
         _snake_case     		=				{}
       # 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.
       _snake_case     		=				model_inputs.pop(self.model.main_input_name	)
       _snake_case     		=				self.model.generate(lowerCamelCase						,   **lowerCamelCase						,   **lowerCamelCase	)
       return model_outputs






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for output_ids in model_outputs:
         _snake_case     		=				{
             "generated_text": self.tokenizer.decode(
                 lowerCamelCase						,   skip_special_tokens=lowerCamelCase						,   )
         }
         records.append(lowerCamelCase	)
       return records


