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

A   :     Dict       =				logging.get_logger(__name__)


@add_end_docstrings(SCREAMING_SNAKE_CASE__    )
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  def __init__(							self       :				List[str]     ,   *__magic_name__       :				Any     ,   **__magic_name__       :				List[str]      )     ->		List[str]:
        super().__init__(*__magic_name__     ,   **__magic_name__      )
        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       __A   (							self       :				List[str]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				List[str]=None      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     {}

        if prompt is not None:
              SCREAMING_SNAKE_CASE_      =     prompt

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







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







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Any=None      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     load_image(__magic_name__      )

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

              SCREAMING_SNAKE_CASE_      =     self.model.config.model_type

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

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

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

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

        else:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(images=__magic_name__     ,   return_tensors=self.framework      )

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

        return model_inputs







  def       __A   (							self       :				Dict     ,   __magic_name__       :				Tuple     ,   __magic_name__       :				List[str]=None      )     ->		int:
        # 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"]     ,   __magic_name__      )
            and all(x is None for x in model_inputs["input_ids"]      )
        ):
              SCREAMING_SNAKE_CASE_      =     None

        if generate_kwargs is None:
              SCREAMING_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.
        SCREAMING_SNAKE_CASE_      =     model_inputs.pop(self.model.main_input_name      )
        SCREAMING_SNAKE_CASE_      =     self.model.generate(__magic_name__     ,   **__magic_name__     ,   **__magic_name__      )
        return model_outputs







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				Any      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     []
        for output_ids in model_outputs:
              SCREAMING_SNAKE_CASE_      =     {
                  "generated_text": self.tokenizer.decode(
                      __magic_name__     ,   skip_special_tokens=__magic_name__     ,   )
              }
              records.append(__magic_name__      )
        return records



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  DebertaTokenizer
  lowerCamelCase__   =  True
  lowerCamelCase__   =  DebertaTokenizerFast







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE_      =     [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "[UNK]",
        ]
        SCREAMING_SNAKE_CASE_      =     dict(zip(__magic_name__     ,   range(len(__magic_name__      )      )      )      )
        SCREAMING_SNAKE_CASE_      =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        SCREAMING_SNAKE_CASE_      =     {"unk_token": "[UNK]"}

        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        SCREAMING_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(__magic_name__      ) + "\n"      )
        with open(self.merges_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write("\n".join(__magic_name__      )      )







  def       __A   (							self       :				str     ,   **__magic_name__       :				int      )     ->		Union[str, Any]:
        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				List[Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        return input_text, output_text







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_      =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     tokenizer("Hello"     ,   "World"      )
        SCREAMING_SNAKE_CASE_      =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["token_type_ids"]     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Any      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     self.tokenizer_class.from_pretrained("microsoft/deberta-base"      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("sequence builders"     ,   add_special_tokens=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("multi-sequence build"     ,   add_special_tokens=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   "multi-sequence build"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__     ,   __magic_name__      )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







  @slow
  def       __A   (							self       :				Tuple      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     [self.tokenizer_class]
        if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class      )

        for tokenizer_class in tokenizer_classes:
              SCREAMING_SNAKE_CASE_      =     tokenizer_class.from_pretrained("microsoft/deberta-base"      )

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   padding=__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     [tokenizer.decode(__magic_name__     ,   skip_special_tokens=__magic_name__      ) for seq in encoding["input_ids"]]

              # fmt: off
              SCREAMING_SNAKE_CASE_      =     {
                  "input_ids": [
                      [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                  ],
                  "token_type_ids": [
                      [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, 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, 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, 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, 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]
                  ]
              }
              # fmt: on

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              self.assertDictEqual(encoding.data     ,   __magic_name__      )

              for expected, decoded in zip(__magic_name__     ,   __magic_name__      ):
                    self.assertEqual(__magic_name__     ,   __magic_name__      )


