

import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_            = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_            = {
    'allenai/led-base-16384': 16384,
}

class 			a					(		UpperCAmelCase			):
      _lowercase        =     VOCAB_FILES_NAMES
      _lowercase        =     PRETRAINED_VOCAB_FILES_MAP
      _lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase        =     LEDTokenizer
      _lowercase        =     ["input_ids", "attention_mask"]


      def __init__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="replace"  ,   A_="<s>"  ,   A_="</s>"  ,   A_="</s>"  ,   A_="<s>"  ,   A_="<unk>"  ,   A_="<pad>"  ,   A_="<mask>"  ,   A_=False  ,   A_=True  ,   **A_  ,   ):







          '''simple docstring'''


          super().__init__(
              A_  ,   A_  ,   tokenizer_file=A_  ,   errors=A_  ,   bos_token=A_  ,   eos_token=A_  ,   sep_token=A_  ,   cls_token=A_  ,   unk_token=A_  ,   pad_token=A_  ,   mask_token=A_  ,   add_prefix_space=A_  ,   trim_offsets=A_  ,   **A_  ,   )

          _UpperCAmelCase    :						Any							=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
          if pre_tok_state.get("add_prefix_space"  ,   A_		) != add_prefix_space:
              _UpperCAmelCase    :						Any							=      getattr(A_  ,   pre_tok_state.pop("type"		)		)
              _UpperCAmelCase    :						Dict							=      add_prefix_space
              _UpperCAmelCase    :						List[str]							=      pre_tok_class(**A_		)

          _UpperCAmelCase    :						Any							=      add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _UpperCAmelCase    :						List[str]							=      "post_processor"
          _UpperCAmelCase    :						List[Any]							=      getattr(self.backend_tokenizer  ,   A_  ,   A_		)
          if tokenizer_component_instance:
              _UpperCAmelCase    :						Optional[int]							=      json.loads(tokenizer_component_instance.__getstate__()		)

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                  _UpperCAmelCase    :						Dict							=      tuple(state["sep"]		)
              if "cls" in state:
                  _UpperCAmelCase    :						Optional[Any]							=      tuple(state["cls"]		)

              _UpperCAmelCase    :						Tuple							=      False

              if state.get("add_prefix_space"  ,   A_		) != add_prefix_space:
                  _UpperCAmelCase    :						Optional[Any]							=      add_prefix_space
                  _UpperCAmelCase    :						Dict							=      True

              if state.get("trim_offsets"  ,   A_		) != trim_offsets:
                  _UpperCAmelCase    :						Optional[int]							=      trim_offsets
                  _UpperCAmelCase    :						Tuple							=      True

              if changes_to_apply:
                  _UpperCAmelCase    :						Union[str, Any]							=      getattr(A_  ,   state.pop("type"		)		)
                  _UpperCAmelCase    :						Union[str, Any]							=      component_class(**A_		)
                  setattr(self.backend_tokenizer  ,   A_  ,   A_		)


      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def     _UpperCAmelCase			(    self		):







          '''simple docstring'''


          if self._mask_token is None:
              if self.verbose:
                  logger.error("Using mask_token, but it is not set yet."		)
              return None
          return str(self._mask_token		)


      @mask_token.setter
      def     _UpperCAmelCase			(    self  ,   A_		):







          '''simple docstring'''


          _UpperCAmelCase    :						Any							=      AddedToken(A_  ,   lstrip=A_  ,   rstrip=A_		) if isinstance(A_  ,   A_		) else value
          _UpperCAmelCase    :						Optional[int]							=      value


      def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







          '''simple docstring'''


          _UpperCAmelCase    :						Optional[int]							=      kwargs.get("is_split_into_words"  ,   A_		)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                  "to use it with pretokenized inputs."		)

          return super()._batch_encode_plus(*A_  ,   **A_		)


      def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







          '''simple docstring'''


          _UpperCAmelCase    :						Optional[Any]							=      kwargs.get("is_split_into_words"  ,   A_		)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                  "to use it with pretokenized inputs."		)

          return super()._encode_plus(*A_  ,   **A_		)


      def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







          '''simple docstring'''


          _UpperCAmelCase    :						List[str]							=      self._tokenizer.model.save(A_  ,   name=A_		)
          return tuple(A_		)


      def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None		):







          '''simple docstring'''


          _UpperCAmelCase    :						Any							=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
              return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


      def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







          '''simple docstring'''


          _UpperCAmelCase    :						List[Any]							=      [self.sep_token_id]
          _UpperCAmelCase    :						List[str]							=      [self.cls_token_id]

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





      def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = PaddingStrategy.DO_NOT_PAD  ,   A_ = None  ,   A_ = None  ,   ):







          '''simple docstring'''


          _UpperCAmelCase    :						Any							=      super()._pad(
              encoded_inputs=A_  ,   max_length=A_  ,   padding_strategy=A_  ,   pad_to_multiple_of=A_  ,   return_attention_mask=A_  ,   )

          # Load from model defaults
          if return_attention_mask is None:
              _UpperCAmelCase    :						Union[str, Any]							=      "attention_mask" in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
              _UpperCAmelCase    :						List[Any]							=      encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              _UpperCAmelCase    :						Any							=      len(encoded_inputs["global_attention_mask"]		) != len(A_		)

              if needs_to_be_padded:
                  _UpperCAmelCase    :						Any							=      len(A_		) - len(encoded_inputs["global_attention_mask"]		)

                  if self.padding_side == "right":
                      # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                      _UpperCAmelCase    :						Union[str, Any]							=      (
                          encoded_inputs["global_attention_mask"] + [-1] * difference
                      )
                  elif self.padding_side == "left":
                      _UpperCAmelCase    :						str							=      [-1] * difference + encoded_inputs[
                          "global_attention_mask"
                      ]
                  else:
                      raise ValueError("Invalid padding strategy:" + str(self.padding_side		)		)

          return encoded_inputs





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


