



'''simple docstring'''




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


lowerCamelCase							:Dict				    =					logging.get_logger(__name__)


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

lowerCamelCase							:Any				    =					{
    '''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''',
    },
}

lowerCamelCase							:Union[str, Any]				    =					{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		int					=					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE   :		str					=					LEDTokenizer
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					['input_ids', 'attention_mask']


	def __init__(self       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase="replace"       ,      lowercase="<s>"       ,      lowercase="</s>"       ,      lowercase="</s>"       ,      lowercase="<s>"       ,      lowercase="<unk>"       ,      lowercase="<pad>"       ,      lowercase="<mask>"       ,      lowercase=False       ,      lowercase=True       ,      **lowercase       ,      ):
				super().__init__(
				    lowercase       ,      lowercase       ,      tokenizer_file=lowercase       ,      errors=lowercase       ,      bos_token=lowercase       ,      eos_token=lowercase       ,      sep_token=lowercase       ,      cls_token=lowercase       ,      unk_token=lowercase       ,      pad_token=lowercase       ,      mask_token=lowercase       ,      add_prefix_space=lowercase       ,      trim_offsets=lowercase       ,      **lowercase       ,      )

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

				A_	:						Dict	=     add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				A_	:						List[Any]	=     """post_processor"""
				A_	:						Optional[int]	=     getattr(self.backend_tokenizer       ,      lowercase       ,      lowercase							)
				if tokenizer_component_instance:
							A_	:						Any	=     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:
										A_	:						Tuple	=     tuple(state["""sep"""]							)
							if "cls" in state:
										A_	:						List[str]	=     tuple(state["""cls"""]							)

							A_	:						Optional[int]	=     False

							if state.get("""add_prefix_space"""       ,      lowercase							) != add_prefix_space:
										A_	:						List[str]	=     add_prefix_space
										A_	:						Tuple	=     True

							if state.get("""trim_offsets"""       ,      lowercase							) != trim_offsets:
										A_	:						Union[str, Any]	=     trim_offsets
										A_	:						Union[str, Any]	=     True

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


	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def     _a	(self							):
				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     _a	(self       ,      lowercase							):
				A_	:						List[Any]	=     AddedToken(lowercase       ,      lstrip=lowercase       ,      rstrip=lowercase							) if isinstance(lowercase       ,      lowercase							) else value
				A_	:						List[str]	=     value


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				A_	:						Optional[int]	=     kwargs.get("""is_split_into_words"""       ,      lowercase							)

				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(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				A_	:						Optional[Any]	=     kwargs.get("""is_split_into_words"""       ,      lowercase							)

				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(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase = None							):
				A_	:						int	=     self._tokenizer.model.save(lowercase       ,      name=lowercase							)
				return tuple(lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase=None							):
				A_	:						Optional[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     _a	(self       ,      lowercase       ,      lowercase = None							):
				A_	:						Optional[int]	=     [self.sep_token_id]
				A_	:						Optional[int]	=     [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     _a	(self       ,      lowercase       ,      lowercase = None       ,      lowercase = PaddingStrategy.DO_NOT_PAD       ,      lowercase = None       ,      lowercase = None       ,      ):
				A_	:						Any	=     super()._pad(
				    encoded_inputs=lowercase       ,      max_length=lowercase       ,      padding_strategy=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      )

				# Load from model defaults
				if return_attention_mask is None:
							A_	:						List[Any]	=     """attention_mask""" in self.model_input_names

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

							if needs_to_be_padded:
										A_	:						Dict	=     len(lowercase							) - 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`
													A_	:						List[str]	=     (
													    encoded_inputs["""global_attention_mask"""] + [-1] * difference
													)
										elif self.padding_side == "left":
													A_	:						int	=     [-1] * difference + encoded_inputs[
													    """global_attention_mask"""
													]
										else:
													raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

				return encoded_inputs




'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=16       ,      lowercase=2       ,      lowercase=0.02       ,      lowercase=4       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						str	=     batch_size
				A_	:						List[Any]	=     seq_length
				A_	:						Dict	=     is_training
				A_	:						List[Any]	=     use_attention_mask
				A_	:						Any	=     use_token_type_ids
				A_	:						Optional[int]	=     use_labels
				A_	:						Tuple	=     vocab_size
				A_	:						List[str]	=     hidden_size
				A_	:						List[str]	=     num_hidden_layers
				A_	:						Optional[Any]	=     num_attention_heads
				A_	:						int	=     intermediate_size
				A_	:						Optional[Any]	=     hidden_act
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[Any]	=     attention_probs_dropout_prob
				A_	:						Any	=     max_position_embeddings
				A_	:						Union[str, Any]	=     type_vocab_size
				A_	:						int	=     type_sequence_label_size
				A_	:						Any	=     initializer_range
				A_	:						List[str]	=     num_choices


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Any	=     None
				if self.use_attention_mask:
							A_	:						Any	=     random_attention_mask([self.batch_size, self.seq_length]							)

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,      lowercase       ,      atol=1E-4							)							)