import os
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_pegasus import PegasusTokenizer
else:
		__magic_name__:					Dict							=     None


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


__magic_name__:					Union[str, Any]							=     "▁"

__magic_name__:					Optional[int]							=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__magic_name__:					Any							=     {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

__magic_name__:					int							=     {
    "google/pegasus-xsum": 512,
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					List[Any]											=   VOCAB_FILES_NAMES
	lowercase__						:					Union[str, Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Optional[Any]											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					str											=   PegasusTokenizer
	lowercase__						:					Optional[int]											=   ['''input_ids''', '''attention_mask''']
	def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<mask_2>"      ,       lowerCAmelCase__="<mask_1>"      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=1_03      ,       **lowerCAmelCase__      ,       )							->				Union[str, Any]:
							__magic_name__      :       Optional[Any]       						= offset

							if additional_special_tokens is not None:
													if not isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			raise TypeError(
																			    F'additional_special_tokens should be of type {type(lowerCAmelCase__			)}, but is'
																			    F' {type(lowerCAmelCase__			)}'			)

													__magic_name__      :       Optional[Any]       						= (
													    ([mask_token_sent] + additional_special_tokens)
													    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
													    else additional_special_tokens
													)
													# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
													additional_special_tokens_extended += [
													    F'<unk_{i}>' for i in range(len(lowerCAmelCase__			)      ,       self.offset - 1			)
													]

													if len(set(lowerCAmelCase__			)			) != len(lowerCAmelCase__			):
																			raise ValueError(
																			    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																			    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'			)
													__magic_name__      :       Union[str, Any]       						= additional_special_tokens_extended
							else:
													__magic_name__      :       Any       						= [mask_token_sent] if mask_token_sent is not None else []
													additional_special_tokens += [F'<unk_{i}>' for i in range(2      ,       self.offset			)]

							super().__init__(
							    lowerCAmelCase__      ,       tokenizer_file=lowerCAmelCase__      ,       pad_token=lowerCAmelCase__      ,       eos_token=lowerCAmelCase__      ,       unk_token=lowerCAmelCase__      ,       mask_token=lowerCAmelCase__      ,       mask_token_sent=lowerCAmelCase__      ,       offset=lowerCAmelCase__      ,       additional_special_tokens=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )
							__magic_name__      :       Any       						= vocab_file
							__magic_name__      :       Tuple       						= False if not self.vocab_file else True
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= set(self.all_special_ids			)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id			)  # <unk> is only sometimes special

							if all_special_ids != set(range(len(self.additional_special_tokens			) + 3			)			):
													raise ValueError(
													    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
													    F' {len(self.additional_special_tokens			)} additional_special_tokens, but got {all_special_ids}'			)

							return [1 if x in all_special_ids else 0 for x in seq]
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = False			)							->				List[int]:
							if already_has_special_tokens:
													return self._special_token_mask(lowerCAmelCase__			)
							elif token_ids_a is None:
													return self._special_token_mask(lowerCAmelCase__			) + [1]
							else:
													return self._special_token_mask(token_ids_a + token_ids_a			) + [1]
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None			)							->				List[int]:
							if token_ids_a is None:
													return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							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
							__magic_name__      :       Optional[int]       						= 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__			)

							return (out_vocab_file,)








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
