'''simple docstring'''




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__SCREAMING_SNAKE_CASE       :     List[Any]     =  get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     List[Any]     =  R"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""

class 	lowerCamelCase_	:
      '''simple docstring'''

      @add_start_docstrings(A       )
      def __call__(       self						:				Dict						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray       ):
             raise NotImplementedError(
                 F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""       )

class 	lowerCamelCase_	:
      '''simple docstring'''

      @add_start_docstrings(A       )
      def __call__(       self						:				Dict						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray       ):
             raise NotImplementedError(
                 F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @add_start_docstrings(A       )
      def __call__(       self						:				Union[str, Any]						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int						,					**A						:				str       ):
             for processor in self:
                    _UpperCAmelCase     :  Optional[int]							      =    inspect.signature(processor.__call__       ).parameters
                    if len(A       ) > 3:
                           if not all(arg in kwargs for arg in list(function_args.keys()       )[2:]       ):
                                  raise ValueError(
                                      F"""Make sure that all the required parameters: {list(function_args.keys()       )} for """
                                      F"""{processor.__class__} are passed to the logits processor."""       )
                           _UpperCAmelCase     :  int							      =    processor(A						,					A						,					A						,					**A       )
                    else:
                           _UpperCAmelCase     :  List[str]							      =    processor(A						,					A						,					A       )
             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				List[str]						,					A						:				float       ):
             if not isinstance(A						,					A       ) or not (temperature > 0):
                    raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""       )

             _UpperCAmelCase     :  str							      =    temperature





      def __call__(       self						:				List[str]						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    scores / self.temperature
             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				float						,					A						:				float = -float("Inf"       )						,					A						:				int = 1       ):
             if not isinstance(A						,					A       ) or (top_p < 0 or top_p > 1.0):
                    raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""       )
             if not isinstance(A						,					A       ) or (min_tokens_to_keep < 1):
                    raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""       )

             _UpperCAmelCase     :  Dict							      =    top_p
             _UpperCAmelCase     :  Any							      =    filter_value
             _UpperCAmelCase     :  int							      =    min_tokens_to_keep





      def __call__(       self						:				Tuple						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    lax.top_k(A						,					scores.shape[-1]       )

             _UpperCAmelCase     :  List[str]							      =    jnp.full_like(A						,					self.filter_value       )
             _UpperCAmelCase     :  int							      =    jax.nn.softmax(A						,					axis=-1       ).cumsum(axis=-1       )
             _UpperCAmelCase     :  Optional[int]							      =    cumulative_probs < self.top_p

             # include the token that is higher than top_p as well
             _UpperCAmelCase     :  Union[str, Any]							      =    jnp.roll(A						,					1       )
             score_mask |= score_mask.at[:, 0].set(A       )

             # min tokens to keep
             _UpperCAmelCase     :  Tuple							      =    score_mask.at[:, : self.min_tokens_to_keep].set(A       )

             _UpperCAmelCase     :  Dict							      =    jnp.where(A						,					A						,					A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    jax.lax.sort_key_val(A						,					A       )[-1]

             return next_scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Union[str, Any]						,					A						:				int						,					A						:				float = -float("Inf"       )						,					A						:				int = 1       ):
             if not isinstance(A						,					A       ) or top_k <= 0:
                    raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""       )

             _UpperCAmelCase     :  Optional[Any]							      =    max(A						,					A       )
             _UpperCAmelCase     :  List[str]							      =    filter_value





      def __call__(       self						:				Dict						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    scores.shape
             _UpperCAmelCase     :  List[Any]							      =    jnp.full(batch_size * vocab_size						,					self.filter_value       )

             _UpperCAmelCase     :  int							      =    min(self.top_k						,					scores.shape[-1]       )  # Safety check
             _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    lax.top_k(A						,					A       )
             _UpperCAmelCase     :  Optional[int]							      =    jnp.broadcast_to((jnp.arange(A       ) * vocab_size)[:, None]						,					(batch_size, topk)       ).flatten()
             _UpperCAmelCase     :  List[str]							      =    topk_scores.flatten()
             _UpperCAmelCase     :  Tuple							      =    topk_indices.flatten() + shift

             _UpperCAmelCase     :  Optional[int]							      =    next_scores_flat.at[topk_indices_flat].set(A       )
             _UpperCAmelCase     :  Any							      =    next_scores_flat.reshape(A						,					A       )
             return next_scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    bos_token_id





      def __call__(       self						:				Tuple						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase     :  str							      =    jnp.full(scores.shape						,					-float("inf"       )       )

             _UpperCAmelCase     :  int							      =    1 - jnp.bool_(cur_len - 1       )

             _UpperCAmelCase     :  List[str]							      =    jnp.where(A						,					new_scores.at[:, self.bos_token_id].set(0       )						,					A       )

             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Dict						,					A						:				int						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    max_length
             _UpperCAmelCase     :  Union[str, Any]							      =    eos_token_id





      def __call__(       self						:				List[Any]						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    jnp.full(scores.shape						,					-float("inf"       )       )

             _UpperCAmelCase     :  List[str]							      =    1 - jnp.bool_(cur_len - self.max_length + 1       )

             _UpperCAmelCase     :  str							      =    jnp.where(A						,					new_scores.at[:, self.eos_token_id].set(0       )						,					A       )

             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				int						,					A						:				int       ):
             if not isinstance(A						,					A       ) or min_length < 0:
                    raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""       )

             if not isinstance(A						,					A       ) or eos_token_id < 0:
                    raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""       )

             _UpperCAmelCase     :  Tuple							      =    min_length
             _UpperCAmelCase     :  int							      =    eos_token_id





      def __call__(       self						:				int						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             # create boolean flag to decide if min length penalty should be applied
             _UpperCAmelCase     :  str							      =    1 - jnp.clip(cur_len - self.min_length						,					0						,					1       )

             _UpperCAmelCase     :  str							      =    jnp.where(A						,					scores.at[:, self.eos_token_id].set(-float("inf"       )       )						,					A       )

             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				str						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    list(A       )
             _UpperCAmelCase     :  str							      =    begin_index





      def __call__(       self						:				Optional[Any]						,					A						:				Optional[Any]						,					A						:				List[Any]						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    1 - jnp.bool_(cur_len - self.begin_index       )

             _UpperCAmelCase     :  Any							      =    jnp.where(A						,					scores.at[:, self.begin_suppress_tokens].set(-float("inf"       )       )						,					A       )

             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Union[str, Any]						,					A						:				list       ):
             _UpperCAmelCase     :  Any							      =    list(A       )





      def __call__(       self						:				str						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    scores.at[..., self.suppress_tokens].set(-float("inf"       )       )

             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    dict(A       )
             # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
             # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
             # Indexes without forced tokens will have a negative value.
             _UpperCAmelCase     :  str							      =    jnp.ones((max(force_token_map.keys()       ) + 1)						,					dtype=jnp.intaa       ) * -1
             for index, token in force_token_map.items():
                    if token is not None:
                           _UpperCAmelCase     :  Dict							      =    force_token_array.at[index].set(A       )
             _UpperCAmelCase     :  Any							      =    jnp.intaa(A       )





      def __call__(       self						:				List[Any]						,					A						:				jnp.ndarray						,					A						:				jnp.ndarray						,					A						:				int       ):
             def _force_token(A						:				str       ):
                    _UpperCAmelCase     :  List[str]							      =    scores.shape[0]
                    _UpperCAmelCase     :  Optional[Any]							      =    self.force_token_array[generation_idx]

                    _UpperCAmelCase     :  Dict							      =    jnp.ones_like(A						,					dtype=scores.dtype       ) * -float("inf"       )
                    _UpperCAmelCase     :  List[str]							      =    jnp.zeros((batch_size, 1)						,					dtype=scores.dtype       )
                    _UpperCAmelCase     :  int							      =    lax.dynamic_update_slice(A						,					A						,					(0, current_token)       )
                    return new_scores

             _UpperCAmelCase     :  Tuple							      =    lax.cond(
                 cur_len >= self.force_token_array.shape[0]						,					lambda: scores						,					lambda: lax.cond(
                     self.force_token_array[cur_len] >= 0						,					lambda: _force_token(A       )						,					lambda: scores						,					)						,					)
             return scores

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					A						:				Dict						,					A						:				int						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    generate_config.eos_token_id
             _UpperCAmelCase     :  List[Any]							      =    generate_config.no_timestamps_token_id
             _UpperCAmelCase     :  Optional[int]							      =    generate_config.no_timestamps_token_id + 1

             _UpperCAmelCase     :  Union[str, Any]							      =    decoder_input_length + 1

             if generate_config.is_multilingual:
                    # room for language token and task token
                    self.begin_index += 2
             if hasattr(A						,					"max_initial_timestamp_index"       ):
                    _UpperCAmelCase     :  Optional[int]							      =    generate_config.max_initial_timestamp_index
             else:
                    _UpperCAmelCase     :  Dict							      =    model_config.vocab_size
             if self.max_initial_timestamp_index is None:
                    _UpperCAmelCase     :  str							      =    model_config.vocab_size





      def __call__(       self						:				str						,					A						:				Dict						,					A						:				int						,					A						:				List[Any]       ):
             # suppress <|notimestamps|> which is handled by without_timestamps
             _UpperCAmelCase     :  Tuple							      =    scores.at[:, self.no_timestamps_token_id].set(-float("inf"       )       )

             def handle_pairs(A						:				Union[str, Any]						,					A						:				List[str]       ):
                    _UpperCAmelCase     :  Optional[Any]							      =    jnp.where((cur_len - self.begin_index) >= 1						,					A						,					A       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    jnp.where(
                        input_ids_k[cur_len - 1] >= self.timestamp_begin						,					True and last_was_timestamp						,					A						,					)

                    _UpperCAmelCase     :  List[Any]							      =    jnp.where((cur_len - self.begin_index) < 2						,					A						,					A       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    jnp.where(
                        input_ids_k[cur_len - 2] >= self.timestamp_begin						,					A						,					A						,					)

                    return jnp.where(
                        A						,					jnp.where(
                            penultimate_was_timestamp > 0						,					scores_k.at[self.timestamp_begin :].set(-float("inf"       )       )						,					scores_k.at[: self.eos_token_id].set(-float("inf"       )       )						,					)						,					A						,					)

             _UpperCAmelCase     :  Optional[int]							      =    jax.vmap(A       )(A						,					A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    jnp.where(cur_len == self.begin_index						,					A						,					A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    jnp.where(
                 self.max_initial_timestamp_index is not None						,					True and apply_max_initial_timestamp						,					A						,					)

             _UpperCAmelCase     :  Tuple							      =    self.timestamp_begin + self.max_initial_timestamp_index

             _UpperCAmelCase     :  Optional[Any]							      =    jnp.where(
                 A						,					scores.at[:, last_allowed + 1 :].set(-float("inf"       )       )						,					A						,					)

             # if sum of probability over timestamps is above any other token, sample timestamp
             _UpperCAmelCase     :  List[str]							      =    jax.nn.log_softmax(A						,					axis=-1       )

             def handle_cumulative_probs(A						:				str						,					A						:				Dict       ):
                    _UpperCAmelCase     :  Optional[Any]							      =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]						,					axis=-1       )
                    _UpperCAmelCase     :  int							      =    jnp.max(logprobs_k[: self.timestamp_begin]       )
                    return jnp.where(
                        timestamp_logprob > max_text_token_logprob						,					scores_k.at[: self.timestamp_begin].set(-float("inf"       )       )						,					A						,					)

             _UpperCAmelCase     :  str							      =    jax.vmap(A       )(A						,					A       )

             return scores


'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_047
__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_145

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						NllbTokenizer
      __UpperCamelCase:  Tuple						=						NllbTokenizerFast
      __UpperCamelCase:  Union[str, Any]						=						True
      __UpperCamelCase:  Dict						=						True
      __UpperCamelCase:  Optional[Any]						=						{}




      def      _A	(       self						:				Union[str, Any]       ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer(A						,					keep_accents=A       )
             tokenizer.save_pretrained(self.tmpdirname       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer(A						,					keep_accents=A       )

             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.tokenize("This is a test"       )
             self.assertListEqual(A						,					["▁This", "▁is", "▁a", "▁t", "est"]       )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(A       )						,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,					)

             _UpperCAmelCase     :  List[str]							      =    tokenizer.tokenize("I was born in 92000, and this is falsé."       )
             self.assertListEqual(
                 A						,					[
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "9",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "é",
                     ".",
                 ]						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.convert_tokens_to_ids(A       )
             self.assertListEqual(
                 A						,					[
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 ]						,					)

             _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer.convert_ids_to_tokens(A       )
             self.assertListEqual(
                 A						,					[
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "<unk>",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "<unk>",
                     ".",
                 ]						,					)




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Dict							      =    self.rust_tokenizer_class.from_pretrained(A						,					**A       )
                           _UpperCAmelCase     :  str							      =    self.tokenizer_class.from_pretrained(A						,					**A       )

                           _UpperCAmelCase     :  Optional[int]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.save_pretrained(A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it save with the same files + the tokenizer.json file for the fast one
                           self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )
                           _UpperCAmelCase     :  Optional[int]							      =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f       )
                           self.assertSequenceEqual(A						,					A       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  List[str]							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )

                           # Save tokenizer rust, legacy_format=True
                           _UpperCAmelCase     :  Optional[Any]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  str							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  str							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it save with the same files
                           self.assertSequenceEqual(A						,					A       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )

                           # Save tokenizer rust, legacy_format=False
                           _UpperCAmelCase     :  Optional[int]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it saved the tokenizer.json file
                           self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             if not self.test_seqaseq:
                    return

             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizers()
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
                           # Longer text that will definitely require truncation.
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               " UN Chief Says There Is No Military Solution in Syria",
                               " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                               " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                               " will only worsen the violence and misery for millions of people.",
                           ]
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               "Şeful ONU declară că nu există o soluţie militară în Siria",
                               "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                               " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                               " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                           ]
                           try:
                                  _UpperCAmelCase     :  Optional[int]							      =    tokenizer.prepare_seqaseq_batch(
                                      src_texts=A						,					tgt_texts=A						,					max_length=3						,					max_target_length=10						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"						,					)
                           except NotImplementedError:
                                  return
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					10       )
                           # max_target_length will default to max_length if not specified
                           _UpperCAmelCase     :  Tuple							      =    tokenizer.prepare_seqaseq_batch(
                               A						,					tgt_texts=A						,					max_length=3						,					return_tensors="pt"       )
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					3       )

                           _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer.prepare_seqaseq_batch(
                               src_texts=A						,					max_length=3						,					max_target_length=10						,					return_tensors="pt"       )
                           self.assertEqual(batch_encoder_only.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch_encoder_only.attention_mask.shape[1]						,					3       )
                           self.assertNotIn("decoder_input_ids"						,					A       )




      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."       )
      def      _A	(       self						:				List[Any]       ):
             pass





      def      _A	(       self						:				Union[str, Any]       ):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Any							      =    [AddedToken("<special>"						,					lstrip=A       )]

                           _UpperCAmelCase     :  List[Any]							      =    self.rust_tokenizer_class.from_pretrained(
                               A						,					additional_special_tokens=A						,					**A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.encode("Hey this is a <special> token"       )

                           _UpperCAmelCase     :  Any							      =    tokenizer_r.encode("<special>"						,					add_special_tokens=A       )[0]

                           self.assertTrue(special_token_id in r_output       )

                           if self.test_slow_tokenizer:
                                  _UpperCAmelCase     :  Dict							      =    self.rust_tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A						,					)
                                  _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A       )

                                  _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer_p.encode("Hey this is a <special> token"       )

                                  _UpperCAmelCase     :  Any							      =    tokenizer_cr.encode("Hey this is a <special> token"       )

                                  self.assertEqual(A						,					A       )
                                  self.assertEqual(A						,					A       )
                                  self.assertTrue(special_token_id in p_output       )
                                  self.assertTrue(special_token_id in cr_output       )

@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"facebook/nllb-200-distilled-600M"
      __UpperCamelCase:  Optional[int]						=						[
          " UN Chief Says There Is No Military Solution in Syria",
          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
      ]
      __UpperCamelCase:  str						=						[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      __UpperCamelCase:  str						=						[
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]




      @classmethod
      def      _A	(       cls						:				int       ):
             _UpperCAmelCase     :  NllbTokenizer							      =    NllbTokenizer.from_pretrained(
                 cls.checkpoint_name						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"       )
             _UpperCAmelCase     :  Union[str, Any]							      =    1
             return cls




      def      _A	(       self						:				Any       ):
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]						,					256001       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]						,					256002       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]						,					256057       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )




      def      _A	(       self						:				Tuple       ):
             self.assertIn(A						,					self.tokenizer.all_special_ids       )
             # fmt: off
             _UpperCAmelCase     :  List[Any]							      =    [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
             # fmt: on

             _UpperCAmelCase     :  Tuple							      =    self.tokenizer.decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer.decode(generated_ids[1:]						,					skip_special_tokens=A       )
             self.assertEqual(A						,					A       )
             self.assertNotIn(self.tokenizer.eos_token						,					A       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  List[Any]							      =    ["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]						,					A       )
             _UpperCAmelCase     :  Dict							      =    10
             _UpperCAmelCase     :  Tuple							      =    self.tokenizer(A						,					max_length=A						,					truncation=A       ).input_ids[0]
             self.assertEqual(ids[-1]						,					2       )
             self.assertEqual(ids[0]						,					A       )
             self.assertEqual(len(A       )						,					A       )




      def      _A	(       self						:				Dict       ):
             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )						,					[256203, 3]       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    tempfile.mkdtemp()
             _UpperCAmelCase     :  str							      =    self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(A       )
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer.from_pretrained(A       )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,					A       )




      @require_torch
      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    self.tokenizer(
                 self.src_text						,					text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=len(self.expected_src_tokens       )						,					return_tensors="pt"						,					)
             _UpperCAmelCase     :  Tuple							      =    shift_tokens_right(
                 batch["labels"]						,					self.tokenizer.pad_token_id						,					self.tokenizer.lang_code_to_id["ron_Latn"]       )

             self.assertIsInstance(A						,					A       )

             self.assertEqual((2, 15)						,					batch.input_ids.shape       )
             self.assertEqual((2, 15)						,					batch.attention_mask.shape       )
             _UpperCAmelCase     :  Dict							      =    batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )
             self.assertEqual(A						,					batch.decoder_input_ids[0, 0]       )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens						,					[EN_CODE]       )
             self.assertEqual(self.tokenizer.suffix_tokens						,					[self.tokenizer.eos_token_id]       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer(self.src_text						,					padding=A						,					truncation=A						,					max_length=3						,					return_tensors="pt"       )
             _UpperCAmelCase     :  Dict							      =    self.tokenizer(
                 text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=10						,					return_tensors="pt"       )
             _UpperCAmelCase     :  List[Any]							      =    targets["input_ids"]
             _UpperCAmelCase     :  Union[str, Any]							      =    shift_tokens_right(
                 A						,					self.tokenizer.pad_token_id						,					decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]						,					)

             self.assertEqual(batch.input_ids.shape[1]						,					3       )
             self.assertEqual(batch.decoder_input_ids.shape[1]						,					10       )




      @require_torch
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    self.tokenizer._build_translation_inputs(
                 "A test"						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     # A, test, EOS, en_XX
                     "input_ids": [[256047, 70, 7356, 2]],
                     "attention_mask": [[1, 1, 1, 1]],
                     # ar_AR
                     "forced_bos_token_id": 256057,
                 }						,					)





      @require_torch
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Dict							      =    True
             _UpperCAmelCase     :  Any							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]       )

             _UpperCAmelCase     :  Optional[int]							      =    False
             _UpperCAmelCase     :  str							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]       )

