


"""simple docstring"""


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A	:  Optional[Any]            =     get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =XLMRobertaTokenizer
							__UpperCAmelCase		:       str							 =XLMRobertaTokenizerFast
							__UpperCAmelCase		:       Dict							 =True
							__UpperCAmelCase		:       Union[str, Any]							 =True

							def      snake_case							(	self      ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCAmelCase							       =     XLMRobertaTokenizer(__a       ,  keep_accents=__a      )
										tokenizer.save_pretrained(self.tmpdirname      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "<pad>"
										__lowerCAmelCase							       =     1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a      )       ,  __a      )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a      )       ,  __a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     list(self.get_tokenizer().get_vocab().keys()      )

										self.assertEqual(vocab_keys[0]       ,  "<s>"      )
										self.assertEqual(vocab_keys[1]       ,  "<pad>"      )
										self.assertEqual(vocab_keys[-1]       ,  "<mask>"      )
										self.assertEqual(len(__a      )       ,  10_02      )

							def      snake_case							(	self      ):
										self.assertEqual(self.get_tokenizer().vocab_size       ,  10_02      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     XLMRobertaTokenizer(__a       ,  keep_accents=__a      )

										__lowerCAmelCase							       =     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 [2_85, 46, 10, 1_70, 3_82]]       ,  )

										__lowerCAmelCase							       =     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",
										        ".",
										    ]       ,  )
										__lowerCAmelCase							       =     tokenizer.convert_tokens_to_ids(__a      )
										self.assertListEqual(
										    __a       ,  [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]       ,  )

										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

										__lowerCAmelCase							       =     (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
																__lowerCAmelCase							       =     self.rust_tokenizer_class.from_pretrained(__a       ,  **__a      )
																__lowerCAmelCase							       =     self.tokenizer_class.from_pretrained(__a       ,  **__a      )

																__lowerCAmelCase							       =     tempfile.mkdtemp()

																__lowerCAmelCase							       =     tokenizer_r.save_pretrained(__a      )
																__lowerCAmelCase							       =     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      )      )
																__lowerCAmelCase							       =     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
																__lowerCAmelCase							       =     tokenizer_r.from_pretrained(__a      )
																__lowerCAmelCase							       =     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      )      )
																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(__a      )

																# Save tokenizer rust, legacy_format=True
																__lowerCAmelCase							       =     tempfile.mkdtemp()

																__lowerCAmelCase							       =     tokenizer_r.save_pretrained(__a       ,  legacy_format=__a      )
																__lowerCAmelCase							       =     tokenizer_p.save_pretrained(__a      )

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

																# Checks everything loads correctly in the same way
																__lowerCAmelCase							       =     tokenizer_r.from_pretrained(__a      )
																__lowerCAmelCase							       =     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
																__lowerCAmelCase							       =     tempfile.mkdtemp()

																__lowerCAmelCase							       =     tokenizer_r.save_pretrained(__a       ,  legacy_format=__a      )
																__lowerCAmelCase							       =     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
																__lowerCAmelCase							       =     tokenizer_r.from_pretrained(__a      )
																__lowerCAmelCase							       =     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      )

							@cached_property
							def      snake_case							(	self      ):
										return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"      )

							def      snake_case							(	self      ):
										with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(__a       ,  f.name      )
													__lowerCAmelCase							       =     XLMRobertaTokenizer(f.name       ,  keep_accents=__a      )
													__lowerCAmelCase							       =     pickle.dumps(__a      )
										pickle.loads(__a      )

							def      snake_case							(	self      ):
										if not self.test_rust_tokenizer:
													return

										__lowerCAmelCase							       =     self.get_tokenizer()
										__lowerCAmelCase							       =     self.get_rust_tokenizer()

										__lowerCAmelCase							       =     "I was born in 92000, and this is falsé."

										__lowerCAmelCase							       =     tokenizer.tokenize(__a      )
										__lowerCAmelCase							       =     rust_tokenizer.tokenize(__a      )
										self.assertListEqual(__a       ,  __a      )

										__lowerCAmelCase							       =     tokenizer.encode(__a       ,  add_special_tokens=__a      )
										__lowerCAmelCase							       =     rust_tokenizer.encode(__a       ,  add_special_tokens=__a      )
										self.assertListEqual(__a       ,  __a      )

										__lowerCAmelCase							       =     self.get_rust_tokenizer()
										__lowerCAmelCase							       =     tokenizer.encode(__a      )
										__lowerCAmelCase							       =     rust_tokenizer.encode(__a      )
										self.assertListEqual(__a       ,  __a      )

							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "Hello World!"
										__lowerCAmelCase							       =     [0, 3_53_78, 66_61, 38, 2]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(__a       ,  self.big_tokenizer.encode(__a      )      )

							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     (
										    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
										    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
										)
										__lowerCAmelCase							       =     [
										    0,
										    32_93,
										    83,
										    10,
										    45_52,
										    49_89,
										    79_86,
										    6_78,
										    10,
										    59_15,
										    1_11,
										    17_94_59,
										    12_48_50,
										    4,
										    60_44,
										    2_37,
										    12,
										    6,
										    5,
										    6,
										    4,
										    67_80,
										    7_05,
										    15,
										    13_88,
										    44,
										    3_78,
										    1_01_14,
										    7_11,
										    1_52,
										    20,
										    6,
										    5,
										    2_23_76,
										    6_42,
										    12_21,
										    1_51_90,
										    3_41_53,
										    4_50,
										    56_08,
										    9_59,
										    11_19,
										    5_77_02,
										    1_36,
										    1_86,
										    47,
										    10_98,
										    2_93_67,
										    47,
										    # 4426, # What fairseq tokenizes from "<unk>": "_<"
										    # 3678, # What fairseq tokenizes from "<unk>": "unk"
										    # 2740, # What fairseq tokenizes from "<unk>": ">"
										    3,  # What we tokenize from "<unk>": "<unk>"
										    6,  # Residue from the tokenization: an extra sentencepiece underline
										    4,
										    60_44,
										    2_37,
										    62_84,
										    5_09_01,
										    5_28,
										    31,
										    90,
										    34,
										    9_27,
										    2,
										]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(__a       ,  self.big_tokenizer.encode(__a      )      )



							@slow
							def      snake_case							(	self      ):
										# fmt: off
										__lowerCAmelCase							       =     {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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, 1]], "attention_mask": [[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, 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, 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, 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, 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, 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]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=__a       ,  model_name="xlm-roberta-base"       ,  revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"       ,  )








"""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


A	:  Dict            =     get_logger(__name__)


A	:  Dict            =     R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     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."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or not (temperature > 0):
													raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"      )

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 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}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										__lowerCAmelCase							       =     jnp.roll(__a       ,  1      )
										score_mask |= score_mask.at[:, 0].set(__a      )

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 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}"      )

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										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}"      )

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



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

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     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.
										__lowerCAmelCase							       =     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:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     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"      ):
													__lowerCAmelCase							       =     generate_config.max_initial_timestamp_index
										else:
													__lowerCAmelCase							       =     model_config.vocab_size
										if self.max_initial_timestamp_index is None:
													__lowerCAmelCase							       =     model_config.vocab_size



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     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       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     jax.nn.log_softmax(__a       ,  axis=-1      )

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     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       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




