




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )







@require_flax
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self	,  _a	,  _a       )		->							str:
           _A							:  List[str]    =		jnp.ones((batch_size, length)       ) / length
           return scores

     def       a__			(						self       )		->							Optional[int]:
           _A							:  List[Any]    =		None
           _A							:  Union[str, Any]    =		20

           _A							:  List[str]    =		self._get_uniform_logits(batch_size=2	,  length=_a       )

           # tweak scores to not be uniform anymore
           _A							:  str    =		scores.at[1, 5].set((1 / length) + 0.1       )  # peak, 1st batch
           _A							:  str    =		scores.at[1, 10].set((1 / length) - 0.4       )  # valley, 1st batch

           # compute softmax
           _A							:  str    =		jax.nn.softmax(_a	,  axis=-1       )

           _A							:  Union[str, Any]    =		FlaxTemperatureLogitsWarper(temperature=0.5       )
           _A							:  Tuple    =		FlaxTemperatureLogitsWarper(temperature=1.3       )

           _A							:  Union[str, Any]    =		jax.nn.softmax(temp_dist_warper_sharper(_a	,  scores.copy()	,  cur_len=_a       )	,  axis=-1       )
           _A							:  Optional[int]    =		jax.nn.softmax(temp_dist_warper_smoother(_a	,  scores.copy()	,  cur_len=_a       )	,  axis=-1       )

           # uniform distribution stays uniform
           self.assertTrue(jnp.allclose(probs[0, :]	,  warped_prob_sharp[0, :]	,  atol=1e-3       )       )
           self.assertTrue(jnp.allclose(probs[0, :]	,  warped_prob_smooth[0, :]	,  atol=1e-3       )       )

           # sharp peaks get higher, valleys get lower
           self.assertLess(probs[1, :].max()	,  warped_prob_sharp[1, :].max()       )
           self.assertGreater(probs[1, :].min()	,  warped_prob_sharp[1, :].min()       )

           # smooth peaks get lower, valleys get higher
           self.assertGreater(probs[1, :].max()	,  warped_prob_smooth[1, :].max()       )
           self.assertLess(probs[1, :].min()	,  warped_prob_smooth[1, :].min()       )

     def       a__			(						self       )		->							Tuple:
           _A							:  int    =		None
           _A							:  List[Any]    =		10
           _A							:  int    =		2

           # create ramp distribution
           _A							:  Optional[int]    =		np.broadcast_to(np.arange(_a       )[None, :]	,  (batch_size, vocab_size)       ).copy()
           _A							:  List[Any]    =		ramp_logits[1:, : vocab_size // 2] + vocab_size

           _A							:  int    =		FlaxTopKLogitsWarper(3       )

           _A							:  Optional[int]    =		top_k_warp(_a	,  _a	,  cur_len=_a       )

           # check that correct tokens are filtered
           self.assertListEqual(jnp.isinf(scores[0]       ).tolist()	,  7 * [True] + 3 * [False]       )
           self.assertListEqual(jnp.isinf(scores[1]       ).tolist()	,  2 * [True] + 3 * [False] + 5 * [True]       )

           # check special case
           _A							:  Optional[Any]    =		5
           _A							:  Any    =		FlaxTopKLogitsWarper(top_k=1	,  filter_value=0.0	,  min_tokens_to_keep=3       )

           _A							:  Any    =		np.broadcast_to(np.arange(_a       )[None, :]	,  (batch_size, length)       ).copy()
           _A							:  Dict    =		top_k_warp_safety_check(_a	,  _a	,  cur_len=_a       )

           # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
           self.assertListEqual((scores == 0.0).sum(axis=-1       ).tolist()	,  [2, 2]       )

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Union[str, Any]    =		None
           _A							:  Optional[int]    =		10
           _A							:  Optional[Any]    =		2

           # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
           _A							:  Union[str, Any]    =		np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]       )       )

           _A							:  Any    =		FlaxTopPLogitsWarper(0.8       )
           _A							:  Union[str, Any]    =		np.exp(top_p_warp(_a	,  _a	,  cur_len=_a       )       )

           # dist should be filtered to keep min num values so that sum is >= top_p
           # exp (-inf) => 0
           _A							:  List[Any]    =		np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]       )
           self.assertTrue(np.allclose(_a	,  _a	,  atol=1e-3       )       )

           # check edge cases with negative and extreme logits
           _A							:  List[str]    =		np.broadcast_to(np.arange(_a       )[None, :]	,  (batch_size, vocab_size)       ).copy() - (
               vocab_size // 2
           )

           # make ramp_logits more extreme
           _A							:  List[Any]    =		ramp_logits[1] * 100.0

           # make sure at least 2 tokens are kept
           _A							:  Tuple    =		FlaxTopPLogitsWarper(0.9	,  min_tokens_to_keep=2	,  filter_value=0.0       )
           _A							:  int    =		top_p_warp(_a	,  _a	,  cur_len=_a       )

           # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
           self.assertListEqual((filtered_dist != 0.0).sum(axis=-1       ).tolist()	,  [3, 2]       )

     def       a__			(						self       )		->							int:
           _A							:  Tuple    =		20
           _A							:  str    =		4
           _A							:  Optional[Any]    =		0

           _A							:  str    =		FlaxMinLengthLogitsProcessor(min_length=10	,  eos_token_id=_a       )

           # check that min length is applied at length 5
           _A							:  Dict    =		ids_tensor((batch_size, 20)	,  vocab_size=20       )
           _A							:  Optional[Any]    =		5
           _A							:  Tuple    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  List[str]    =		min_dist_processor(_a	,  _a	,  cur_len=_a       )
           self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()	,  4 * [-float("""inf"""       )]       )

           # check that min length is not applied anymore at length 15
           _A							:  Optional[Any]    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  Tuple    =		15
           _A							:  Optional[int]    =		min_dist_processor(_a	,  _a	,  cur_len=_a       )
           self.assertFalse(jnp.isinf(_a       ).any()       )

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Optional[Any]    =		20
           _A							:  Any    =		4
           _A							:  str    =		0

           _A							:  Any    =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a       )

           # check that all scores are -inf except the bos_token_id score
           _A							:  Dict    =		ids_tensor((batch_size, 1)	,  vocab_size=20       )
           _A							:  Tuple    =		1
           _A							:  List[Any]    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  int    =		logits_processor(_a	,  _a	,  cur_len=_a       )
           self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]       ).all()       )
           self.assertListEqual(scores[:, bos_token_id].tolist()	,  4 * [0]       )  # score for bos_token_id shold be zero

           # check that bos_token_id is not forced if current length is greater than 1
           _A							:  Any    =		3
           _A							:  Dict    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  List[str]    =		logits_processor(_a	,  _a	,  cur_len=_a       )
           self.assertFalse(jnp.isinf(_a       ).any()       )

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Optional[int]    =		20
           _A							:  Optional[int]    =		4
           _A							:  Any    =		0
           _A							:  Dict    =		5

           _A							:  List[Any]    =		FlaxForcedEOSTokenLogitsProcessor(max_length=_a	,  eos_token_id=_a       )

           # check that all scores are -inf except the eos_token_id when max_length is reached
           _A							:  List[Any]    =		ids_tensor((batch_size, 4)	,  vocab_size=20       )
           _A							:  Optional[Any]    =		4
           _A							:  str    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  Any    =		logits_processor(_a	,  _a	,  cur_len=_a       )
           self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]       ).all()       )
           self.assertListEqual(scores[:, eos_token_id].tolist()	,  4 * [0]       )  # score for eos_token_id should be zero

           # check that eos_token_id is not forced if max_length is not reached
           _A							:  Any    =		3
           _A							:  int    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  List[str]    =		logits_processor(_a	,  _a	,  cur_len=_a       )
           self.assertFalse(jnp.isinf(_a       ).any()       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Dict    =		4
           _A							:  Tuple    =		10
           _A							:  int    =		15
           _A							:  int    =		2
           _A							:  Any    =		1
           _A							:  Tuple    =		15

           # dummy input_ids and scores
           _A							:  List[Any]    =		ids_tensor((batch_size, sequence_length)	,  _a       )
           _A							:  Optional[int]    =		input_ids.copy()

           _A							:  Union[str, Any]    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  Optional[int]    =		scores.copy()

           # instantiate all dist processors
           _A							:  Tuple    =		FlaxTemperatureLogitsWarper(temperature=0.5       )
           _A							:  List[str]    =		FlaxTopKLogitsWarper(3       )
           _A							:  Optional[int]    =		FlaxTopPLogitsWarper(0.8       )

           # instantiate all logits processors
           _A							:  Union[str, Any]    =		FlaxMinLengthLogitsProcessor(min_length=10	,  eos_token_id=_a       )
           _A							:  List[str]    =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a       )
           _A							:  List[str]    =		FlaxForcedEOSTokenLogitsProcessor(max_length=_a	,  eos_token_id=_a       )

           _A							:  Union[str, Any]    =		10

           # no processor list
           _A							:  Optional[int]    =		temp_dist_warp(_a	,  _a	,  cur_len=_a       )
           _A							:  Tuple    =		top_k_warp(_a	,  _a	,  cur_len=_a       )
           _A							:  List[str]    =		top_p_warp(_a	,  _a	,  cur_len=_a       )
           _A							:  str    =		min_dist_proc(_a	,  _a	,  cur_len=_a       )
           _A							:  Union[str, Any]    =		bos_dist_proc(_a	,  _a	,  cur_len=_a       )
           _A							:  Optional[int]    =		eos_dist_proc(_a	,  _a	,  cur_len=_a       )

           # with processor list
           _A							:  Tuple    =		FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
           _A							:  str    =		processor(_a	,  _a	,  cur_len=_a       )

           # scores should be equal
           self.assertTrue(jnp.allclose(_a	,  _a	,  atol=1e-3       )       )

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()	,  input_ids_comp.tolist()       )



     def       a__			(						self       )		->							str:
           _A							:  int    =		4
           _A							:  Tuple    =		10
           _A							:  Optional[Any]    =		15
           _A							:  str    =		2
           _A							:  str    =		1
           _A							:  Dict    =		15

           # dummy input_ids and scores
           _A							:  int    =		ids_tensor((batch_size, sequence_length)	,  _a       )
           _A							:  List[Any]    =		input_ids.copy()

           _A							:  int    =		self._get_uniform_logits(_a	,  _a       )
           _A							:  Optional[int]    =		scores.copy()

           # instantiate all dist processors
           _A							:  Any    =		FlaxTemperatureLogitsWarper(temperature=0.5       )
           _A							:  Any    =		FlaxTopKLogitsWarper(3       )
           _A							:  Any    =		FlaxTopPLogitsWarper(0.8       )

           # instantiate all logits processors
           _A							:  int    =		FlaxMinLengthLogitsProcessor(min_length=10	,  eos_token_id=_a       )
           _A							:  Union[str, Any]    =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a       )
           _A							:  Union[str, Any]    =		FlaxForcedEOSTokenLogitsProcessor(max_length=_a	,  eos_token_id=_a       )

           _A							:  List[str]    =		10

           # no processor list
           def run_no_processor_list(_a	,  _a	,  _a       ):
                 _A							:  List[str]    =		temp_dist_warp(_a	,  _a	,  cur_len=_a       )
                 _A							:  Any    =		top_k_warp(_a	,  _a	,  cur_len=_a       )
                 _A							:  str    =		top_p_warp(_a	,  _a	,  cur_len=_a       )
                 _A							:  Optional[Any]    =		min_dist_proc(_a	,  _a	,  cur_len=_a       )
                 _A							:  Optional[int]    =		bos_dist_proc(_a	,  _a	,  cur_len=_a       )
                 _A							:  List[str]    =		eos_dist_proc(_a	,  _a	,  cur_len=_a       )
                 return scores

           # with processor list
           def run_processor_list(_a	,  _a	,  _a       ):
                 _A							:  Optional[int]    =		FlaxLogitsProcessorList(
                     [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
                 _A							:  Optional[Any]    =		processor(_a	,  _a	,  cur_len=_a       )
                 return scores

           _A							:  int    =		jax.jit(_a       )
           _A							:  str    =		jax.jit(_a       )

           _A							:  Tuple    =		jitted_run_no_processor_list(_a	,  _a	,  _a       )
           _A							:  str    =		jitted_run_processor_list(_a	,  _a	,  _a       )

           # scores should be equal
           self.assertTrue(jnp.allclose(_a	,  _a	,  atol=1e-3       )       )

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()	,  input_ids_comp.tolist()       )







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case            = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  List[Any]    =		PegasusTokenizer(_a       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							int:
           return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""</s>"""
           _A							:  Dict    =		1

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

     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]	,  """<pad>"""       )
           self.assertEqual(vocab_keys[1]	,  """</s>"""       )
           self.assertEqual(vocab_keys[-1]	,  """v"""       )
           self.assertEqual(len(_a       )	,  1103       )

     def       a__			(						self       )		->							Optional[int]:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1103       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Any    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Optional[int]    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  int    =		(
               """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
               """ </s> <pad> <pad> <pad>"""
           )
           _A							:  Optional[int]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  List[Any]    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           _A							:  Optional[int]    =		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
           _A							:  Union[str, Any]    =		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Union[str, Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 9_6103
           assert tokenizer.pad_token_id == 0
           assert tokenizer.eos_token_id == 1
           assert tokenizer.offset == 103
           assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
           assert tokenizer.unk_token == "<unk>"
           assert tokenizer.model_max_length == 1024
           _A							:  Any    =		"""To ensure a smooth flow of bank resolutions."""
           _A							:  Optional[int]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Optional[Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Union[str, Any]    =		["""This is going to be way too long.""" * 150, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Union[str, Any]    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  Tuple    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 1024)
           assert batch.attention_mask.shape == (2, 1024)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[Any]    =		{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], """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, 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, 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="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  Tuple    =		PegasusTokenizer(_a	,  offset=0	,  mask_token_sent=_a	,  mask_token="""[MASK]"""       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Optional[Any]:
           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[str]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							List[Any]:
           _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		(
               """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
               """ <pad> <pad> <pad>"""
           )
           _A							:  Optional[Any]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  int    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     @require_torch
     def       a__			(						self       )		->							Optional[int]:
           _A							:  Tuple    =		["""This is going to be way too long.""" * 1000, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Tuple    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  str    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 4096)
           assert batch.attention_mask.shape == (2, 4096)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           _A							:  Any    =		self._large_tokenizer(_a       ).input_ids

           self.assertListEqual(
               _a	,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]	,  )

