

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase										=		logging.get_logger(__name__)





def 	lowercase      (   __UpperCamelCase						) ->      List[int]:
 if isinstance(__UpperCamelCase      ,   np.ndarray						):
  return list(tensor.shape						)

 __magic_name__     =      tf.shape(__UpperCamelCase						)

 if tensor.shape == tf.TensorShape(__UpperCamelCase						):
  return dynamic

 __magic_name__     =      tensor.shape.as_list()

 return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase						)]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase = None      ,   __UpperCamelCase = None						) ->      tf.Tensor:
 return tf.nn.softmax(logits=logits + 1e-9      ,   axis=__UpperCamelCase      ,   name=__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=1e-5      ,   __UpperCamelCase=-1						) ->      List[str]:
 # This is a very simplified functional layernorm, designed to duplicate
 # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
 # models in Transformers.

 if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase      ,   __UpperCamelCase						):
  raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''						)

 # Get mean and variance on the axis to be normalized
 __magic_name__   ,						__magic_name__     =      tf.nn.moments(__UpperCamelCase      ,   axes=[axis]      ,   keepdims=__UpperCamelCase						)

 if axis != -1:
  # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
  # on every dimension except axis
  __magic_name__     =      [1] * inputs.shape.rank
  __magic_name__     =      shape_list(__UpperCamelCase						)[axis]
  __magic_name__     =      tf.reshape(__UpperCamelCase      ,   __UpperCamelCase						)
  __magic_name__     =      tf.reshape(__UpperCamelCase      ,   __UpperCamelCase						)

 # Compute layer normalization using the batch_normalization
 # function.
 __magic_name__     =      tf.nn.batch_normalization(
     __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   offset=__UpperCamelCase      ,   scale=__UpperCamelCase      ,   variance_epsilon=__UpperCamelCase      ,   )
 return outputs





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=0      ,   __UpperCamelCase=-1						) ->      List[str]:
 # Replicates the behavior of torch.flatten in TF

 # If end_dim or start_dim is negative, count them from the end
 if end_dim < 0:
  end_dim += input.shape.rank
 if start_dim < 0:
  start_dim += input.shape.rank

 if start_dim == end_dim:
  return input

 __magic_name__     =      tf.shape(__UpperCamelCase						)
 __magic_name__     =      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
 __magic_name__     =      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,   axis=0						)
 return tf.reshape(__UpperCamelCase      ,   __UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      tf.Tensor:
 if not isinstance(__UpperCamelCase      ,   tf.Tensor						):
  __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)  # Catches stray NumPy inputs
 if encoder_attention_mask.shape.rank == 3:
  __magic_name__     =      encoder_attention_mask[:, None, :, :]
 if encoder_attention_mask.shape.rank == 2:
  __magic_name__     =      encoder_attention_mask[:, None, None, :]
 # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
 # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
 # /transformer/transformer_layers.py#L270
 # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
 # encoder_extended_attention_mask.transpose(-1, -2))
 __magic_name__     =      (
     tf.cast(1      ,   encoder_attention_mask.dtype						) - encoder_extended_attention_mask
 ) * encoder_extended_attention_mask.dtype.min

 return encoder_extended_attention_mask





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase = "input_ids"						) ->      None:
 tf.debugging.assert_less(
     __UpperCamelCase      ,   tf.cast(__UpperCamelCase      ,   dtype=tensor.dtype						)      ,   message=(
         f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase						)}) must be smaller than the embedding '''
         f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
     )      ,   )





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[Any]:
 __magic_name__     =      64512
 # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
 # because in that case even chunking the array would not make the saving
 # possible.
 __magic_name__     =      [x for x in data if len(__UpperCamelCase						) > HDF5_OBJECT_HEADER_LIMIT]

 # Expecting this to never be true.
 if bad_attributes:
  raise RuntimeError(
      '''The following attributes cannot be saved to HDF5 file because '''
      f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
      f'''bytes: {bad_attributes}'''						)

 __magic_name__     =      np.asarray(__UpperCamelCase						)

 __magic_name__     =      1
 __magic_name__     =      np.array_split(__UpperCamelCase      ,   __UpperCamelCase						)

 # This will never loop forever thanks to the test above.
 while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data						):
  num_chunks += 1
  __magic_name__     =      np.array_split(__UpperCamelCase      ,   __UpperCamelCase						)

 if num_chunks > 1:
  for chunk_id, chunk_data in enumerate(__UpperCamelCase						):
   __magic_name__     =      chunk_data
 else:
  __magic_name__     =      data





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 if name in group.attrs:
  __magic_name__     =      [n.decode('''utf8'''						) if hasattr(__UpperCamelCase      ,   '''decode'''						) else n for n in group.attrs[name]]
 else:
  __magic_name__     =      []
  __magic_name__     =      0
  while "%s%d" % (name, chunk_id) in group.attrs:
   data.extend(
       [n.decode('''utf8'''						) if hasattr(__UpperCamelCase      ,   '''decode'''						) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]						)
   chunk_id += 1
 return data





def 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:

 def _expand_single_ad_tensor(__UpperCamelCase						):
  if isinstance(__UpperCamelCase      ,   tf.Tensor						) and t.shape.rank == 1:
   return tf.expand_dims(__UpperCamelCase      ,   axis=-1						)
  return t

 return tf.nest.map_structure(_expand_single_ad_tensor      ,   __UpperCamelCase						)




"""simple docstring"""






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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


__lowerCamelCase										=		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       ReformerTokenizer
     _lowerCamelCase     =       ReformerTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       False
     _lowerCamelCase     =       True







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

      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )
      tokenizer.save_pretrained(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''<s>'''
      __magic_name__     =      1

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      list(self.get_tokenizer().get_vocab().keys()       )

      self.assertEqual(vocab_keys[0] ,						'''<unk>'''       )
      self.assertEqual(vocab_keys[1] ,						'''<s>'''       )
      self.assertEqual(vocab_keys[-1] ,						'''j'''       )
      self.assertEqual(len(UpperCamelCase_       ) ,						1000       )







     def 	lowerCAmelCase__    (      self       ):
      self.assertEqual(self.get_tokenizer().vocab_size ,						1000       )







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

      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_rust_tokenizer()

      __magic_name__     =      '''I was born in 92000, and this is falsé.'''

      __magic_name__     =      tokenizer.tokenize(UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.tokenize(UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      tokenizer.encode(UpperCamelCase_ ,						add_special_tokens=UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.encode(UpperCamelCase_ ,						add_special_tokens=UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      self.get_rust_tokenizer()
      __magic_name__     =      tokenizer.encode(UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.encode(UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=15       ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
        __magic_name__     =      self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ ,						**UpperCamelCase_       )

        # Simple input
        __magic_name__     =      '''This is a simple input'''
        __magic_name__     =      ['''This is a simple input 1''', '''This is a simple input 2''']
        __magic_name__     =      ('''This is a simple input''', '''This is a pair''')
        __magic_name__     =      [
            ('''This is a simple input 1''', '''This is a simple input 2'''),
            ('''This is a simple pair 1''', '''This is a simple pair 2'''),
        ]

        # Simple input tests
        self.assertRaises(UpperCamelCase_ ,						tokenizer_r.encode ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

        # Simple input
        self.assertRaises(UpperCamelCase_ ,						tokenizer_r.encode_plus ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

        # Simple input
        self.assertRaises(
            UpperCamelCase_ ,						tokenizer_r.batch_encode_plus ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length''' ,						)

        # Pair input
        self.assertRaises(UpperCamelCase_ ,						tokenizer_r.encode ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

        # Pair input
        self.assertRaises(UpperCamelCase_ ,						tokenizer_r.encode_plus ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

        # Pair input
        self.assertRaises(
            UpperCamelCase_ ,						tokenizer_r.batch_encode_plus ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length''' ,						)







     def 	lowerCAmelCase__    (      self       ):
      pass







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )

      __magic_name__     =      tokenizer.tokenize('''This is a test'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[285, 46, 10, 170, 382] ,						)

      __magic_name__     =      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
      self.assertListEqual(
          UpperCamelCase_ ,						[
              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''',
              '''.''',
          ] ,						)
      __magic_name__     =      tokenizer.convert_tokens_to_ids(UpperCamelCase_       )
      self.assertListEqual(
          UpperCamelCase_ ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,						)

      __magic_name__     =      tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
      self.assertListEqual(
          UpperCamelCase_ ,						[
              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>''',
              '''.''',
          ] ,						)







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''Hello World!'''
      __magic_name__     =      [126, 32, 262, 152, 38, 72, 287]

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







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      (
          '''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'''
      )
      __magic_name__     =      [
          108,
          265,
          24,
          111,
          4,
          258,
          156,
          35,
          28,
          275,
          3,
          259,
          297,
          260,
          84,
          4,
          35,
          110,
          44,
          8,
          259,
          91,
          268,
          21,
          11,
          209,
          274,
          109,
          266,
          277,
          117,
          86,
          93,
          315,
          258,
          278,
          258,
          277,
          258,
          0,
          258,
          288,
          258,
          319,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          287,
          258,
          315,
          258,
          289,
          258,
          278,
          99,
          269,
          266,
          262,
          8,
          259,
          241,
          4,
          217,
          230,
          268,
          266,
          55,
          168,
          106,
          75,
          193,
          266,
          223,
          27,
          49,
          26,
          282,
          25,
          264,
          299,
          19,
          26,
          0,
          258,
          277,
          117,
          86,
          93,
          176,
          183,
          270,
          11,
          262,
          42,
          61,
          265,
      ]

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







     @require_torch
     @slow
     def 	lowerCAmelCase__    (      self       ):
      import torch

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      __magic_name__     =      list(self.big_tokenizer.get_vocab().keys()       )[:10]
      __magic_name__     =      ''' '''.join(UpperCamelCase_       )
      __magic_name__     =      self.big_tokenizer.encode_plus(UpperCamelCase_ ,						return_tensors='''pt'''       )
      __magic_name__     =      self.big_tokenizer.batch_encode_plus([sequence, sequence] ,						return_tensors='''pt'''       )

      __magic_name__     =      ReformerConfig()
      # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
      __magic_name__     =      encoded_sequence['''input_ids'''].shape
      __magic_name__     =      ReformerModel(UpperCamelCase_       )

      # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
       model(**UpperCamelCase_       )
       model(**UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      # fmt: off
      __magic_name__     =      {'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''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]]}  # noqa: E501
      # fmt: on

      # This tokenizer does not know some characters like ")".
      # That is the reason why we use very simple texts here.
      # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
      __magic_name__     =      [
          '''This is a very simple sentence.''',
          '''The quick brown fox jumps over the lazy dog.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=UpperCamelCase_ ,						model_name='''google/reformer-crime-and-punishment''' ,						revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' ,						padding=UpperCamelCase_ ,						sequences=UpperCamelCase_ ,						)

