


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						LEDTokenizer
 __snake_case				=						LEDTokenizerFast
 __snake_case				=						True



 def 		UpperCamelCase__  (			self      ):
       super().setUp()
       snake_case_ =   [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       snake_case_ =   dict(zip(_UpperCAmelCase	,   range(len(_UpperCAmelCase      )      )      )      )
       snake_case_ =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       snake_case_ =   {'''unk_token''': '''<unk>'''}

       snake_case_ =   os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file''']      )
       snake_case_ =   os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file''']      )
       with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8'''      ) as fp:
             fp.write(json.dumps(_UpperCAmelCase      ) + '''\n'''      )
       with open(self.merges_file	,   '''w'''	,   encoding='''utf-8'''      ) as fp:
             fp.write('''\n'''.join(_UpperCAmelCase      )      )



 def 		UpperCamelCase__  (			self	,   **_UpperCAmelCase      ):
       kwargs.update(self.special_tokens_map      )
       return self.tokenizer_class.from_pretrained(self.tmpdirname	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   **_UpperCAmelCase      ):
       kwargs.update(self.special_tokens_map      )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return "lower newer", "lower newer"



 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''      )



 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       snake_case_ =   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   tokenizer(_UpperCAmelCase	,   max_length=len(_UpperCAmelCase      )	,   padding=_UpperCAmelCase	,   return_tensors='''pt'''      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

             self.assertEqual((2, 9)	,   batch.input_ids.shape      )
             self.assertEqual((2, 9)	,   batch.attention_mask.shape      )
             snake_case_ =   batch.input_ids.tolist()[0]
             self.assertListEqual(_UpperCAmelCase	,   _UpperCAmelCase      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   tokenizer(_UpperCAmelCase	,   padding=_UpperCAmelCase	,   return_tensors='''pt'''      )
             self.assertIn('''input_ids'''	,   _UpperCAmelCase      )
             self.assertIn('''attention_mask'''	,   _UpperCAmelCase      )
             self.assertNotIn('''labels'''	,   _UpperCAmelCase      )
             self.assertNotIn('''decoder_attention_mask'''	,   _UpperCAmelCase      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   tokenizer(text_target=_UpperCAmelCase	,   max_length=32	,   padding='''max_length'''	,   return_tensors='''pt'''      )
             self.assertEqual(32	,   targets['''input_ids'''].shape[1]      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   tokenizer(
                 ['''I am a small frog''' * 10_24, '''I am a small frog''']	,   padding=_UpperCAmelCase	,   truncation=_UpperCAmelCase	,   return_tensors='''pt'''      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )
             self.assertEqual(batch.input_ids.shape	,   (2, 51_22)      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ['''A long paragraph for summarization.''']
       snake_case_ =   [
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   tokenizer(_UpperCAmelCase	,   return_tensors='''pt'''      )
             snake_case_ =   tokenizer(text_target=_UpperCAmelCase	,   return_tensors='''pt'''      )
             snake_case_ =   inputs['''input_ids''']
             snake_case_ =   targets['''input_ids''']
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             snake_case_ =   ['''Summary of the text.''', '''Another summary.''']
             snake_case_ =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             snake_case_ =   tokenizer(_UpperCAmelCase	,   padding=_UpperCAmelCase      )
             snake_case_ =   [[0] * len(_UpperCAmelCase      ) for x in encoded_output['''input_ids''']]
             snake_case_ =   tokenizer.pad(_UpperCAmelCase      )
             self.assertSequenceEqual(outputs['''global_attention_mask''']	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       pass





 def 		UpperCamelCase__  (			self      ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                   snake_case_ =   self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,   **_UpperCAmelCase      )
                   snake_case_ =   self.tokenizer_class.from_pretrained(_UpperCAmelCase	,   **_UpperCAmelCase      )
                   snake_case_ =   '''A, <mask> AllenNLP sentence.'''
                   snake_case_ =   tokenizer_r.encode_plus(_UpperCAmelCase	,   add_special_tokens=_UpperCAmelCase	,   return_token_type_ids=_UpperCAmelCase      )
                   snake_case_ =   tokenizer_p.encode_plus(_UpperCAmelCase	,   add_special_tokens=_UpperCAmelCase	,   return_token_type_ids=_UpperCAmelCase      )
                   self.assertEqual(sum(tokens_r['''token_type_ids''']      )	,   sum(tokens_p['''token_type_ids''']      )      )
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']      ) / len(tokens_r['''attention_mask''']      )	,   sum(tokens_p['''attention_mask''']      ) / len(tokens_p['''attention_mask''']      )	,   )

                   snake_case_ =   tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
                   snake_case_ =   tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )
                   self.assertSequenceEqual(tokens_p['''input_ids''']	,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
                   self.assertSequenceEqual(tokens_r['''input_ids''']	,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

                   self.assertSequenceEqual(
                       _UpperCAmelCase	,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
                   self.assertSequenceEqual(
                       _UpperCAmelCase	,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )



from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor