


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 __lowerCAmelCase									=				'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import MarianTokenizer
 from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case=None     ,				snake_case=None     ,				snake_case=None     ,				snake_case=None     ,				snake_case=None     ,				snake_case=None     ,				)    ->							Union[str, Any]:
       if attention_mask is None:
              lowercase__:  Optional[Any]         =  np.where(input_ids != config.pad_token_id     ,				1     ,				0	)
       if decoder_attention_mask is None:
              lowercase__:  Tuple         =  np.where(decoder_input_ids != config.pad_token_id     ,				1     ,				0	)
       if head_mask is None:
              lowercase__:  int         =  np.ones((config.encoder_layers, config.encoder_attention_heads)	)
       if decoder_head_mask is None:
              lowercase__:  Dict         =  np.ones((config.decoder_layers, config.decoder_attention_heads)	)
       if cross_attn_head_mask is None:
              lowercase__:  Dict         =  np.ones((config.decoder_layers, config.decoder_attention_heads)	)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }





class     __a     :
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=13							,					lowerCAmelCase__=7							,					lowerCAmelCase__=True							,					lowerCAmelCase__=False							,					lowerCAmelCase__=99							,					lowerCAmelCase__=16							,					lowerCAmelCase__=2							,					lowerCAmelCase__=4							,					lowerCAmelCase__=4							,					lowerCAmelCase__="gelu"							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=32							,					lowerCAmelCase__=2							,					lowerCAmelCase__=1							,					lowerCAmelCase__=0							,					lowerCAmelCase__=0.0_2							,					)   ->							Any:




        '''simple docstring'''
        lowercase__:  List[str]         =  parent
        lowercase__:  Optional[int]         =  batch_size
        lowercase__:  Any         =  seq_length
        lowercase__:  Dict         =  is_training
        lowercase__:  Union[str, Any]         =  use_labels
        lowercase__:  List[str]         =  vocab_size
        lowercase__:  Tuple         =  hidden_size
        lowercase__:  Union[str, Any]         =  num_hidden_layers
        lowercase__:  Dict         =  num_attention_heads
        lowercase__:  Optional[int]         =  intermediate_size
        lowercase__:  Union[str, Any]         =  hidden_act
        lowercase__:  List[str]         =  hidden_dropout_prob
        lowercase__:  List[Any]         =  attention_probs_dropout_prob
        lowercase__:  Union[str, Any]         =  max_position_embeddings
        lowercase__:  str         =  eos_token_id
        lowercase__:  int         =  pad_token_id
        lowercase__:  Union[str, Any]         =  bos_token_id
        lowercase__:  Union[str, Any]         =  initializer_range
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  Tuple         =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size					)							,					3							,					self.vocab_size					)
        lowercase__:  str         =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,					dtype=np.intaa					))							,					-1					)

        lowercase__:  List[str]         =  shift_tokens_right(lowerCAmelCase__							,					1							,					2					)

        lowercase__:  Optional[Any]         =  MarianConfig(
            vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					encoder_layers=self.num_hidden_layers							,					decoder_layers=self.num_hidden_layers							,					encoder_attention_heads=self.num_attention_heads							,					decoder_attention_heads=self.num_attention_heads							,					encoder_ffn_dim=self.intermediate_size							,					decoder_ffn_dim=self.intermediate_size							,					dropout=self.hidden_dropout_prob							,					attention_dropout=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					eos_token_id=self.eos_token_id							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					initializer_range=self.initializer_range							,					use_cache=lowerCAmelCase__							,					)
        lowercase__:  Dict         =  prepare_marian_inputs_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        return config, inputs_dict
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Union[str, Any]         =  self.prepare_config_and_inputs()
        return config, inputs_dict
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  20
        lowercase__:  Dict         =  model_class_name(lowerCAmelCase__					)

        lowercase__:  Any         =  model.encode(inputs_dict['input_ids']					)

        lowercase__     ,		lowercase__:  List[Any]         =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        lowercase__:  Optional[Any]         =  model.init_cache(decoder_input_ids.shape[0]							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  int         =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,					dtype='i4'					)

        lowercase__:  Tuple         =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,					)
        lowercase__:  Union[str, Any]         =  model.decode(
            decoder_input_ids[:, :-1]							,					lowerCAmelCase__							,					decoder_attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					decoder_position_ids=lowerCAmelCase__							,					)

        lowercase__:  Optional[int]         =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,					dtype='i4'					)
        lowercase__:  List[Any]         =  model.decode(
            decoder_input_ids[:, -1:]							,					lowerCAmelCase__							,					decoder_attention_mask=lowerCAmelCase__							,					past_key_values=outputs_cache.past_key_values							,					decoder_position_ids=lowerCAmelCase__							,					)

        lowercase__:  Optional[int]         =  model.decode(lowerCAmelCase__							,					lowerCAmelCase__					)

        lowercase__:  str         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
        self.parent.assertTrue(diff < 1E-3							,					msg=F'Max diff is {diff}'					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							str:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  20
        lowercase__:  Any         =  model_class_name(lowerCAmelCase__					)

        lowercase__:  Any         =  model.encode(inputs_dict['input_ids']					)

        lowercase__     ,		lowercase__:  List[Any]         =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        lowercase__:  List[str]         =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
            ]							,					axis=-1							,					)

        lowercase__:  Optional[Any]         =  model.init_cache(decoder_input_ids.shape[0]							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  int         =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,					)

        lowercase__:  Union[str, Any]         =  model.decode(
            decoder_input_ids[:, :-1]							,					lowerCAmelCase__							,					decoder_attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					decoder_position_ids=lowerCAmelCase__							,					)
        lowercase__:  Optional[Any]         =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,					dtype='i4'					)
        lowercase__:  List[Any]         =  model.decode(
            decoder_input_ids[:, -1:]							,					lowerCAmelCase__							,					past_key_values=outputs_cache.past_key_values							,					decoder_attention_mask=lowerCAmelCase__							,					decoder_position_ids=lowerCAmelCase__							,					)

        lowercase__:  List[Any]         =  model.decode(lowerCAmelCase__							,					lowerCAmelCase__							,					decoder_attention_mask=lowerCAmelCase__					)

        lowercase__:  Tuple         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
        self.parent.assertTrue(diff < 1E-3							,					msg=F'Max diff is {diff}'					)





@require_flax
class     __a     (       __UpperCamelCase			,			unittest.TestCase			,			__UpperCamelCase ):
 __lowercase					:				List[Any]  	=					True
 __lowercase					:				List[Any]  	=					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 __lowercase					:				Dict  	=					(FlaxMarianMTModel,) if is_flax_available() else ()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  List[Any]         =  FlaxMarianModelTester(self					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Union[str, Any]         =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  List[str]         =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Any         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__					):
                      lowercase__:  Union[str, Any]         =  self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__					)
                      lowercase__:  int         =  model_class(lowerCAmelCase__					)

                      @jax.jit
                      def encode_jitted(lowerCAmelCase__							,					lowerCAmelCase__=None							,					**lowerCAmelCase__					):
                             return model.encode(input_ids=lowerCAmelCase__							,					attention_mask=lowerCAmelCase__					)

                      with self.subTest('JIT Enabled'					):
                             lowercase__:  Optional[int]         =  encode_jitted(**lowerCAmelCase__					).to_tuple()

                      with self.subTest('JIT Disabled'					):
                             with jax.disable_jit():
                                    lowercase__:  str         =  encode_jitted(**lowerCAmelCase__					).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__					)							,					len(lowerCAmelCase__					)					)
                      for jitted_output, output in zip(lowerCAmelCase__							,					lowerCAmelCase__					):
                             self.assertEqual(jitted_output.shape							,					output.shape					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  List[str]         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__					):
                      lowercase__:  str         =  model_class(lowerCAmelCase__					)
                      lowercase__:  List[Any]         =  model.encode(inputs_dict['input_ids']							,					inputs_dict['attention_mask']					)

                      lowercase__:  List[str]         =  {
                          'decoder_input_ids': inputs_dict['decoder_input_ids'],
                          'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                          'encoder_outputs': encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					):
                             return model.decode(
                                 decoder_input_ids=lowerCAmelCase__							,					decoder_attention_mask=lowerCAmelCase__							,					encoder_outputs=lowerCAmelCase__							,					)

                      with self.subTest('JIT Enabled'					):
                             lowercase__:  Any         =  decode_jitted(**lowerCAmelCase__					).to_tuple()

                      with self.subTest('JIT Disabled'					):
                             with jax.disable_jit():
                                    lowercase__:  List[str]         =  decode_jitted(**lowerCAmelCase__					).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__					)							,					len(lowerCAmelCase__					)					)
                      for jitted_output, output in zip(lowerCAmelCase__							,					lowerCAmelCase__					):
                             self.assertEqual(jitted_output.shape							,					output.shape					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        for model_class_name in self.all_model_classes:
               lowercase__:  Optional[Any]         =  model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'					)
               # FlaxMarianForSequenceClassification expects eos token in input_ids
               lowercase__:  int         =  np.ones((1, 1)					) * model.config.eos_token_id
               lowercase__:  Optional[int]         =  model(lowerCAmelCase__					)
               self.assertIsNotNone(lowerCAmelCase__					)





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       unittest.TestCase ):
 __lowercase					:				Optional[Any]  	=					None
 __lowercase					:				Optional[Any]  	=					None
 @classmethod
 def 						SCREAMING_SNAKE_CASE__  (  cls					)   ->							None:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
        return cls
 @cached_property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        return MarianTokenizer.from_pretrained(self.model_name					)
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        return self.tokenizer.eos_token_id
 @cached_property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  FlaxMarianMTModel         =  FlaxMarianMTModel.from_pretrained(self.model_name					)
        self.assertEqual(model.config.decoder_start_token_id							,					model.config.pad_token_id					)
        return model
 def 						SCREAMING_SNAKE_CASE__  (  self							,					**lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  str         =  self.translate_src_text(**lowerCAmelCase__					)
        self.assertListEqual(self.expected_text							,					lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					**lowerCAmelCase__					)   ->							int:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  self.tokenizer(self.src_text							,					padding=lowerCAmelCase__							,					return_tensors='np'							,					**lowerCAmelCase__					)
        lowercase__:  List[Any]         =  self.model.generate(
            model_inputs.input_ids							,					attention_mask=model_inputs.attention_mask							,					num_beams=2							,					max_length=128							,					).sequences
        lowercase__:  Union[str, Any]         =  self.tokenizer.batch_decode(lowerCAmelCase__							,					skip_special_tokens=lowerCAmelCase__					)
        return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				Optional[int]  	=					'en'
 __lowercase					:				Tuple  	=					'fr'
 __lowercase					:				str  	=					[
     'I am a small frog.',
     'Now I can forget the 100 words of german that I know.',
 ]
 __lowercase					:				Tuple  	=					[
     'Je suis une petite grenouille.',
     'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
 ]
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				List[str]  	=					'fr'
 __lowercase					:				List[Any]  	=					'en'
 __lowercase					:				Tuple  	=					[
     'Donnez moi le micro.',
     'Tom et Mary étaient assis à une table.',  # Accents
 ]
 __lowercase					:				Optional[Any]  	=					[
     'Give me the microphone.',
     'Tom and Mary were sitting at a table.',
 ]
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				Any  	=					'mt'
 __lowercase					:				str  	=					'en'
 __lowercase					:				Optional[Any]  	=					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
 __lowercase					:				Optional[int]  	=					['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				Dict  	=					'en'
 __lowercase					:				List[Any]  	=					'de'
 __lowercase					:				Dict  	=					[
     'I am a small frog.',
     'Now I can forget the 100 words of german that I know.',
     'Tom asked his teacher for advice.',
     'That\'s how I would do it.',
     'Tom really admired Mary\'s courage.',
     'Turn around and close your eyes.',
 ]
 __lowercase					:				str  	=					[
     'Ich bin ein kleiner Frosch.',
     'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
     'Tom bat seinen Lehrer um Rat.',
     'So würde ich das machen.',
     'Tom bewunderte Marias Mut wirklich.',
     'Drehen Sie sich um und schließen Sie die Augen.',
 ]
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				List[str]  	=					'en'
 __lowercase					:				int  	=					'zh'
 __lowercase					:				Tuple  	=					['My name is Wolfgang and I live in Berlin']
 __lowercase					:				Tuple  	=					['我叫沃尔夫冈 我住在柏林']
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				List[str]  	=					'ru'
 __lowercase					:				int  	=					'fr'
 __lowercase					:				Optional[Any]  	=					['Он показал мне рукопись своей новой пьесы.']
 __lowercase					:				Any  	=					['Il m\'a montré le manuscrit de sa nouvelle pièce.']
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class     __a     (       __UpperCamelCase ):
 __lowercase					:				Optional[Any]  	=					'en'
 __lowercase					:				Optional[int]  	=					'ROMANCE'
 __lowercase					:				str  	=					[
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 __lowercase					:				Tuple  	=					[
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        self._assert_generated_batch_equal_expected()






import unittest

from cer import CER


__lowerCAmelCase									=				CER()





class     __a     (       unittest.TestCase ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['White House']
        lowercase__:  Optional[int]         =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  Union[str, Any]         =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        lowercase__:  Tuple         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1					) < 1E-6					)

        lowercase__:  Dict         =  ['werewolf']
        lowercase__:  Optional[Any]         =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.2_5					) < 1E-6					)

        # consecutive whitespaces case 1
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  int         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)

        # consecutive whitespaces case 2
        lowercase__:  List[str]         =  ['were   wolf']
        lowercase__:  Tuple         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  ['werewolf']
        lowercase__:  Dict         =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf']
        lowercase__:  Optional[int]         =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['werewolf']
        lowercase__:  List[str]         =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[str]         =  ['werewolf']
        lowercase__:  List[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf', 'I am your father']
        lowercase__:  Union[str, Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)

        lowercase__:  List[str]         =  ['werewolf', 'I am your father', 'doge']
        lowercase__:  Any         =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  ['My hovercraft', 'is full of eels']
        lowercase__:  Union[str, Any]         =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__							,					concatenate_texts=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Dict         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Tuple         =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  Any         =  ['我能吞下玻璃', '而不伤身体']
        lowercase__:  Union[str, Any]         =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  int         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Any         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertFalse(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Dict         =  ['']
        lowercase__:  int         =  ['Hypothesis']
        with self.assertRaises(lowerCAmelCase__					):
               cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)


if __name__ == "__main__":
 unittest.main()


