
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
       _UpperCamelCase:					int     ='platform'

       import jax
       import jax.numpy as jnp

       from transformers import AutoTokenizer
       from transformers.models.mbart.modeling_flax_mbart import (
           FlaxMBartForConditionalGeneration,
           FlaxMBartForQuestionAnswering,
           FlaxMBartForSequenceClassification,
           FlaxMBartModel,
           shift_tokens_right,
       )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Tuple=None		,				__SCREAMING_SNAKE_CASE					:  int=None		,				__SCREAMING_SNAKE_CASE					:  str=None		,				__SCREAMING_SNAKE_CASE					:  Any=None		,				__SCREAMING_SNAKE_CASE					:  Any=None		,				__SCREAMING_SNAKE_CASE					:  Optional[int]=None		,				):






  """simple docstring"""




  if attention_mask is None:
    _lowerCAmelCase						=		np.where(input_ids != config.pad_token_id		,				1		,				0    )
  if decoder_attention_mask is None:
    _lowerCAmelCase						=		np.where(decoder_input_ids != config.pad_token_id		,				1		,				0    )
  if head_mask is None:
    _lowerCAmelCase						=		np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    _lowerCAmelCase						=		np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    _lowerCAmelCase						=		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": decoder_attention_mask,
  }





class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def __init__(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			str=13  ,      _lowerCAmelCase					:			Dict=7  ,      _lowerCAmelCase					:			Dict=True  ,      _lowerCAmelCase					:			Any=False  ,      _lowerCAmelCase					:			Optional[Any]=99  ,      _lowerCAmelCase					:			List[str]=16  ,      _lowerCAmelCase					:			Union[str, Any]=2  ,      _lowerCAmelCase					:			Any=4  ,      _lowerCAmelCase					:			Union[str, Any]=4  ,      _lowerCAmelCase					:			Optional[int]="gelu"  ,      _lowerCAmelCase					:			List[str]=0.1  ,      _lowerCAmelCase					:			Optional[int]=0.1  ,      _lowerCAmelCase					:			List[str]=32  ,      _lowerCAmelCase					:			List[Any]=2  ,      _lowerCAmelCase					:			Optional[Any]=1  ,      _lowerCAmelCase					:			Dict=0  ,      _lowerCAmelCase					:			int=2  ,      _lowerCAmelCase					:			Optional[Any]=0.02  ,      )					->   Dict:
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		seq_length
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_labels
         _lowerCAmelCase						=		vocab_size
         _lowerCAmelCase						=		hidden_size
         _lowerCAmelCase						=		num_hidden_layers
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		intermediate_size
         _lowerCAmelCase						=		hidden_act
         _lowerCAmelCase						=		hidden_dropout_prob
         _lowerCAmelCase						=		attention_probs_dropout_prob
         _lowerCAmelCase						=		max_position_embeddings
         _lowerCAmelCase						=		eos_token_id
         _lowerCAmelCase						=		pad_token_id
         _lowerCAmelCase						=		bos_token_id
         _lowerCAmelCase						=		decoder_start_token_id
         _lowerCAmelCase						=		initializer_range



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Dict:
         _lowerCAmelCase						=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,      self.vocab_size       )  ,      3  ,      self.vocab_size       )
         _lowerCAmelCase						=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,      dtype=np.intaa       ))  ,      -1       )

         _lowerCAmelCase						=		shift_tokens_right(_lowerCAmelCase  ,      1       )

         _lowerCAmelCase						=		MBartConfig(
             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  ,      decoder_start_token_id=self.decoder_start_token_id  ,      initializer_range=self.initializer_range  ,      use_cache=_lowerCAmelCase  ,      )
         _lowerCAmelCase						=		prepare_mbart_inputs_dict(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         return config, inputs_dict



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Optional[int]:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.prepare_config_and_inputs()
         return config, inputs_dict



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[int]       )					->   Optional[Any]:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(_lowerCAmelCase       )

         _lowerCAmelCase						=		model.encode(inputs_dict['input_ids']       )

         _lowerCAmelCase	,       _lowerCAmelCase						=		(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         _lowerCAmelCase						=		model.init_cache(decoder_input_ids.shape[0]  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,      dtype='i4'       )

         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]  ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,      )
         _lowerCAmelCase						=		model.decode(
             decoder_input_ids[:, :-1]  ,      _lowerCAmelCase  ,      decoder_attention_mask=_lowerCAmelCase  ,      past_key_values=_lowerCAmelCase  ,      decoder_position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,      dtype='i4'       )
         _lowerCAmelCase						=		model.decode(
             decoder_input_ids[:, -1:]  ,      _lowerCAmelCase  ,      decoder_attention_mask=_lowerCAmelCase  ,      past_key_values=outputs_cache.past_key_values  ,      decoder_position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		model.decode(_lowerCAmelCase  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		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					:			Union[str, Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			List[str]       )					->   Tuple:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(_lowerCAmelCase       )

         _lowerCAmelCase						=		model.encode(inputs_dict['input_ids']       )

         _lowerCAmelCase	,       _lowerCAmelCase						=		(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         _lowerCAmelCase						=		jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
             ]  ,      axis=-1  ,      )

         _lowerCAmelCase						=		model.init_cache(decoder_input_ids.shape[0]  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]  ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,      )

         _lowerCAmelCase						=		model.decode(
             decoder_input_ids[:, :-1]  ,      _lowerCAmelCase  ,      decoder_attention_mask=_lowerCAmelCase  ,      past_key_values=_lowerCAmelCase  ,      decoder_position_ids=_lowerCAmelCase  ,      )
         _lowerCAmelCase						=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,      dtype='i4'       )
         _lowerCAmelCase						=		model.decode(
             decoder_input_ids[:, -1:]  ,      _lowerCAmelCase  ,      past_key_values=outputs_cache.past_key_values  ,      decoder_attention_mask=_lowerCAmelCase  ,      decoder_position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		model.decode(_lowerCAmelCase  ,      _lowerCAmelCase  ,      decoder_attention_mask=_lowerCAmelCase       )

         _lowerCAmelCase						=		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 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							99



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Optional[int]:
         _lowerCAmelCase						=		np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]  ,      dtype=np.intaa  ,      )

         _lowerCAmelCase						=		input_ids.shape[0]
         _lowerCAmelCase						=		MBartConfig(
             vocab_size=self.vocab_size  ,      d_model=24  ,      encoder_layers=2  ,      decoder_layers=2  ,      encoder_attention_heads=2  ,      decoder_attention_heads=2  ,      encoder_ffn_dim=32  ,      decoder_ffn_dim=32  ,      max_position_embeddings=48  ,      eos_token_id=2  ,      pad_token_id=1  ,      bos_token_id=0  ,      )
         return config, input_ids, batch_size



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   int:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self._get_config_and_data()
         _lowerCAmelCase						=		FlaxMBartForSequenceClassification(_lowerCAmelCase       )
         _lowerCAmelCase						=		model(input_ids=_lowerCAmelCase  ,      decoder_input_ids=_lowerCAmelCase       )
         _lowerCAmelCase						=		(batch_size, config.num_labels)
         self.assertEqual(outputs['logits'].shape  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Dict:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self._get_config_and_data()
         _lowerCAmelCase						=		FlaxMBartForQuestionAnswering(_lowerCAmelCase       )
         _lowerCAmelCase						=		model(input_ids=_lowerCAmelCase       )

         self.assertEqual(outputs['start_logits'].shape  ,      input_ids.shape       )
         self.assertEqual(outputs['end_logits'].shape  ,      input_ids.shape       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   Tuple:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self._get_config_and_data()
         _lowerCAmelCase						=		FlaxMBartForConditionalGeneration(_lowerCAmelCase       )
         _lowerCAmelCase						=		lm_model(input_ids=_lowerCAmelCase       )
         _lowerCAmelCase						=		(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Union[str, Any]:
         _lowerCAmelCase						=		MBartConfig(
             vocab_size=self.vocab_size  ,      d_model=14  ,      encoder_layers=2  ,      decoder_layers=2  ,      encoder_attention_heads=2  ,      decoder_attention_heads=2  ,      encoder_ffn_dim=8  ,      decoder_ffn_dim=8  ,      max_position_embeddings=48  ,      )
         _lowerCAmelCase						=		FlaxMBartForConditionalGeneration(_lowerCAmelCase       )
         _lowerCAmelCase						=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,      dtype=np.intaa       )
         _lowerCAmelCase						=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,      dtype=np.intaa       )
         _lowerCAmelCase						=		lm_model(input_ids=_lowerCAmelCase  ,      decoder_input_ids=_lowerCAmelCase       )
         _lowerCAmelCase						=		(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['logits'].shape  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Dict:
         _lowerCAmelCase						=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,      dtype=np.intaa       )
         _lowerCAmelCase						=		shift_tokens_right(_lowerCAmelCase  ,      1       )
         _lowerCAmelCase						=		np.equal(_lowerCAmelCase  ,      1       ).astype(np.floataa       ).sum()
         _lowerCAmelCase						=		np.equal(_lowerCAmelCase  ,      1       ).astype(np.floataa       ).sum()
         self.assertEqual(shifted.shape  ,      input_ids.shape       )
         self.assertEqual(_lowerCAmelCase  ,      n_pad_before - 1       )
         self.assertTrue(np.equal(shifted[:, 0]  ,      2       ).all()       )





@require_flax
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase		,  SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							True
       UpperCamelCase_				 =							(
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase_				 =							(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Any:
         _lowerCAmelCase						=		FlaxMBartModelTester(self       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Optional[int]:
         _lowerCAmelCase	,       _lowerCAmelCase						=		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					:			Tuple       )					->   Dict:
         _lowerCAmelCase	,       _lowerCAmelCase						=		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					:			List[Any]       )					->   Dict:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
             _lowerCAmelCase						=		self._prepare_for_class(_lowerCAmelCase  ,      _lowerCAmelCase       )
             _lowerCAmelCase						=		model_class(_lowerCAmelCase       )

             @jax.jit
             def encode_jitted(_lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Any=None  ,      **_lowerCAmelCase					:			List[str]       ):
               return model.encode(input_ids=_lowerCAmelCase  ,      attention_mask=_lowerCAmelCase       )

             with self.subTest('JIT Enabled'       ):
               _lowerCAmelCase						=		encode_jitted(**_lowerCAmelCase       ).to_tuple()

             with self.subTest('JIT Disabled'       ):
               with jax.disable_jit():
                 _lowerCAmelCase						=		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					:			Union[str, Any]       )					->   Dict:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
             _lowerCAmelCase						=		model_class(_lowerCAmelCase       )
             _lowerCAmelCase						=		model.encode(inputs_dict['input_ids']  ,      inputs_dict['attention_mask']       )

             _lowerCAmelCase						=		{
                 '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					:			Dict  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Any       ):
               return model.decode(
                   decoder_input_ids=_lowerCAmelCase  ,      decoder_attention_mask=_lowerCAmelCase  ,      encoder_outputs=_lowerCAmelCase  ,      )

             with self.subTest('JIT Enabled'       ):
               _lowerCAmelCase						=		decode_jitted(**_lowerCAmelCase       ).to_tuple()

             with self.subTest('JIT Disabled'       ):
               with jax.disable_jit():
                 _lowerCAmelCase						=		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[str]       )					->   Dict:
         for model_class_name in self.all_model_classes:
           _lowerCAmelCase						=		model_class_name.from_pretrained('facebook/mbart-large-cc25'  ,      from_pt=_lowerCAmelCase       )
           # FlaxMBartForSequenceClassification expects eos token in input_ids
           _lowerCAmelCase						=		np.ones((1, 1)       ) * model.config.eos_token_id
           _lowerCAmelCase						=		model(_lowerCAmelCase       )
           self.assertIsNotNone(_lowerCAmelCase       )





@require_flax
@require_sentencepiece
@require_tokenizers
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							[
           ''' UN Chief Says There Is No Military Solution in Syria''',
       ]
       UpperCamelCase_				 =							[
           '''Şeful ONU declară că nu există o soluţie militară în Siria''',
       ]
       UpperCamelCase_				 =							'''facebook/mbart-large-en-ro'''



       @cached_property
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[int]:
         return AutoTokenizer.from_pretrained(self.model_name       )



       @cached_property
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Optional[int]:
         _lowerCAmelCase						=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name  ,      from_pt=_lowerCAmelCase       )
         return model



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.translate_src_text(**_lowerCAmelCase       )
         self.assertListEqual(self.expected_text  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[int]       )					->   Tuple:
         _lowerCAmelCase						=		self.tokenizer(self.src_text  ,      **_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		self.model.generate(
             model_inputs.input_ids  ,      attention_mask=model_inputs.attention_mask  ,      decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']  ,      early_stopping=_lowerCAmelCase  ,      num_beams=2  ,      ).sequences
         _lowerCAmelCase						=		self.tokenizer.batch_decode(_lowerCAmelCase  ,      skip_special_tokens=_lowerCAmelCase       )
         return generated_words






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Any:
         self._assert_generated_batch_equal_expected()


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      return_tensors='np'       )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
