


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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       : Union[str, Any]			     =       """platform"""

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=None		,			_UpperCAmelCase=None		,			_UpperCAmelCase=None		,			_UpperCAmelCase=None		,			_UpperCAmelCase=None		,			_UpperCAmelCase=None		,			)    ->  Optional[Any]:
    if attention_mask is None:
        lowerCamelCase__					:   List[str]      				=	np.where(input_ids != config.pad_token_id		,			1		,			0				)
    if decoder_attention_mask is None:
        lowerCamelCase__					:   Tuple      				=	np.where(decoder_input_ids != config.pad_token_id		,			1		,			0				)
    if head_mask is None:
        lowerCamelCase__					:   Any      				=	np.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        lowerCamelCase__					:   Tuple      				=	np.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        lowerCamelCase__					:   Any      				=	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      lowerCAmelCase							:
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict=13    ,  UpperCAmelCase						:   List[Any]=7    ,  UpperCAmelCase						:   Union[str, Any]=True    ,  UpperCAmelCase						:   Union[str, Any]=False    ,  UpperCAmelCase						:   int=99    ,  UpperCAmelCase						:   Tuple=16    ,  UpperCAmelCase						:   Any=2    ,  UpperCAmelCase						:   Any=4    ,  UpperCAmelCase						:   Optional[int]=4    ,  UpperCAmelCase						:   Tuple="gelu"    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   str=0.1    ,  UpperCAmelCase						:   List[Any]=32    ,  UpperCAmelCase						:   Optional[Any]=2    ,  UpperCAmelCase						:   Union[str, Any]=1    ,  UpperCAmelCase						:   List[str]=0    ,  UpperCAmelCase						:   int=0.0_2    ,  )    ->			List[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   List[str]      				=	batch_size
     lowerCamelCase__					:   List[str]      				=	seq_length
     lowerCamelCase__					:   Optional[int]      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Dict      				=	vocab_size
     lowerCamelCase__					:   List[Any]      				=	hidden_size
     lowerCamelCase__					:   Dict      				=	num_hidden_layers
     lowerCamelCase__					:   Optional[int]      				=	num_attention_heads
     lowerCamelCase__					:   Optional[int]      				=	intermediate_size
     lowerCamelCase__					:   Optional[Any]      				=	hidden_act
     lowerCamelCase__					:   List[Any]      				=	hidden_dropout_prob
     lowerCamelCase__					:   Optional[int]      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   Tuple      				=	max_position_embeddings
     lowerCamelCase__					:   Union[str, Any]      				=	eos_token_id
     lowerCamelCase__					:   int      				=	pad_token_id
     lowerCamelCase__					:   Union[str, Any]      				=	bos_token_id
     lowerCamelCase__					:   Dict      				=	initializer_range
 def   A_			(   self						:   List[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Dict      				=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size			)    ,  3    ,  self.vocab_size			)
     lowerCamelCase__					:   Union[str, Any]      				=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,  dtype=np.intaa			))    ,  -1			)

     lowerCamelCase__					:   List[Any]      				=	shift_tokens_right(__UpperCamelCase    ,  1    ,  2			)

     lowerCamelCase__					:   int      				=	BlenderbotConfig(
         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=__UpperCamelCase    ,  )
     lowerCamelCase__					:   Optional[Any]      				=	prepare_blenderbot_inputs_dict(__UpperCamelCase    ,  __UpperCamelCase    ,  __UpperCamelCase			)
     return config, inputs_dict
 def   A_			(   self						:   str			)    ->			Optional[int]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.prepare_config_and_inputs()
     return config, inputs_dict
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	20
     lowerCamelCase__					:   List[Any]      				=	model_class_name(__UpperCamelCase			)

     lowerCamelCase__					:   Optional[int]      				=	model.encode(inputs_dict['input_ids']			)

     lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     lowerCamelCase__					:   Union[str, Any]      				=	model.init_cache(decoder_input_ids.shape[0]    ,  __UpperCamelCase    ,  __UpperCamelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,  dtype='i4'			)

     lowerCamelCase__					:   Tuple      				=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]    ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,  )
     lowerCamelCase__					:   Optional[int]      				=	model.decode(
         decoder_input_ids[:, :-1]    ,  __UpperCamelCase    ,  decoder_attention_mask=__UpperCamelCase    ,  past_key_values=__UpperCamelCase    ,  decoder_position_ids=__UpperCamelCase    ,  )

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

     lowerCamelCase__					:   Optional[int]      				=	model.decode(__UpperCamelCase    ,  __UpperCamelCase			)

     lowerCamelCase__					:   Any      				=	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   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Any			)    ->			List[str]:
     lowerCamelCase__					:   List[Any]      				=	20
     lowerCamelCase__					:   List[str]      				=	model_class_name(__UpperCamelCase			)

     lowerCamelCase__					:   int      				=	model.encode(inputs_dict['input_ids']			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

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

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

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

     lowerCamelCase__					:   Union[str, Any]      				=	model.decode(__UpperCamelCase    ,  __UpperCamelCase    ,  decoder_attention_mask=__UpperCamelCase			)

     lowerCamelCase__					:   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}"""			)


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 UpperCAmelCase__									=	99
 def   A_			(   self						:   Any			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	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__					:   Tuple      				=	input_ids.shape[0]
     lowerCamelCase__					:   Any      				=	BlenderbotConfig(
         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   A_			(   self						:   Tuple			)    ->			List[Any]:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self._get_config_and_data()
     lowerCamelCase__					:   str      				=	FlaxBlenderbotForConditionalGeneration(__UpperCamelCase			)
     lowerCamelCase__					:   int      				=	lm_model(input_ids=__UpperCamelCase			)
     lowerCamelCase__					:   List[Any]      				=	(batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['logits'].shape    ,  __UpperCamelCase			)
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	BlenderbotConfig(
         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__					:   List[str]      				=	FlaxBlenderbotForConditionalGeneration(__UpperCamelCase			)
     lowerCamelCase__					:   Dict      				=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,  dtype=np.intaa			)
     lowerCamelCase__					:   Union[str, Any]      				=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,  dtype=np.intaa			)
     lowerCamelCase__					:   Union[str, Any]      				=	lm_model(input_ids=__UpperCamelCase    ,  decoder_input_ids=__UpperCamelCase			)
     lowerCamelCase__					:   str      				=	(*summary.shape, config.vocab_size)
     self.assertEqual(outputs['logits'].shape    ,  __UpperCamelCase			)







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


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase,						__UpperCamelCase    ):
 UpperCAmelCase__									=	True
 UpperCAmelCase__									=	(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 UpperCAmelCase__									=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
 def   A_			(   self						:   Union[str, Any]			)    ->			Optional[int]:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxBlenderbotModelTester(self			)
 def   A_			(   self						:   int			)    ->			Dict:
     lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(__UpperCamelCase    ,  __UpperCamelCase    ,  __UpperCamelCase			)
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	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(__UpperCamelCase    ,  __UpperCamelCase    ,  __UpperCamelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   Optional[int]      				=	self._prepare_for_class(__UpperCamelCase    ,  __UpperCamelCase			)
             lowerCamelCase__					:   Dict      				=	model_class(__UpperCamelCase			)

             @jax.jit
             def encode_jitted(UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Dict=None    ,  **UpperCAmelCase						:   Tuple			):
                 return model.encode(input_ids=__UpperCamelCase    ,  attention_mask=__UpperCamelCase			)

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	encode_jitted(**__UpperCamelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Any      				=	encode_jitted(**__UpperCamelCase			).to_tuple()

             self.assertEqual(len(__UpperCamelCase			)    ,  len(__UpperCamelCase			)			)
             for jitted_output, output in zip(__UpperCamelCase    ,  __UpperCamelCase			):
                 self.assertEqual(jitted_output.shape    ,  output.shape			)
 def   A_			(   self						:   int			)    ->			Optional[Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase__					:   Dict      				=	{
                 '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(UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   int			):
                 return model.decode(
                     decoder_input_ids=__UpperCamelCase    ,  decoder_attention_mask=__UpperCamelCase    ,  encoder_outputs=__UpperCamelCase    ,  )

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Optional[Any]      				=	decode_jitted(**__UpperCamelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[Any]      				=	decode_jitted(**__UpperCamelCase			).to_tuple()

             self.assertEqual(len(__UpperCamelCase			)    ,  len(__UpperCamelCase			)			)
             for jitted_output, output in zip(__UpperCamelCase    ,  __UpperCamelCase			):
                 self.assertEqual(jitted_output.shape    ,  output.shape			)
 @slow
 def   A_			(   self						:   Optional[int]			)    ->			int:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					:   Union[str, Any]      				=	model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         lowerCamelCase__					:   Any      				=	np.ones((1, 1)			) * model.config.eos_token_id
         lowerCamelCase__					:   Optional[int]      				=	model(__UpperCamelCase			)
         self.assertIsNotNone(__UpperCamelCase			)







 @unittest.skipUnless(jax_device != 'cpu'    ,  '3B test too slow on CPU.'			)
 @slow
 def   A_			(   self						:   Optional[Any]			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
     lowerCamelCase__					:   Tuple      				=	{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

     lowerCamelCase__					:   str      				=	FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'    ,  from_pt=__UpperCamelCase			)
     lowerCamelCase__					:   int      				=	BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

     lowerCamelCase__					:   List[Any]      				=	['Sam']
     lowerCamelCase__					:   Any      				=	tokenizer(__UpperCamelCase    ,  return_tensors='jax'			)

     lowerCamelCase__					:   str      				=	model.generate(**__UpperCamelCase    ,  **__UpperCamelCase			)
     lowerCamelCase__					:   List[str]      				=	'Sam is a great name. It means \"sun\" in Gaelic.'

     lowerCamelCase__					:   str      				=	tokenizer.batch_decode(__UpperCamelCase    ,  **__UpperCamelCase			)
     assert generated_txt[0].strip() == tgt_text












import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[int]      				=	'laion/clap-htsat-unfused'
     lowerCamelCase__					:   List[Any]      				=	tempfile.mkdtemp()
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     shutil.rmtree(self.tmpdirname			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizer()
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase__					:   List[Any]      				=	ClapProcessor.from_pretrained(self.tmpdirname			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=self.get_tokenizer()    ,  feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase__					:   Dict      				=	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor(do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     lowerCamelCase__					:   str      				=	ClapProcessor.from_pretrained(
         self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Any      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	floats_list((3, 1000)			)

     lowerCamelCase__					:   Tuple      				=	feature_extractor(UpperCAmelCase    ,  return_tensors='np'			)
     lowerCamelCase__					:   Tuple      				=	processor(audios=UpperCAmelCase    ,  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   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[Any]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Any      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	'This is a test string'

     lowerCamelCase__					:   Union[str, Any]      				=	processor(text=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	tokenizer(UpperCAmelCase			)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]			)
 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[str]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[int]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__					:   List[Any]      				=	processor.batch_decode(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	tokenizer.batch_decode(UpperCAmelCase			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     self.assertListEqual(
         processor.model_input_names[2:]    ,  feature_extractor.model_input_names    ,  msg='`processor` and `feature_extractor` model input names do not match'    ,  )





