
'''simple docstring'''

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
   import librosa  # noqa: E402

   __UpperCAmelCase												=   True
   __UpperCAmelCase												=   ''
except Exception as e:
   __UpperCAmelCase												=   False
   __UpperCAmelCase												=   (
       f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
   )


from PIL import Image  # noqa: E402


class 		_a     (							SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'mel_config.json'






   @register_to_config
   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 256    ,__SCREAMING_SNAKE_CASE = 256    ,__SCREAMING_SNAKE_CASE = 22050    ,__SCREAMING_SNAKE_CASE = 2048    ,__SCREAMING_SNAKE_CASE = 512    ,__SCREAMING_SNAKE_CASE = 80    ,__SCREAMING_SNAKE_CASE = 32    ,):
    SCREAMING_SNAKE_CASE  :  Dict								=		hop_length
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		sample_rate
    SCREAMING_SNAKE_CASE  :  Dict								=		n_fft
    SCREAMING_SNAKE_CASE  :  str								=		top_db
    SCREAMING_SNAKE_CASE  :  int								=		n_iter
    self.set_resolution(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		None

    if not _librosa_can_be_imported:
     raise ValueError(_import_error      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		x_res
    SCREAMING_SNAKE_CASE  :  Any								=		y_res
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.y_res
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.x_res * self.hop_length - 1






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None      ):
    if audio_file is not None:
     SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		librosa.load(__SCREAMING_SNAKE_CASE    ,mono=__SCREAMING_SNAKE_CASE    ,sr=self.sr      )
    else:
     SCREAMING_SNAKE_CASE  :  Any								=		raw_audio

    # Pad with silence if necessary.
    if len(self.audio      ) < self.x_res * self.hop_length:
     SCREAMING_SNAKE_CASE  :  Tuple								=		np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio      ),)      )]      )






   def 					__a			(       self      ):
    return len(self.audio      ) // self.slice_size






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE = 0      ):
    return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






   def 					__a			(       self      ):
    return self.sr






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		librosa.feature.melspectrogram(
        y=self.get_audio_slice(__SCREAMING_SNAKE_CASE      )    ,sr=self.sr    ,n_fft=self.n_fft    ,hop_length=self.hop_length    ,n_mels=self.n_mels      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		librosa.power_to_db(__SCREAMING_SNAKE_CASE    ,ref=np.max    ,top_db=self.top_db      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		(((log_S + self.top_db) * 255 / self.top_db).clip(0    ,255      ) + 0.5).astype(np.uinta      )
    SCREAMING_SNAKE_CASE  :  Dict								=		Image.fromarray(__SCREAMING_SNAKE_CASE      )
    return image






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		np.frombuffer(image.tobytes()    ,dtype='uint8'      ).reshape((image.height, image.width)      )
    SCREAMING_SNAKE_CASE  :  int								=		bytedata.astype('float'      ) * self.top_db / 255 - self.top_db
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		librosa.db_to_power(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		librosa.feature.inverse.mel_to_audio(
        __SCREAMING_SNAKE_CASE    ,sr=self.sr    ,n_fft=self.n_fft    ,hop_length=self.hop_length    ,n_iter=self.n_iter      )
    return audio




'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.ctrl.modeling_tf_ctrl import (
       TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFCTRLForSequenceClassification,
       TFCTRLLMHeadModel,
       TFCTRLModel,
   )


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Dict								=		parent
    SCREAMING_SNAKE_CASE  :  int								=		13
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		7
    SCREAMING_SNAKE_CASE  :  List[Any]								=		True
    SCREAMING_SNAKE_CASE  :  Dict								=		True
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		True
    SCREAMING_SNAKE_CASE  :  int								=		True
    SCREAMING_SNAKE_CASE  :  str								=		True
    SCREAMING_SNAKE_CASE  :  int								=		99
    SCREAMING_SNAKE_CASE  :  List[str]								=		32
    SCREAMING_SNAKE_CASE  :  List[str]								=		2
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		4
    SCREAMING_SNAKE_CASE  :  Tuple								=		37
    SCREAMING_SNAKE_CASE  :  str								=		'gelu'
    SCREAMING_SNAKE_CASE  :  Tuple								=		0.1
    SCREAMING_SNAKE_CASE  :  Any								=		0.1
    SCREAMING_SNAKE_CASE  :  List[str]								=		512
    SCREAMING_SNAKE_CASE  :  List[Any]								=		16
    SCREAMING_SNAKE_CASE  :  List[Any]								=		2
    SCREAMING_SNAKE_CASE  :  Any								=		0.02
    SCREAMING_SNAKE_CASE  :  List[str]								=		3
    SCREAMING_SNAKE_CASE  :  Any								=		4
    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.vocab_size - 1






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size      )

    SCREAMING_SNAKE_CASE  :  Dict								=		None
    if self.use_input_mask:
     SCREAMING_SNAKE_CASE  :  List[str]								=		random_attention_mask([self.batch_size, self.seq_length]      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    if self.use_token_type_ids:
     SCREAMING_SNAKE_CASE  :  str								=		ids_tensor([self.batch_size, self.seq_length]    ,self.type_vocab_size      )

    SCREAMING_SNAKE_CASE  :  int								=		None
    if self.use_mc_token_ids:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ids_tensor([self.batch_size, self.num_choices]    ,self.seq_length      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		None
    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    SCREAMING_SNAKE_CASE  :  str								=		None
    if self.use_labels:
     SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ids_tensor([self.batch_size, self.seq_length]    ,self.num_labels      )
     SCREAMING_SNAKE_CASE  :  str								=		ids_tensor([self.batch_size]    ,self.num_choices      )

    SCREAMING_SNAKE_CASE  :  Dict								=		CTRLConfig(
        vocab_size=self.vocab_size    ,n_embd=self.hidden_size    ,n_layer=self.num_hidden_layers    ,n_head=self.num_attention_heads    ,n_positions=self.max_position_embeddings    ,pad_token_id=self.pad_token_id    ,)

    SCREAMING_SNAKE_CASE  :  Tuple								=		ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,2      )

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
        token_type_ids,
        mc_token_ids,
        sequence_labels,
        token_labels,
        choice_labels,
    )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,*__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  int								=		TFCTRLModel(config=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[input_ids, None, input_mask]  # None is the input for 'past'
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		model(__SCREAMING_SNAKE_CASE      )

    self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,*__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		TFCTRLLMHeadModel(config=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
    SCREAMING_SNAKE_CASE  :  Any								=		model(__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,*__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
    SCREAMING_SNAKE_CASE  :  Dict								=		{
        'input_ids': input_ids,
        'token_type_ids': token_type_ids,
        'labels': sequence_labels,
    }
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		TFCTRLForSequenceClassification(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		model(__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.prepare_config_and_inputs()

    (
        (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, 
    )  :  List[Any]								=		config_and_inputs

    SCREAMING_SNAKE_CASE  :  int								=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict



@require_tf
class 		_a     (							SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A		= (TFCTRLLMHeadModel,) if is_tf_available() else ()
   A		= (
       {
           'feature-extraction': TFCTRLModel,
           'text-classification': TFCTRLForSequenceClassification,
           'text-generation': TFCTRLLMHeadModel,
           'zero-shot': TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A		= False
   A		= False






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
     # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
     # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
     # config could not be created.
     return True

    return False






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		TFCTRLModelTester(self      )
    SCREAMING_SNAKE_CASE  :  Dict								=		ConfigTester(self    ,config_class=__SCREAMING_SNAKE_CASE    ,n_embd=37      )






   def 					__a			(       self      ):
    self.config_tester.run_common_tests()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_model(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_lm_head(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_for_sequence_classification(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE  :  Any								=		[TFCTRLLMHeadModel]
    SCREAMING_SNAKE_CASE  :  str								=		[TFCTRLForSequenceClassification]

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  List[str]								=		model_class(__SCREAMING_SNAKE_CASE      )
     model.build()  # may be needed for the get_bias() call below
     assert isinstance(model.get_input_embeddings()    ,tf.keras.layers.Layer      )

     if model_class in list_lm_models:
      SCREAMING_SNAKE_CASE  :  Dict								=		model.get_output_embeddings()
      assert isinstance(__SCREAMING_SNAKE_CASE    ,tf.keras.layers.Layer      )
      SCREAMING_SNAKE_CASE  :  Any								=		model.get_bias()
      assert isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
      for k, v in name.items():
       assert isinstance(__SCREAMING_SNAKE_CASE    ,tf.Variable      )
     elif model_class in list_other_models_with_output_ebd:
      SCREAMING_SNAKE_CASE  :  Any								=		model.get_output_embeddings()
      assert isinstance(__SCREAMING_SNAKE_CASE    ,tf.keras.layers.Layer      )
      SCREAMING_SNAKE_CASE  :  str								=		model.get_bias()
      assert name is None
     else:
      SCREAMING_SNAKE_CASE  :  List[Any]								=		model.get_output_embeddings()
      assert x is None
      SCREAMING_SNAKE_CASE  :  int								=		model.get_bias()
      assert name is None






   @slow
   def 					__a			(       self      ):
    for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE  :  List[str]								=		TFCTRLModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
     self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )



@require_tf
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		TFCTRLLMHeadModel.from_pretrained('ctrl'      )
    SCREAMING_SNAKE_CASE  :  Any								=		tf.convert_to_tensor([[11859, 0, 1611, 8]]    ,dtype=tf.intaa      )  # Legal the president is
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[
        11859,
        0,
        1611,
        8,
        5,
        150,
        26449,
        2,
        19,
        348,
        469,
        3,
        2595,
        48,
        20740,
        246533,
        246533,
        19,
        30,
        5,
    ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model.generate(__SCREAMING_SNAKE_CASE    ,do_sample=__SCREAMING_SNAKE_CASE      )
    self.assertListEqual(output_ids[0].numpy().tolist()    ,__SCREAMING_SNAKE_CASE      )


