






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


A_	:	List[Any]				=				{
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	int				=				[
       'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Speech2Text2ForCausalLM',
       'Speech2Text2PreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
   from .processing_speech_to_text_a import SpeechaTextaProcessor
   from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_to_text_a import (
          SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechaTextaForCausalLM,
          SpeechaTextaPreTrainedModel,
      )

else:
   import sys

   A_	:	str				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    ):
         UpperCamelCase_:      Tuple					   =       parent
         UpperCamelCase_:      Optional[Any]					   =       1_3
         UpperCamelCase_:      Any					   =       7
         UpperCamelCase_:      Any					   =       True
         UpperCamelCase_:      List[Any]					   =       True
         UpperCamelCase_:      Dict					   =       True
         UpperCamelCase_:      Optional[Any]					   =       True
         UpperCamelCase_:      Tuple					   =       True
         UpperCamelCase_:      Any					   =       9_9
         UpperCamelCase_:      Dict					   =       3_2
         UpperCamelCase_:      Dict					   =       2
         UpperCamelCase_:      List[Any]					   =       4
         UpperCamelCase_:      int					   =       3_7
         UpperCamelCase_:      List[str]					   =       'gelu'
         UpperCamelCase_:      Union[str, Any]					   =       0.1
         UpperCamelCase_:      int					   =       0.1
         UpperCamelCase_:      int					   =       5_1_2
         UpperCamelCase_:      str					   =       1_6
         UpperCamelCase_:      List[str]					   =       2
         UpperCamelCase_:      Optional[int]					   =       0.0_2
         UpperCamelCase_:      int					   =       3
         UpperCamelCase_:      Tuple					   =       4
         UpperCamelCase_:      List[Any]					   =       None
         UpperCamelCase_:      Union[str, Any]					   =       self.vocab_size - 1





       def     _a					(      self							):
         UpperCamelCase_:      Optional[Any]					   =       ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size							)

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

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

         UpperCamelCase_:      Dict					   =       None
         if self.use_mc_token_ids:
           UpperCamelCase_:      str					   =       ids_tensor([self.batch_size, self.num_choices]						,    self.seq_length							)

         UpperCamelCase_:      Any					   =       None
         UpperCamelCase_:      Any					   =       None
         UpperCamelCase_:      Dict					   =       None
         if self.use_labels:
           UpperCamelCase_:      str					   =       ids_tensor([self.batch_size]						,    self.type_sequence_label_size							)
           UpperCamelCase_:      Dict					   =       ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels							)
           UpperCamelCase_:      Union[str, Any]					   =       ids_tensor([self.batch_size]						,    self.num_choices							)

         UpperCamelCase_:      List[str]					   =       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						,    )

         UpperCamelCase_:      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						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    *_lowerCamelCase							):
         UpperCamelCase_:      Dict					   =       TFCTRLModel(config=_lowerCamelCase							)
         UpperCamelCase_:      int					   =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         UpperCamelCase_:      Optional[int]					   =       model(_lowerCamelCase							)

         UpperCamelCase_:      Optional[int]					   =       [input_ids, None, input_mask]  # None is the input for 'past'
         UpperCamelCase_:      Dict					   =       model(_lowerCamelCase							)

         UpperCamelCase_:      List[str]					   =       model(_lowerCamelCase							)

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





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





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





       def     _a					(      self							):
         UpperCamelCase_:      int					   =       self.prepare_config_and_inputs()

         (
             (
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,
         ):      str					   =       config_and_inputs

         UpperCamelCase_:      List[str]					   =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_tf
class        _lowerCAmelCase( UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase      ):





       """simple docstring"""







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





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         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							):
         UpperCamelCase_:      List[str]					   =       TFCTRLModelTester(self							)
         UpperCamelCase_:      List[Any]					   =       ConfigTester(self						,    config_class=_lowerCamelCase						,    n_embd=3_7							)





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





       def     _a					(      self							):
         UpperCamelCase_:      Dict					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Optional[int]					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Union[str, Any]					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      Union[str, Any]					   =       self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_:      Optional[Any]					   =       [TFCTRLLMHeadModel]
         UpperCamelCase_:      Optional[Any]					   =       [TFCTRLForSequenceClassification]

         for model_class in self.all_model_classes:
           UpperCamelCase_:      Union[str, Any]					   =       model_class(_lowerCamelCase							)
           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:
             UpperCamelCase_:      str					   =       model.get_output_embeddings()
             assert isinstance(_lowerCamelCase						,    tf.keras.layers.Layer							)
             UpperCamelCase_:      List[Any]					   =       model.get_bias()
             assert isinstance(_lowerCamelCase						,    _lowerCamelCase							)
             for k, v in name.items():
               assert isinstance(_lowerCamelCase						,    tf.Variable							)
           elif model_class in list_other_models_with_output_ebd:
             UpperCamelCase_:      Any					   =       model.get_output_embeddings()
             assert isinstance(_lowerCamelCase						,    tf.keras.layers.Layer							)
             UpperCamelCase_:      List[Any]					   =       model.get_bias()
             assert name is None
           else:
             UpperCamelCase_:      Dict					   =       model.get_output_embeddings()
             assert x is None
             UpperCamelCase_:      Optional[int]					   =       model.get_bias()
             assert name is None





       @slow
       def     _a					(      self							):
         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_:      Optional[Any]					   =       TFCTRLModel.from_pretrained(_lowerCamelCase							)
           self.assertIsNotNone(_lowerCamelCase							)







@require_tf
class        _lowerCAmelCase( unittest.TestCase      ):





       """simple docstring"""







       @slow
       def     _a					(      self							):
         UpperCamelCase_:      Optional[int]					   =       TFCTRLLMHeadModel.from_pretrained('ctrl'							)
         UpperCamelCase_:      Any					   =       tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]						,    dtype=tf.intaa							)  # Legal the president is
         UpperCamelCase_:      Dict					   =       [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         UpperCamelCase_:      List[str]					   =       model.generate(_lowerCamelCase						,    do_sample=_lowerCamelCase							)
         self.assertListEqual(output_ids[0].numpy().tolist()						,    _lowerCamelCase							)