
'''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 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			List[Any]						,			) ->							List[Any]:
        snake_case_    							=				parent
        snake_case_    							=				13
        snake_case_    							=				7
        snake_case_    							=				True
        snake_case_    							=				True
        snake_case_    							=				True
        snake_case_    							=				True
        snake_case_    							=				True
        snake_case_    							=				99
        snake_case_    							=				32
        snake_case_    							=				2
        snake_case_    							=				4
        snake_case_    							=				37
        snake_case_    							=				"gelu"
        snake_case_    							=				0.1
        snake_case_    							=				0.1
        snake_case_    							=				5_12
        snake_case_    							=				16
        snake_case_    							=				2
        snake_case_    							=				0.02
        snake_case_    							=				3
        snake_case_    							=				4
        snake_case_    							=				None
        snake_case_    							=				self.vocab_size - 1

  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        snake_case_    							=				ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size     )

        snake_case_    							=				None
        if self.use_input_mask:
              snake_case_    							=				random_attention_mask([self.batch_size, self.seq_length]     )

        snake_case_    							=				None
        if self.use_token_type_ids:
              snake_case_    							=				ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size     )

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

        snake_case_    							=				None
        snake_case_    							=				None
        snake_case_    							=				None
        if self.use_labels:
              snake_case_    							=				ids_tensor([self.batch_size]						,			self.type_sequence_label_size     )
              snake_case_    							=				ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels     )
              snake_case_    							=				ids_tensor([self.batch_size]						,			self.num_choices     )

        snake_case_    							=				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						,			)

        snake_case_    							=				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 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Tuple						,			*lowerCAmelCase_	:			Dict     ) ->							str:
        snake_case_    							=				TFCTRLModel(config=lowerCAmelCase_     )
        snake_case_    							=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        snake_case_    							=				model(lowerCAmelCase_     )

        snake_case_    							=				[input_ids, None, input_mask]  # None is the input for 'past'
        snake_case_    							=				model(lowerCAmelCase_     )

        snake_case_    							=				model(lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Union[str, Any]						,			*lowerCAmelCase_	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				TFCTRLLMHeadModel(config=lowerCAmelCase_     )
        snake_case_    							=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)     )

  def 		_lowerCamelCase						(self	:			List[str]						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			List[str]						,			*lowerCAmelCase_	:			List[Any]     ) ->							List[Any]:
        snake_case_    							=				self.num_labels
        snake_case_    							=				ids_tensor([self.batch_size]						,			self.type_sequence_label_size     )
        snake_case_    							=				{
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "labels": sequence_labels,
        }
        snake_case_    							=				TFCTRLForSequenceClassification(lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)     )


  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        snake_case_    							=				self.prepare_config_and_inputs()

        (
            (
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						
        )    							=				config_and_inputs

        snake_case_    							=				{"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'''






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

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Optional[int]     ) ->							int:
        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 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				TFCTRLModelTester(self     )
        snake_case_    							=				ConfigTester(self						,			config_class=lowerCAmelCase_						,			n_embd=37     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Dict:
        self.config_tester.run_common_tests()

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Union[str, Any]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_lm_head(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							List[Any]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_    							=				[TFCTRLLMHeadModel]
        snake_case_    							=				[TFCTRLForSequenceClassification]

        for model_class in self.all_model_classes:
              snake_case_    							=				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:
                    snake_case_    							=				model.get_output_embeddings()
                    assert isinstance(lowerCAmelCase_						,			tf.keras.layers.Layer     )
                    snake_case_    							=				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:
                    snake_case_    							=				model.get_output_embeddings()
                    assert isinstance(lowerCAmelCase_						,			tf.keras.layers.Layer     )
                    snake_case_    							=				model.get_bias()
                    assert name is None
              else:
                    snake_case_    							=				model.get_output_embeddings()
                    assert x is None
                    snake_case_    							=				model.get_bias()
                    assert name is None


  @slow
  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							List[Any]:
        for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_    							=				TFCTRLModel.from_pretrained(lowerCAmelCase_     )
              self.assertIsNotNone(lowerCAmelCase_     )



@require_tf
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  @slow
  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Tuple:
        snake_case_    							=				TFCTRLLMHeadModel.from_pretrained("ctrl"     )
        snake_case_    							=				tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]]						,			dtype=tf.intaa     )  # Legal the president is
        snake_case_    							=				[
            1_18_59,
            0,
            16_11,
            8,
            5,
            1_50,
            2_64_49,
            2,
            19,
            3_48,
            4_69,
            3,
            25_95,
            48,
            2_07_40,
            24_65_33,
            24_65_33,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        snake_case_    							=				model.generate(lowerCAmelCase_						,			do_sample=lowerCAmelCase_     )
        self.assertListEqual(output_ids[0].numpy().tolist()						,			lowerCAmelCase_     )



'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     parser.add_argument(
         "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
     )
     parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
     parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

