"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import 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 import (
     TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
     TFXLMForSequenceClassification,
     TFXLMForTokenClassification,
     TFXLMModel,
     TFXLMWithLMHeadModel,
     XLMConfig,
 )






class  a__     :


   def __init__(  self,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				13
        lowercase__								=				7
        lowercase__								=				True
        lowercase__								=				True
        lowercase__								=				True
        lowercase__								=				True
        lowercase__								=				True
        lowercase__								=				False
        lowercase__								=				False
        lowercase__								=				False
        lowercase__								=				2
        lowercase__								=				99
        lowercase__								=				0
        lowercase__								=				32
        lowercase__								=				2
        lowercase__								=				4
        lowercase__								=				0.1
        lowercase__								=				0.1
        lowercase__								=				512
        lowercase__								=				16
        lowercase__								=				2
        lowercase__								=				0.02
        lowercase__								=				3
        lowercase__								=				4
        lowercase__								=				"last"
        lowercase__								=				True
        lowercase__								=				None
        lowercase__								=				0


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)
        lowercase__								=				random_attention_mask([self.batch_size, self.seq_length],					dtype=tf.floataa	)

        lowercase__								=				None
        if self.use_input_lengths:
             lowercase__								=				(
                 ids_tensor([self.batch_size],					vocab_size=2	) + self.seq_length - 2
             )  # small variation of seq_length

        lowercase__								=				None
        if self.use_token_type_ids:
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.n_langs	)

        lowercase__								=				None
        lowercase__								=				None
        lowercase__								=				None
        if self.use_labels:
             lowercase__								=				ids_tensor([self.batch_size],					self.type_sequence_label_size	)
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.num_labels	)
             lowercase__								=				ids_tensor([self.batch_size],					2,					dtype=tf.floataa	)
             lowercase__								=				ids_tensor([self.batch_size],					self.num_choices	)

        lowercase__								=				XLMConfig(
            vocab_size=self.vocab_size,					n_special=self.n_special,					emb_dim=self.hidden_size,					n_layers=self.num_hidden_layers,					n_heads=self.num_attention_heads,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					gelu_activation=self.gelu_activation,					sinusoidal_embeddings=self.sinusoidal_embeddings,					asm=self.asm,					causal=self.causal,					n_langs=self.n_langs,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					summary_type=self.summary_type,					use_proj=self.use_proj,					bos_token_id=self.bos_token_id,					)

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				TFXLMModel(config=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)

        lowercase__								=				[input_ids, input_mask]
        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				TFXLMWithLMHeadModel(_SCREAMING_SNAKE_CASE	)

        lowercase__								=				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)

        lowercase__								=				outputs

        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				TFXLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE	)

        lowercase__								=				{"input_ids": input_ids, "lengths": input_lengths}

        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)

        self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				TFXLMForSequenceClassification(_SCREAMING_SNAKE_CASE	)

        lowercase__								=				{"input_ids": input_ids, "lengths": input_lengths}

        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)

        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				TFXLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_choices
        lowercase__								=				TFXLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE,					1	),					(1, self.num_choices, 1)	)
        lowercase__								=				tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE,					1	),					(1, self.num_choices, 1)	)
        lowercase__								=				tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE,					1	),					(1, self.num_choices, 1)	)
        lowercase__								=				{
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        lowercase__								=				model(_SCREAMING_SNAKE_CASE	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    
        )								=				config_and_inputs
        lowercase__								=				{
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "langs": token_type_ids,
            "lengths": input_lengths,
        }
        return config, inputs_dict






@require_tf
class  a__     ( _a       ,       _a       ,       unittest.TestCase						):
   snake_case_				=   (
       (
           TFXLMModel,
           TFXLMWithLMHeadModel,
           TFXLMForSequenceClassification,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForTokenClassification,
           TFXLMForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   snake_case_				=   (
       (TFXLMWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   snake_case_				=   (
       {
           "feature-extraction": TFXLMModel,
           "fill-mask": TFXLMWithLMHeadModel,
           "question-answering": TFXLMForQuestionAnsweringSimple,
           "text-classification": TFXLMForSequenceClassification,
           "text-generation": TFXLMWithLMHeadModel,
           "token-classification": TFXLMForTokenClassification,
           "zero-shot": TFXLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   snake_case_				=   False
   snake_case_				=   False


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast"	)
        ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

        return False


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				TFXLMModelTester(self	)
        lowercase__								=				ConfigTester(self,					config_class=_SCREAMING_SNAKE_CASE,					emb_dim=37	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_model(*_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_qa(*_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_sequence_classif(*_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_token_classification(*_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__								=				TFXLMModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
             self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class  a__     ( unittest.TestCase						):


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"	)
        lowercase__								=				tf.convert_to_tensor([[14, 447]],					dtype=tf.intaa	)  # the president
        lowercase__								=				[
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
        ]  # the president the president the president the president the president the president the president the president the president the president
        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
        lowercase__								=				model.generate(_SCREAMING_SNAKE_CASE,					do_sample=_SCREAMING_SNAKE_CASE	)
        self.assertListEqual(output_ids[0].numpy().tolist(),					_SCREAMING_SNAKE_CASE	)





"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					WavLMForSequenceClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['projector.weight']
   __lowerCamelCase    		=					downstream_dict['projector.bias']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.bias']
   return model



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['connector.weight']
   __lowerCamelCase    		=					downstream_dict['connector.bias']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      __lowerCamelCase    		=					downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      __lowerCamelCase    		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
   __lowerCamelCase    		=					downstream_dict['objective.W']
   return model



@torch.no_grad()
def       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					torch.load(lowercase_      , map_location='cpu'						)

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

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

   if hf_config.use_weighted_layer_sum:
      __lowerCamelCase    		=					checkpoint['Featurizer']['weights']

   hf_feature_extractor.save_pretrained(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   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.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






