'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self							,					_a							,					_a=13							,					_a=7							,					_a=True							,					_a=True							,					_a=True							,					_a=True							,					_a=99							,					_a=32							,					_a=18							,					_a=43							,					_a=5							,					_a=4							,					_a=37							,					_a="gelu"							,					_a=0.1							,					_a=0.1							,					_a=512							,					_a=16							,					_a=2							,					_a=0.02							,					_a=3							,					_a=4							,					_a=None							,					)     ->  Tuple:
        lowercase_       :							Union[str, Any]  					=       parent
        lowercase_       :							str  					=       batch_size
        lowercase_       :							Any  					=       seq_length
        lowercase_       :							Dict  					=       is_training
        lowercase_       :							Optional[int]  					=       use_input_mask
        lowercase_       :							Optional[Any]  					=       use_token_type_ids
        lowercase_       :							str  					=       use_labels
        lowercase_       :							Optional[Any]  					=       vocab_size
        lowercase_       :							Union[str, Any]  					=       hidden_size
        lowercase_       :							int  					=       input_embedding_size
        lowercase_       :							List[str]  					=       output_embedding_size
        lowercase_       :							Union[str, Any]  					=       num_hidden_layers
        lowercase_       :							Union[str, Any]  					=       num_attention_heads
        lowercase_       :							Dict  					=       intermediate_size
        lowercase_       :							Optional[int]  					=       hidden_act
        lowercase_       :							Union[str, Any]  					=       hidden_dropout_prob
        lowercase_       :							Any  					=       attention_probs_dropout_prob
        lowercase_       :							Dict  					=       max_position_embeddings
        lowercase_       :							List[str]  					=       type_vocab_size
        lowercase_       :							Optional[Any]  					=       type_sequence_label_size
        lowercase_       :							List[Any]  					=       initializer_range
        lowercase_       :							Optional[int]  					=       num_labels
        lowercase_       :							Optional[int]  					=       num_choices
        lowercase_       :							int  					=       scope
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							str  					=       ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

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

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

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

        lowercase_       :							Dict  					=       RemBertConfig(
            vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					input_embedding_size=self.input_embedding_size							,					output_embedding_size=self.output_embedding_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					type_vocab_size=self.type_vocab_size							,					is_decoder=_a							,					initializer_range=self.initializer_range							,					)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        (
            (
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,
        )       :							List[Any]  					=       self.prepare_config_and_inputs()

        lowercase_       :							Optional[Any]  					=       True
        lowercase_       :							Union[str, Any]  					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
        lowercase_       :							Optional[Any]  					=       ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2					)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  Any:
        lowercase_       :							str  					=       RemBertModel(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Optional[Any]  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a					)
        lowercase_       :							Union[str, Any]  					=       model(_a							,					token_type_ids=_a					)
        lowercase_       :							str  					=       model(_a					)
        self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					)     ->  List[Any]:
        lowercase_       :							Tuple  					=       True
        lowercase_       :							int  					=       RemBertModel(_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Optional[Any]  					=       model(
            _a							,					attention_mask=_a							,					token_type_ids=_a							,					encoder_hidden_states=_a							,					encoder_attention_mask=_a							,					)
        lowercase_       :							Tuple  					=       model(
            _a							,					attention_mask=_a							,					token_type_ids=_a							,					encoder_hidden_states=_a							,					)
        lowercase_       :							List[Any]  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a					)
        self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					)     ->  List[str]:
        lowercase_       :							Union[str, Any]  					=       RemBertForCausalLM(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Dict  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  int:
        lowercase_       :							Union[str, Any]  					=       RemBertForMaskedLM(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							int  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					)     ->  Tuple:
        lowercase_       :							Union[str, Any]  					=       True
        lowercase_       :							Any  					=       True
        lowercase_       :							Union[str, Any]  					=       RemBertForCausalLM(config=_a					)
        model.to(_a					)
        model.eval()

        # first forward pass
        lowercase_       :							List[str]  					=       model(
            _a							,					attention_mask=_a							,					encoder_hidden_states=_a							,					encoder_attention_mask=_a							,					use_cache=_a							,					)
        lowercase_       :							str  					=       outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        lowercase_       :							Tuple  					=       ids_tensor((self.batch_size, 3)							,					config.vocab_size					)
        lowercase_       :							List[str]  					=       ids_tensor((self.batch_size, 3)							,					vocab_size=2					)

        # append to next input_ids and
        lowercase_       :							int  					=       torch.cat([input_ids, next_tokens]							,					dim=-1					)
        lowercase_       :							str  					=       torch.cat([input_mask, next_mask]							,					dim=-1					)

        lowercase_       :							Any  					=       model(
            _a							,					attention_mask=_a							,					encoder_hidden_states=_a							,					encoder_attention_mask=_a							,					output_hidden_states=_a							,					)['hidden_states'][0]
        lowercase_       :							Optional[Any]  					=       model(
            _a							,					attention_mask=_a							,					encoder_hidden_states=_a							,					encoder_attention_mask=_a							,					past_key_values=_a							,					output_hidden_states=_a							,					)['hidden_states'][0]

        # select random slice
        lowercase_       :							Tuple  					=       ids_tensor((1,)							,					output_from_past.shape[-1]					).item()
        lowercase_       :							Optional[int]  					=       output_from_no_past[:, -3:, random_slice_idx].detach()
        lowercase_       :							Optional[int]  					=       output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_a							,					_a							,					atol=1e-3					)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  Tuple:
        lowercase_       :							List[Any]  					=       RemBertForQuestionAnswering(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Optional[int]  					=       model(
            _a							,					attention_mask=_a							,					token_type_ids=_a							,					start_positions=_a							,					end_positions=_a							,					)
        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 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  int:
        lowercase_       :							str  					=       self.num_labels
        lowercase_       :							Tuple  					=       RemBertForSequenceClassification(_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							int  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  Union[str, Any]:
        lowercase_       :							Dict  					=       self.num_labels
        lowercase_       :							Optional[Any]  					=       RemBertForTokenClassification(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Any  					=       model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a					)     ->  Tuple:
        lowercase_       :							Any  					=       self.num_choices
        lowercase_       :							List[str]  					=       RemBertForMultipleChoice(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							Any  					=       input_ids.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase_       :							Tuple  					=       token_type_ids.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase_       :							List[Any]  					=       input_mask.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase_       :							Optional[Any]  					=       model(
            _a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a							,					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)					)





   def 		_lowerCamelCase			(self					)     ->  Optional[int]:
        lowercase_       :							List[Any]  					=       self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,
        )       :							str  					=       config_and_inputs
        lowercase_       :							Optional[int]  					=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_torch
class    UpperCAmelCase__	(							_snake_case       ,  _snake_case       ,  unittest.TestCase      ):






   """simple docstring"""
   A				:							Optional[int]			     =     (
       (
           RemBertModel,
           RemBertForMaskedLM,
           RemBertForCausalLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A				:							Optional[int]			     =     (RemBertForCausalLM,) if is_torch_available() else ()
   A				:							Optional[Any]			     =     (
       {
           '''feature-extraction''': RemBertModel,
           '''fill-mask''': RemBertForMaskedLM,
           '''question-answering''': RemBertForQuestionAnswering,
           '''text-classification''': RemBertForSequenceClassification,
           '''text-generation''': RemBertForCausalLM,
           '''token-classification''': RemBertForTokenClassification,
           '''zero-shot''': RemBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   def 		_lowerCamelCase			(self					)     ->  Optional[int]:
        lowercase_       :							int  					=       RemBertModelTester(self					)
        lowercase_       :							List[str]  					=       ConfigTester(self							,					config_class=_a							,					hidden_size=37					)
   def 		_lowerCamelCase			(self					)     ->  Any:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        lowercase_       :							Dict  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_       :							List[Any]  					=       self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             lowercase_       :							int  					=       type
             self.model_tester.create_and_check_model(*_a					)
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        lowercase_       :							Union[str, Any]  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_a					)
   def 		_lowerCamelCase			(self					)     ->  str:
        lowercase_       :							List[str]  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        lowercase_       :							Optional[Any]  					=       self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Optional[int]:
        lowercase_       :							int  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							str  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Dict:
        lowercase_       :							Union[str, Any]  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_a					)
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							str  					=       self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_a					)
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,(
            lowercase_
        )					,
        )       :							Tuple  					=       self.model_tester.prepare_config_and_inputs_for_decoder()

        lowercase_       :							Optional[Any]  					=       None

        self.model_tester.create_and_check_model_as_decoder(
            _a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					_a							,					)





   @slow
   def 		_lowerCamelCase			(self					)     ->  str:
        for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :							Union[str, Any]  					=       RemBertModel.from_pretrained(_a					)
             self.assertIsNotNone(_a					)



@require_torch
class    UpperCAmelCase__	(							unittest.TestCase      ):






   """simple docstring"""
   @slow
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        # Test exact values at the last hidden layer
        lowercase_       :							Optional[Any]  					=       RemBertModel.from_pretrained('google/rembert'					)
        lowercase_       :							Tuple  					=       torch.tensor([[312, 56_498, 313, 2_125, 313]]					)
        lowercase_       :							List[Any]  					=       torch.tensor([[0, 0, 0, 1, 1]]					)
        with torch.no_grad():
             lowercase_       :							Optional[int]  					=       model(_a							,					token_type_ids=_a							,					output_hidden_states=_a					)

        lowercase_       :							List[Any]  					=       1_152

        lowercase_       :							Dict  					=       torch.Size((1, 5, hidden_size)					)
        self.assertEqual(output['last_hidden_state'].shape							,					_a					)

        lowercase_       :							Union[str, Any]  					=       torch.tensor(
            [
                [
                    [0.07_54, -0.20_22, 0.19_04],
                    [-0.33_54, -0.36_92, -0.47_91],
                    [-0.23_14, -0.67_29, -0.07_49],
                    [-0.03_96, -0.31_05, -0.42_34],
                    [-0.15_71, -0.05_25, 0.53_53],
                ]
            ]					)

        # Running on the original tf implementation gives slightly different results here.
        # Not clear why this variations is present
        # TODO: Find reason for discrepancy
        # expected_original_implementation = [[
        #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
        #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
        #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
        #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
        #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
        # ]]

        self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]							,					_a							,					atol=1e-4					)					)





'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A										= logging.get_logger(__name__)

_A										= ['UtteranceLevel']

@torch.no_grad()
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location='cpu'				)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''				)

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


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)




