
"""simple docstring"""



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					logging.get_logger()

          # the current default level is logging.WARNING
          UpperCAmelCase__	:      List[str]     							=					logging.get_verbosity()

          logging.set_verbosity_error()
          self.assertEqual(logger.getEffectiveLevel()     ,	logging.get_verbosity()      )

          logging.set_verbosity_warning()
          self.assertEqual(logger.getEffectiveLevel()     ,	logging.get_verbosity()      )

          logging.set_verbosity_info()
          self.assertEqual(logger.getEffectiveLevel()     ,	logging.get_verbosity()      )

          logging.set_verbosity_debug()
          self.assertEqual(logger.getEffectiveLevel()     ,	logging.get_verbosity()      )

          # restore to the original level
          logging.set_verbosity(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					logging.get_verbosity()

          UpperCAmelCase__	:      Optional[int]     							=					logging.get_logger('transformers.models.bart.tokenization_bart'      )
          UpperCAmelCase__	:      Dict     							=					'Testing 1, 2, 3'

          # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
          if level_origin <= logging.WARNING:
              with CaptureLogger(UpperCamelCase_      ) as cl:
                  logger.warning(UpperCamelCase_      )
              self.assertEqual(cl.out     ,	msg + '\n'      )

          # this is setting the level for all of `transformers.*` loggers
          logging.set_verbosity_error()

          # should not be able to log warnings
          with CaptureLogger(UpperCamelCase_      ) as cl:
              logger.warning(UpperCamelCase_      )
          self.assertEqual(cl.out     ,	''      )

          # should be able to log warnings again
          logging.set_verbosity_warning()
          with CaptureLogger(UpperCamelCase_      ) as cl:
              logger.warning(UpperCamelCase_      )
          self.assertEqual(cl.out     ,	msg + '\n'      )

          # restore to the original level
          logging.set_verbosity(UpperCamelCase_      )



      @mockenv(TRANSFORMERS_VERBOSITY='error'      )
      def  __snake_case							(   self      ):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          # this action activates the env var
          UpperCAmelCase__	:      Optional[Any]     							=					logging.get_logger('transformers.models.bart.tokenization_bart'      )

          UpperCAmelCase__	:      Optional[int]     							=					os.getenv('TRANSFORMERS_VERBOSITY'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					logging.log_levels[env_level_str]

          UpperCAmelCase__	:      int     							=					logging.get_verbosity()
          self.assertEqual(
              UpperCamelCase_     ,	UpperCamelCase_     ,	F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''     ,	)

          # restore to the original level
          UpperCAmelCase__	:      List[Any]     							=					''
          transformers.utils.logging._reset_library_root_logger()



      @mockenv(TRANSFORMERS_VERBOSITY='super-error'      )
      def  __snake_case							(   self      ):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          UpperCAmelCase__	:      int     							=					logging.logging.getLogger()
          with CaptureLogger(UpperCamelCase_      ) as cl:
              # this action activates the env var
              logging.get_logger('transformers.models.bart.tokenization_bart'      )
          self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'     ,	cl.out      )

          # no need to restore as nothing was changed



      def  __snake_case							(   self      ):
          # testing `logger.warning_advice()`
          transformers.utils.logging._reset_library_root_logger()

          UpperCAmelCase__	:      Union[str, Any]     							=					logging.get_logger('transformers.models.bart.tokenization_bart'      )
          UpperCAmelCase__	:      Union[str, Any]     							=					'Testing 1, 2, 3'

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'      ):
              # nothing should be logged as env var disables this method
              with CaptureLogger(UpperCamelCase_      ) as cl:
                  logger.warning_advice(UpperCamelCase_      )
              self.assertEqual(cl.out     ,	''      )

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''      ):
              # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
              with CaptureLogger(UpperCamelCase_      ) as cl:
                  logger.warning_advice(UpperCamelCase_      )
              self.assertEqual(cl.out     ,	msg + '\n'      )




def 					lowerCamelCase   (     ):
    disable_progress_bar()
    assert are_progress_bars_disabled()

    enable_progress_bar()
    assert not are_progress_bars_disabled()



"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,encoder_layers=args.encoder_layers    ,decoder_layers=args.decoder_layers    ,encoder_attention_heads=args.encoder_attention_heads    ,decoder_attention_heads=args.decoder_attention_heads    ,encoder_ffn_dim=args.encoder_ffn_embed_dim    ,decoder_ffn_dim=args.decoder_ffn_embed_dim    ,d_model=args.encoder_embed_dim    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

