"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							[
         '''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    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case		,	__snake_case				=							emb.weight.shape
     __snake_case				=							nn.Linear(snake_case,      snake_case,      bias=snake_case)
     __snake_case				=							emb.weight.data
     return lin_layer





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							torch.load(snake_case,      map_location='''cpu''')
     __snake_case				=							mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
     __snake_case				=							mam_aaa['''model''']
     remove_ignore_keys_(snake_case)
     __snake_case				=							state_dict['''encoder.embed_tokens.weight'''].shape[0]

     __snake_case				=							MaMaaaConfig(
         vocab_size=snake_case,      max_position_embeddings=10_24,      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''',      )

     __snake_case				=							state_dict['''decoder.embed_tokens.weight''']
     __snake_case				=							MaMaaaForConditionalGeneration(snake_case)
     model.model.load_state_dict(snake_case,      strict=snake_case)
     __snake_case				=							make_linear_from_emb(model.model.shared)

     return model


if __name__ == "__main__":
       __lowercase		:		int														=							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.")
       __lowercase		:		str														=							parser.parse_args()
       __lowercase		:		Union[str, Any]														=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
       model.save_pretrained(args.pytorch_dump_folder_path)
"""simple docstring"""



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     List[Any]=13	,      A_							:     Dict=7	,      A_							:     Optional[int]=True	,      A_							:     Optional[int]=True	,      A_							:     List[Any]=True	,      A_							:     Union[str, Any]=True	,      A_							:     str=99	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=5	,      A_							:     Union[str, Any]=4	,      A_							:     Dict=37	,      A_							:     Any="gelu"	,      A_							:     Tuple=0.1	,      A_							:     str=0.1	,      A_							:     List[str]=512	,      A_							:     List[str]=16	,      A_							:     Optional[int]=2	,      A_							:     Optional[Any]=0.02	,      A_							:     str=4	,      )    ->       Any:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_attention_mask
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_choices
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							None
         if self.use_attention_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				=							RoFormerConfig(
             vocab_size=self.vocab_size	,      hidden_size=self.hidden_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, attention_mask







    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __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''': attention_mask}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					True

    UpperCamelCase_	:					List[str]							=					(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerModelTester(self    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''	,      from_pt=A_    )
              __snake_case				=							model(np.ones((1, 1)    )    )
              self.assertIsNotNone(A_    )






@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         __snake_case				=							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         __snake_case				=							model(A_    )[0]

         __snake_case				=							50_000

         __snake_case				=							(1, 6, vocab_size)
         self.assertEqual(output.shape	,      A_    )

         __snake_case				=							jnp.array(
             [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]    )

         self.assertTrue(jnp.allclose(output[:, :3, :3]	,      A_	,      atol=1E-4    )    )