




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormModel,
       )




class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



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







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size     )

           lowerCamelCase   :      str			 =					None
           if self.use_attention_mask:
                  lowerCamelCase   :      Optional[int]			 =					random_attention_mask([self.batch_size, self.seq_length]     )

           lowerCamelCase   :      Optional[Any]			 =					None
           if self.use_token_type_ids:
                  lowerCamelCase   :      int			 =					ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size     )

           lowerCamelCase   :      Optional[int]			 =					RobertaPreLayerNormConfig(
               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=UpperCamelCase__     ,  initializer_range=self.initializer_range     ,  )

           return config, input_ids, token_type_ids, attention_mask







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					self.prepare_config_and_inputs()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      int			 =					config_and_inputs
           lowerCamelCase   :      Any			 =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
           return config, inputs_dict







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      str			 =					self.prepare_config_and_inputs()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					config_and_inputs

           lowerCamelCase   :      List[Any]			 =					True
           lowerCamelCase   :      str			 =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
           lowerCamelCase   :      int			 =					ids_tensor([self.batch_size, self.seq_length]     ,  vocab_size=2     )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      True

    lowerCamelCase_      :	List[Any]	     =      (
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Optional[int]			 =					FlaxRobertaPreLayerNormModelTester(self     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           for model_class_name in self.all_model_classes:
                  lowerCamelCase   :      Dict			 =					model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,  from_pt=UpperCamelCase__     )
                  lowerCamelCase   :      Dict			 =					model(np.ones((1, 1)     )     )
                  self.assertIsNotNone(UpperCamelCase__     )




@require_flax
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @slow
    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Optional[int]			 =					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,  from_pt=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     ,  dtype=jnp.intaa     )
           lowerCamelCase   :      List[str]			 =					model(UpperCamelCase__     )[0]
           lowerCamelCase   :      List[str]			 =					[1, 11, 5_0265]
           self.assertEqual(list(output.shape     )     ,  UpperCamelCase__     )
           # compare the actual values for a slice.
           lowerCamelCase   :      Dict			 =					np.array(
               [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,  dtype=np.floataa     )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,  UpperCamelCase__     ,  atol=1e-4     )     )







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[str]			 =					FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,  from_pt=UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     ,  dtype=jnp.intaa     )
           lowerCamelCase   :      List[str]			 =					model(UpperCamelCase__     )[0]
           # compare the actual values for a slice.
           lowerCamelCase   :      Dict			 =					np.array(
               [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,  dtype=np.floataa     )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,  UpperCamelCase__     ,  atol=1e-4     )     )













import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



