

'''simple docstring'''

import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertModel,
       )


class 	lowerCAmelCase  (   unittest.TestCase ):







       def __init__(				self      :			Optional[int]    ,   __lowercase      :			List[str]    ,   __lowercase      :			int=13    ,   __lowercase      :			Union[str, Any]=7    ,   __lowercase      :			List[Any]=True    ,   __lowercase      :			Dict=True    ,   __lowercase      :			Optional[int]=True    ,   __lowercase      :			Optional[int]=True    ,   __lowercase      :			str=99    ,   __lowercase      :			Any=32    ,   __lowercase      :			Tuple=5    ,   __lowercase      :			List[str]=4    ,   __lowercase      :			List[Any]=37    ,   __lowercase      :			Dict="gelu"    ,   __lowercase      :			str=0.1    ,   __lowercase      :			str=0.1    ,   __lowercase      :			List[str]=512    ,   __lowercase      :			Tuple=16    ,   __lowercase      :			Optional[Any]=2    ,   __lowercase      :			Dict=0.0_2    ,   __lowercase      :			Union[str, Any]=4    ,   ):







            """simple docstring"""







            __lowercase      		=parent
            __lowercase      		=batch_size
            __lowercase      		=seq_length
            __lowercase      		=is_training
            __lowercase      		=use_attention_mask
            __lowercase      		=use_token_type_ids
            __lowercase      		=use_labels
            __lowercase      		=vocab_size
            __lowercase      		=hidden_size
            __lowercase      		=num_hidden_layers
            __lowercase      		=num_attention_heads
            __lowercase      		=intermediate_size
            __lowercase      		=hidden_act
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=max_position_embeddings
            __lowercase      		=type_vocab_size
            __lowercase      		=type_sequence_label_size
            __lowercase      		=initializer_range
            __lowercase      		=num_choices







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size   )

            __lowercase      		=None
            if self.use_attention_mask:
                 __lowercase      		=random_attention_mask([self.batch_size, self.seq_length]   )

            __lowercase      		=DistilBertConfig(
                vocab_size=self.vocab_size    ,   dim=self.hidden_size    ,   n_layers=self.num_hidden_layers    ,   n_heads=self.num_attention_heads    ,   hidden_dim=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   dropout=self.hidden_dropout_prob    ,   attention_dropout=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   initializer_range=self.initializer_range    ,   tie_weights_=_a    ,   )

            return config, input_ids, attention_mask







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=self.prepare_config_and_inputs()
            __lowercase      		=config_and_inputs
            __lowercase      		={"input_ids": input_ids, "attention_mask": attention_mask}
            return config, inputs_dict






@require_flax
class 	lowerCAmelCase  (   a_ ,      unittest.TestCase ):
       lowerCAmelCase_												=			(
           (
               FlaxDistilBertModel,
               FlaxDistilBertForMaskedLM,
               FlaxDistilBertForMultipleChoice,
               FlaxDistilBertForQuestionAnswering,
               FlaxDistilBertForSequenceClassification,
               FlaxDistilBertForTokenClassification,
               FlaxDistilBertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=FlaxDistilBertModelTester(self   )







       @slow
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowercase      		=model_class_name.from_pretrained('distilbert-base-uncased'   )
                 __lowercase      		=model(np.ones((1, 1)   )   )
                 self.assertIsNotNone(_a   )






@require_flax
class 	lowerCAmelCase  (   unittest.TestCase ):







       @slow
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'   )
            __lowercase      		=np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
            __lowercase      		=np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
            __lowercase      		=model(_a    ,   attention_mask=_a   )[0]
            __lowercase      		=(1, 11, 768)
            self.assertEqual(output.shape    ,   _a   )
            __lowercase      		=np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]   )

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




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

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

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					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	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			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     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


