



"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class        _UpperCAmelCase       :
 _lowerCAmelCase   :			Dict							=		MBartConfig
 _lowerCAmelCase   :			Dict							=		{}
 _lowerCAmelCase   :			str							=		"""gelu"""

 def __init__(     self		:       List[Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Optional[int]=13  ,     lowercase_		:       int=7  ,     lowercase_		:       Tuple=True  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Tuple=99  ,     lowercase_		:       Optional[Any]=32  ,     lowercase_		:       Tuple=2  ,     lowercase_		:       Tuple=4  ,     lowercase_		:       Any=37  ,     lowercase_		:       List[Any]=0.1  ,     lowercase_		:       List[Any]=0.1  ,     lowercase_		:       Union[str, Any]=20  ,     lowercase_		:       Dict=2  ,     lowercase_		:       Optional[int]=1  ,     lowercase_		:       Dict=0  ,     ):
     snake_case_ :     Optional[Any]				=	parent
     snake_case_ :     Tuple				=	batch_size
     snake_case_ :     Tuple				=	seq_length
     snake_case_ :     List[str]				=	is_training
     snake_case_ :     int				=	use_labels
     snake_case_ :     Any				=	vocab_size
     snake_case_ :     Optional[int]				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Optional[int]				=	num_attention_heads
     snake_case_ :     str				=	intermediate_size
     snake_case_ :     Any				=	hidden_dropout_prob
     snake_case_ :     Optional[Any]				=	attention_probs_dropout_prob
     snake_case_ :     Dict				=	max_position_embeddings
     snake_case_ :     int				=	eos_token_id
     snake_case_ :     Tuple				=	pad_token_id
     snake_case_ :     Any				=	bos_token_id

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Dict				=	ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size				)
     snake_case_ :     Any				=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)  ,     1				)
     snake_case_ :     Any				=	tf.concat([input_ids, eos_tensor]  ,     axis=1				)

     snake_case_ :     Optional[Any]				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

     snake_case_ :     str				=	self.config_cls(
         vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     encoder_layers=self.num_hidden_layers  ,     decoder_layers=self.num_hidden_layers  ,     encoder_attention_heads=self.num_attention_heads  ,     decoder_attention_heads=self.num_attention_heads  ,     encoder_ffn_dim=self.intermediate_size  ,     decoder_ffn_dim=self.intermediate_size  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     max_position_embeddings=self.max_position_embeddings  ,     eos_token_ids=[2]  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     decoder_start_token_id=self.pad_token_id  ,     **self.config_updates  ,     )
     snake_case_ :     Optional[int]				=	prepare_mbart_inputs_dict(lowercase_  ,     lowercase_  ,     lowercase_				)
     return config, inputs_dict






 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       int  ,     lowercase_		:       Dict				):
     snake_case_ :     List[Any]				=	TFMBartModel(config=lowercase_				).get_decoder()
     snake_case_ :     List[str]				=	inputs_dict['''input_ids''']

     snake_case_ :     Union[str, Any]				=	input_ids[:1, :]
     snake_case_ :     Tuple				=	inputs_dict['''attention_mask'''][:1, :]
     snake_case_ :     List[Any]				=	inputs_dict['''head_mask''']
     snake_case_ :     List[str]				=	1

     # first forward pass
     snake_case_ :     str				=	model(lowercase_  ,     attention_mask=lowercase_  ,     head_mask=lowercase_  ,     use_cache=lowercase_				)

     snake_case_,	snake_case_ :     List[str]				=	outputs.to_tuple()
     snake_case_ :     List[Any]				=	past_key_values[1]





def __lowercase			(							_a		,			_a		,			_a		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			):
    if attention_mask is None:
        snake_case_ :     int				=	tf.cast(tf.math.not_equal(_a		,			config.pad_token_id    )		,			tf.inta    )
    if decoder_attention_mask is None:
        snake_case_ :     List[str]				=	tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape		,			dtype=tf.inta    ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,			config.pad_token_id    )		,			tf.inta    ),
            ]		,			axis=-1		,			)
    if head_mask is None:
        snake_case_ :     Optional[Any]				=	tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        snake_case_ :     Dict				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        snake_case_ :     Any				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }




@require_tf
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			List[str]							=		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
 _lowerCAmelCase   :			Tuple							=		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
 _lowerCAmelCase   :			Union[str, Any]							=		(
     {
         """conversational""": TFMBartForConditionalGeneration,
         """feature-extraction""": TFMBartModel,
         """summarization""": TFMBartForConditionalGeneration,
         """text2text-generation""": TFMBartForConditionalGeneration,
         """translation""": TFMBartForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase   :			Any							=		True
 _lowerCAmelCase   :			Dict							=		False
 _lowerCAmelCase   :			int							=		False

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       int  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Any  ,     lowercase_		:       str  ,     lowercase_		:       Any				):
     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
         # Exception encountered when calling layer '...'
         return True

     return False

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Any				=	TFMBartModelTester(self				)
     snake_case_ :     Optional[int]				=	ConfigTester(self  ,     config_class=lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     self.config_tester.run_common_tests()






 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Tuple				=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_				)





@require_sentencepiece
@require_tokenizers
@require_tf
class        _UpperCAmelCase       (			unittest.TestCase):
 _lowerCAmelCase   :			str							=		[
     """ UN Chief Says There Is No Military Solution in Syria""",
 ]
 _lowerCAmelCase   :			Dict							=		[
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
 ]
 _lowerCAmelCase   :			Any							=		"""facebook/mbart-large-en-ro"""

 @cached_property
 def 						_snake_case					(     self		:       Dict				):
     return AutoTokenizer.from_pretrained(self.model_name				)

 @cached_property
 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     str				=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
     return model

 def 						_snake_case					(     self		:       Dict  ,     **lowercase_		:       Dict				):
     snake_case_ :     List[Any]				=	self.translate_src_text(**lowercase_				)
     self.assertListEqual(self.expected_text  ,     lowercase_				)

 def 						_snake_case					(     self		:       List[Any]  ,     **lowercase_		:       Any				):
     snake_case_ :     str				=	self.tokenizer(self.src_text  ,     **lowercase_  ,     return_tensors='''tf'''				)
     snake_case_ :     Union[str, Any]				=	self.model.generate(
         model_inputs.input_ids  ,     attention_mask=model_inputs.attention_mask  ,     num_beams=2				)
     snake_case_ :     List[str]				=	self.tokenizer.batch_decode(lowercase_  ,     skip_special_tokens=lowercase_				)
     return generated_words






 @slow
 def 						_snake_case					(     self		:       Tuple				):
     self._assert_generated_batch_equal_expected()










"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





