"""simple docstring"""



from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel

@require_tf
class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 a_       :	Optional[int]  			=MarianConfig
 a_       :	Optional[Any]  			={}
 a_       :	Optional[int]  			="gelu"


 def __init__(   self		:      int					,   _snake_case		:      Union[str, Any]					,   _snake_case		:      Dict=13					,   _snake_case		:      Optional[int]=7					,   _snake_case		:      List[str]=True					,   _snake_case		:      Union[str, Any]=False					,   _snake_case		:      Optional[int]=99					,   _snake_case		:      Dict=32					,   _snake_case		:      Union[str, Any]=2					,   _snake_case		:      List[Any]=4					,   _snake_case		:      Optional[int]=37					,   _snake_case		:      Optional[int]=0.1					,   _snake_case		:      Tuple=0.1					,   _snake_case		:      Optional[int]=20					,   _snake_case		:      str=2					,   _snake_case		:      List[Any]=1					,   _snake_case		:      List[str]=0					,   )				->			Tuple:




    '''simple docstring'''






    a__     				=		parent
    a__     				=		batch_size
    a__     				=		seq_length
    a__     				=		is_training
    a__     				=		use_labels
    a__     				=		vocab_size
    a__     				=		hidden_size
    a__     				=		num_hidden_layers
    a__     				=		num_attention_heads
    a__     				=		intermediate_size

    a__     				=		hidden_dropout_prob
    a__     				=		attention_probs_dropout_prob
    a__     				=		max_position_embeddings
    a__     				=		eos_token_id
    a__     				=		pad_token_id
    a__     				=		bos_token_id


 def        _lowerCAmelCase				(   self		:      int				)				->			Dict:




    '''simple docstring'''






    a__     				=		ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size				)
    a__     				=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)					,   1				)
    a__     				=		tf.concat([input_ids, eos_tensor]					,   axis=1				)

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

    a__     				=		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					,   )
    a__     				=		prepare_marian_inputs_dict(_snake_case					,   _snake_case					,   _snake_case				)
    return config, inputs_dict



 def        _lowerCAmelCase				(   self		:      List[str]					,   _snake_case		:      List[Any]					,   _snake_case		:      Optional[Any]				)				->			List[str]:




    '''simple docstring'''






    a__     				=		TFMarianModel(config=_snake_case				).get_decoder()
    a__     				=		inputs_dict['input_ids']

    a__     				=		input_ids[:1, :]
    a__     				=		inputs_dict['attention_mask'][:1, :]
    a__     				=		inputs_dict['head_mask']
    a__     				=		1

    # first forward pass
    a__     				=		model(_snake_case					,   attention_mask=_snake_case					,   head_mask=_snake_case					,   use_cache=_snake_case				)

    a__				,       a__     				=		outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    a__     				=		ids_tensor((self.batch_size, 3)					,   config.vocab_size				)
    a__     				=		tf.cast(ids_tensor((self.batch_size, 3)					,   2				)					,   tf.inta				)

    # append to next input_ids and
    a__     				=		tf.concat([input_ids, next_tokens]					,   axis=-1				)
    a__     				=		tf.concat([attention_mask, next_attn_mask]					,   axis=-1				)

    a__     				=		model(_snake_case					,   attention_mask=_snake_case				)[0]
    a__     				=		model(_snake_case					,   attention_mask=_snake_case					,   past_key_values=_snake_case				)[0]

    self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]				)

    # select random slice
    a__     				=		int(ids_tensor((1,)					,   output_from_past.shape[-1]				)				)
    a__     				=		output_from_no_past[:, -3:, random_slice_idx]
    a__     				=		output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(_snake_case					,   _snake_case					,   rtol=1E-3				)





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,)							->     Optional[Any]:


   '''simple docstring'''


   if attention_mask is None:
      a__     				=		tf.cast(tf.math.not_equal(UpperCAmelCase__,config.pad_token_id					),tf.inta					)
   if decoder_attention_mask is None:
      a__     				=		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:
      a__     				=		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      a__     				=		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      a__     				=		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 SCREAMING_SNAKE_CASE     (    a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	Dict  			=(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 a_       :	Optional[int]  			=(TFMarianMTModel,) if is_tf_available() else ()
 a_       :	List[str]  			=(
     {
         "conversational": TFMarianMTModel,
         "feature-extraction": TFMarianModel,
         "summarization": TFMarianMTModel,
         "text2text-generation": TFMarianMTModel,
         "translation": TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 a_       :	List[Any]  			=True
 a_       :	int  			=False
 a_       :	List[Any]  			=False


 def        _lowerCAmelCase				(   self		:      int				)				->			Dict:




    '''simple docstring'''






    a__     				=		TFMarianModelTester(self				)
    a__     				=		ConfigTester(self					,   config_class=_snake_case				)


 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[str]:




    '''simple docstring'''






    self.config_tester.run_common_tests()



 def        _lowerCAmelCase				(   self		:      int				)				->			List[str]:




    '''simple docstring'''






    a__     				=		self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_snake_case				)

@require_tf
class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 a_       :	Optional[Any]  			=1_0_0_0  # show more chars for failing integration tests


 @classmethod
 def        _lowerCAmelCase				(   cls		:      Union[str, Any]				)				->			None:




    '''simple docstring'''






    a__     				=		F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
    return cls


 @cached_property
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			MarianTokenizer:




    '''simple docstring'''






    return AutoTokenizer.from_pretrained(self.model_name				)


 @property
 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			int:




    '''simple docstring'''






    return self.tokenizer.eos_token_id


 @cached_property
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			List[Any]:




    '''simple docstring'''






    warnings.simplefilter('error'				)
    a__     				=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
    assert isinstance(_snake_case					,   _snake_case				)
    a__     				=		model.config
    self.assertListEqual(c.bad_words_ids					,   [[c.pad_token_id]]				)
    self.assertEqual(c.max_length					,   512				)
    self.assertEqual(c.decoder_start_token_id					,   c.pad_token_id				)
    return model


 def        _lowerCAmelCase				(   self		:      Dict					,   **_snake_case		:      Optional[Any]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		self.translate_src_text(**_snake_case				)
    self.assertListEqual(self.expected_text					,   _snake_case				)



 def        _lowerCAmelCase				(   self		:      List[Any]					,   **_snake_case		:      Optional[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		self.tokenizer(self.src_text					,   **_snake_case					,   padding=_snake_case					,   return_tensors='tf'				)
    a__     				=		self.model.generate(
        model_inputs.input_ids					,   attention_mask=model_inputs.attention_mask					,   num_beams=2					,   max_length=128				)
    a__     				=		self.tokenizer.batch_decode(generated_ids.numpy()					,   skip_special_tokens=_snake_case				)
    return generated_words

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Optional[int]  			="mt"
 a_       :	Tuple  			="en"
 a_       :	Dict  			=["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
 a_       :	Optional[Any]  			=["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			int:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Dict  			="en"
 a_       :	Optional[int]  			="zh"
 a_       :	Dict  			=["My name is Wolfgang and I live in Berlin"]
 a_       :	Union[str, Any]  			=["我叫沃尔夫冈 我住在柏林"]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Any:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Optional[int]  			="en"
 a_       :	Tuple  			="ROMANCE"
 a_       :	Tuple  			=[
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 a_       :	int  			=[
     "Ne passez pas autant de temps à regarder la télé.",
     "A sua mensagem foi enviada.",
     "Es dos años más viejo que yo.",
 ]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Optional[Any]:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()



 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		TranslationPipeline(self.model					,   self.tokenizer					,   framework='tf'				)
    a__     				=		pipeline(self.src_text				)
    self.assertEqual(self.expected_text					,   [x['translation_text'] for x in output]				)



"""simple docstring"""



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					"Hello world! cécé herlolip"





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		FairseqRobertaModel.from_pretrained(UpperCAmelCase__					)
   roberta.eval()  # disable dropout
   a__     				=		roberta.model.encoder.sentence_encoder
   a__     				=		RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,hidden_size=roberta.args.encoder_embed_dim,num_hidden_layers=roberta.args.encoder_layers,num_attention_heads=roberta.args.encoder_attention_heads,intermediate_size=roberta.args.encoder_ffn_embed_dim,max_position_embeddings=5_14,type_vocab_size=1,layer_norm_eps=1e-5,)
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
   print('Our BERT config:',UpperCAmelCase__					)

   a__     				=		RobertaForSequenceClassification(UpperCAmelCase__					) if classification_head else RobertaForMaskedLM(UpperCAmelCase__					)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   a__     				=		roberta_sent_encoder.embed_tokens.weight
   a__     				=		roberta_sent_encoder.embed_positions.weight
   a__     				=		torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.
   a__     				=		roberta_sent_encoder.emb_layer_norm.weight
   a__     				=		roberta_sent_encoder.emb_layer_norm.bias

   for i in range(config.num_hidden_layers					):
      # Encoder: start of layer
      a__     				=		model.roberta.encoder.layer[i]
      a__     				=		roberta_sent_encoder.layers[i]

      # self attention
      a__     				=		layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)					)
      )

      a__     				=		roberta_layer.self_attn.q_proj.weight
      a__     				=		roberta_layer.self_attn.q_proj.bias
      a__     				=		roberta_layer.self_attn.k_proj.weight
      a__     				=		roberta_layer.self_attn.k_proj.bias
      a__     				=		roberta_layer.self_attn.v_proj.weight
      a__     				=		roberta_layer.self_attn.v_proj.bias

      # self-attention output
      a__     				=		layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      a__     				=		roberta_layer.self_attn.out_proj.weight
      a__     				=		roberta_layer.self_attn.out_proj.bias
      a__     				=		roberta_layer.self_attn_layer_norm.weight
      a__     				=		roberta_layer.self_attn_layer_norm.bias

      # intermediate
      a__     				=		layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      a__     				=		roberta_layer.fca.weight
      a__     				=		roberta_layer.fca.bias

      # output
      a__     				=		layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      a__     				=		roberta_layer.fca.weight
      a__     				=		roberta_layer.fca.bias
      a__     				=		roberta_layer.final_layer_norm.weight
      a__     				=		roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'].dense.weight
      a__     				=		roberta.model.classification_heads['mnli'].dense.bias
      a__     				=		roberta.model.classification_heads['mnli'].out_proj.weight
      a__     				=		roberta.model.classification_heads['mnli'].out_proj.bias
   else:
      # LM Head
      a__     				=		roberta.model.encoder.lm_head.dense.weight
      a__     				=		roberta.model.encoder.lm_head.dense.bias
      a__     				=		roberta.model.encoder.lm_head.layer_norm.weight
      a__     				=		roberta.model.encoder.lm_head.layer_norm.bias
      a__     				=		roberta.model.encoder.lm_head.weight
      a__     				=		roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   a__     				=		roberta.encode(UpperCAmelCase__					).unsqueeze(0					)  # batch of size 1

   a__     				=		model(UpperCAmelCase__					)[0]
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase__					)					)
   else:
      a__     				=		roberta.model(UpperCAmelCase__					)[0]
   print(our_output.shape,their_output.shape					)
   a__     				=		torch.max(torch.abs(our_output - their_output					)					).item()
   print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
   a__     				=		torch.allclose(UpperCAmelCase__,UpperCAmelCase__,atol=1e-3					)
   print('Do both models output the same tensors?','🔥' if success else '💩'					)
   if not success:
      raise Exception('Something went wRoNg'					)

   pathlib.Path(UpperCAmelCase__					).mkdir(parents=UpperCAmelCase__,exist_ok=UpperCAmelCase__					)
   print(f'''Saving model to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--roberta_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."
     )
     parser.add_argument(
         "--classification_head", action="store_true", help="Whether to convert a final classification head."
     )
     __magic_name__    				=					parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )


