



"""simple docstring"""





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class       __lowerCamelCase	(   unittest.TestCase    ):
   __UpperCamelCase									=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __UpperCamelCase									=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      if isinstance(model.config       ,	lowerCamelCase				):
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		list(tokenizer.lang_code_to_id.keys()				)[:2]
         _lowerCAmelCase					     =		TranslationPipeline(model=lowerCamelCase       ,	tokenizer=lowerCamelCase       ,	src_lang=lowerCamelCase       ,	tgt_lang=lowerCamelCase				)
      else:
         _lowerCAmelCase					     =		TranslationPipeline(model=lowerCamelCase       ,	tokenizer=lowerCamelCase				)
      return translator, ["Some string", "Some other text"]







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		translator("""Some string"""				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": ANY(lowerCamelCase				)}]				)

      _lowerCAmelCase					     =		translator(["""Some string"""]				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": ANY(lowerCamelCase				)}]				)

      _lowerCAmelCase					     =		translator(["""Some string""", """other string"""]				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": ANY(lowerCamelCase				)}, {"""translation_text""": ANY(lowerCamelCase				)}]				)







   @require_torch
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		pipeline("""translation_en_to_ro"""       ,	model="""patrickvonplaten/t5-tiny-random"""       ,	framework="""pt"""				)
      _lowerCAmelCase					     =		translator("""This is a test string"""       ,	max_length=20				)
      self.assertEqual(
          lowerCamelCase       ,	[
              {
                  """translation_text""": (
                      """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                      """ Beide Beide"""
                  )
              }
          ]       ,	)







   @require_tf
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		pipeline("""translation_en_to_ro"""       ,	model="""patrickvonplaten/t5-tiny-random"""       ,	framework="""tf"""				)
      _lowerCAmelCase					     =		translator("""This is a test string"""       ,	max_length=20				)
      self.assertEqual(
          lowerCamelCase       ,	[
              {
                  """translation_text""": (
                      """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                      """ Beide Beide"""
                  )
              }
          ]       ,	)







   @require_torch
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		pipeline("""translation_en_to_de"""       ,	model="""patrickvonplaten/t5-tiny-random"""       ,	framework="""pt"""				)
      _lowerCAmelCase					     =		translator("""This is a test string"""       ,	max_length=20				)
      self.assertEqual(
          lowerCamelCase       ,	[
              {
                  """translation_text""": (
                      """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                      """ urine urine urine urine urine urine urine"""
                  )
              }
          ]       ,	)







   @require_tf
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		pipeline("""translation_en_to_de"""       ,	model="""patrickvonplaten/t5-tiny-random"""       ,	framework="""tf"""				)
      _lowerCAmelCase					     =		translator("""This is a test string"""       ,	max_length=20				)
      self.assertEqual(
          lowerCamelCase       ,	[
              {
                  """translation_text""": (
                      """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                      """ urine urine urine urine urine urine urine"""
                  )
              }
          ]       ,	)





class       __lowerCamelCase	(   unittest.TestCase    ):







   @require_torch
   @slow
   def 							A__						(self				):




      '''simple docstring'''




      with self.assertRaises(lowerCamelCase				):
         pipeline(task="""translation_cn_to_ar"""				)

      # but we do for this one
      _lowerCAmelCase					     =		pipeline(task="""translation_en_to_de"""				)
      self.assertEqual(translator._preprocess_params["""src_lang"""]       ,	"""en"""				)
      self.assertEqual(translator._preprocess_params["""tgt_lang"""]       ,	"""de"""				)







   @require_torch
   @slow
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
      _lowerCAmelCase					     =		MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

      _lowerCAmelCase					     =		pipeline(task="""translation"""       ,	model=lowerCamelCase       ,	tokenizer=lowerCamelCase				)
      # Missing src_lang, tgt_lang
      with self.assertRaises(lowerCamelCase				):
         translator("""This is a test"""				)

      _lowerCAmelCase					     =		translator("""This is a test"""       ,	src_lang="""en_XX"""       ,	tgt_lang="""ar_AR"""				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": """هذا إختبار"""}]				)

      _lowerCAmelCase					     =		translator("""This is a test"""       ,	src_lang="""en_XX"""       ,	tgt_lang="""hi_IN"""				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": """यह एक परीक्षण है"""}]				)

      # src_lang, tgt_lang can be defined at pipeline call time
      _lowerCAmelCase					     =		pipeline(task="""translation"""       ,	model=lowerCamelCase       ,	tokenizer=lowerCamelCase       ,	src_lang="""en_XX"""       ,	tgt_lang="""ar_AR"""				)
      _lowerCAmelCase					     =		translator("""This is a test"""				)
      self.assertEqual(lowerCamelCase       ,	[{"""translation_text""": """هذا إختبار"""}]				)







   @require_torch
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		"""patrickvonplaten/t5-tiny-random"""
      _lowerCAmelCase					     =		pipeline(task="""translation_cn_to_ar"""       ,	model=lowerCamelCase				)
      self.assertEqual(translator._preprocess_params["""src_lang"""]       ,	"""cn"""				)
      self.assertEqual(translator._preprocess_params["""tgt_lang"""]       ,	"""ar"""				)







   @require_torch
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		"""patrickvonplaten/t5-tiny-random"""
      with pytest.warns(lowerCamelCase       ,	match=R""".*translation_en_to_de.*"""				):
         _lowerCAmelCase					     =		pipeline(task="""translation"""       ,	model=lowerCamelCase				)
      self.assertEqual(translator.task       ,	"""translation_en_to_de"""				)
      self.assertEqual(translator._preprocess_params["""src_lang"""]       ,	"""en"""				)
      self.assertEqual(translator._preprocess_params["""tgt_lang"""]       ,	"""de"""				)







   @require_torch
   def 							A__						(self				):




      '''simple docstring'''




      with self.assertRaises(lowerCamelCase				):
         pipeline(task="""translation"""				)




"""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()
SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	'''Hello world! cécé herlolip'''






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  bool   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		FairseqRobertaModel.from_pretrained(snake_case_   )
   roberta.eval()  # disable dropout
   _lowerCAmelCase					     =		roberta.model.encoder.sentence_encoder
   _lowerCAmelCase					     =		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=514   ,	type_vocab_size=1   ,	layer_norm_eps=1e-5   ,	)
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""   ,	snake_case_   )

   _lowerCAmelCase					     =		RobertaForSequenceClassification(snake_case_   ) if classification_head else RobertaForMaskedLM(snake_case_   )
   model.eval()

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

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

      # self attention
      _lowerCAmelCase					     =		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)   )
      )

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

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

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

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

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

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

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

   pathlib.Path(snake_case_   ).mkdir(parents=snake_case_   ,	exist_ok=snake_case_   )
   print(F"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Any          =	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.'''
  )
  SCREAMING_SNAKE_CASE				:     Any          =	parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )