


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   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				MarianConfig
     __SCREAMING_SNAKE_CASE						    =				{}
     __SCREAMING_SNAKE_CASE						    =				"gelu"


     def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=1_3				,		__lowerCamelCase=7				,		__lowerCamelCase=True				,		__lowerCamelCase=False				,		__lowerCamelCase=9_9				,		__lowerCamelCase=3_2				,		__lowerCamelCase=2				,		__lowerCamelCase=4				,		__lowerCamelCase=3_7				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=2_0				,		__lowerCamelCase=2				,		__lowerCamelCase=1				,		__lowerCamelCase=0				,		)    ->       Optional[int]:
            _A   : Union[str, Any]       					=				parent
            _A   : Tuple       					=				batch_size
            _A   : Any       					=				seq_length
            _A   : Optional[Any]       					=				is_training
            _A   : Dict       					=				use_labels
            _A   : Dict       					=				vocab_size
            _A   : Dict       					=				hidden_size
            _A   : List[str]       					=				num_hidden_layers
            _A   : List[str]       					=				num_attention_heads
            _A   : List[Any]       					=				intermediate_size

            _A   : Dict       					=				hidden_dropout_prob
            _A   : Union[str, Any]       					=				attention_probs_dropout_prob
            _A   : Optional[Any]       					=				max_position_embeddings
            _A   : int       					=				eos_token_id
            _A   : List[str]       					=				pad_token_id
            _A   : Tuple       					=				bos_token_id


     def 				_lowerCamelCase				(		self)    ->       str:
            _A   : List[str]       					=				ids_tensor([self.batch_size, self.seq_length - 1]				,		self.vocab_size)
            _A   : Dict       					=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)				,		1)
            _A   : Optional[int]       					=				tf.concat([input_ids, eos_tensor]				,		axis=1)

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

            _A   : List[Any]       					=				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   : List[Any]       					=				prepare_marian_inputs_dict(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            return config, inputs_dict


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[Any]:
            _A   : Any       					=				TFMarianModel(config=__lowerCamelCase).get_decoder()
            _A   : Any       					=				inputs_dict["input_ids"]

            _A   : List[str]       					=				input_ids[:1, :]
            _A   : List[Any]       					=				inputs_dict["attention_mask"][:1, :]
            _A   : List[Any]       					=				inputs_dict["head_mask"]
            _A   : Any       					=				1

            # first forward pass
            _A   : Optional[int]       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		head_mask=__lowerCamelCase				,		use_cache=__lowerCamelCase)

            _A					,    _A   : Dict       					=				outputs.to_tuple()

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

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

            _A   : Dict       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase)[0]
            _A   : Union[str, Any]       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		past_key_values=__lowerCamelCase)[0]

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

            # select random slice
            _A   : Union[str, Any]       					=				int(ids_tensor((1,)				,		output_from_past.shape[-1]))
            _A   : Optional[int]       					=				output_from_no_past[:, -3:, random_slice_idx]
            _A   : Tuple       					=				output_from_past[:, :, random_slice_idx]

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




def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						List[Any]=None       ,							UpperCamelCase__  :						Tuple=None       ,							UpperCamelCase__  :						Optional[int]=None       ,							UpperCamelCase__  :						List[str]=None       ,							UpperCamelCase__  :						Any=None       ,							):
       if attention_mask is None:
              _A   : Tuple       					=				tf.cast(tf.math.not_equal(UpperCamelCase__       ,							config.pad_token_id		)       ,							tf.inta		)
       if decoder_attention_mask is None:
              _A   : Optional[Any]       					=				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   : Union[str, Any]       					=				tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
       if decoder_head_mask is None:
              _A   : Optional[int]       					=				tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
       if cross_attn_head_mask is None:
              _A   : Dict       					=				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   lowerCAmelCase__ (       a  ,					a  ,					unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE						    =				(TFMarianMTModel,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE						    =				(
         {
             "conversational": TFMarianMTModel,
             "feature-extraction": TFMarianModel,
             "summarization": TFMarianMTModel,
             "text2text-generation": TFMarianMTModel,
             "translation": TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE						    =				True
     __SCREAMING_SNAKE_CASE						    =				False
     __SCREAMING_SNAKE_CASE						    =				False


     def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
            _A   : Any       					=				TFMarianModelTester(self)
            _A   : Union[str, Any]       					=				ConfigTester(self				,		config_class=__lowerCamelCase)


     def 				_lowerCamelCase				(		self)    ->       Any:
            self.config_tester.run_common_tests()


     def 				_lowerCamelCase				(		self)    ->       List[str]:
            _A   : str       					=				self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase)







@require_tf
class   lowerCAmelCase__ (       unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				1000  # show more chars for failing integration tests


     @classmethod
     def 				_lowerCamelCase				(		cls)    ->       None:
            _A   : Any       					=				F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
            return cls


     @cached_property
     def 				_lowerCamelCase				(		self)    ->       MarianTokenizer:
            return AutoTokenizer.from_pretrained(self.model_name)


     @property
     def 				_lowerCamelCase				(		self)    ->       int:
            return self.tokenizer.eos_token_id


     @cached_property
     def 				_lowerCamelCase				(		self)    ->       Any:
            warnings.simplefilter("error")
            _A   : TFMarianMTModel       					=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
            assert isinstance(__lowerCamelCase				,		__lowerCamelCase)
            _A   : int       					=				model.config
            self.assertListEqual(c.bad_words_ids				,		[[c.pad_token_id]])
            self.assertEqual(c.max_length				,		5_1_2)
            self.assertEqual(c.decoder_start_token_id				,		c.pad_token_id)
            return model


     def 				_lowerCamelCase				(		self				,		**__lowerCamelCase)    ->       Any:
            _A   : List[Any]       					=				self.translate_src_text(**__lowerCamelCase)
            self.assertListEqual(self.expected_text				,		__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		**__lowerCamelCase)    ->       List[str]:
            _A   : List[Any]       					=				self.tokenizer(self.src_text				,		**__lowerCamelCase				,		padding=__lowerCamelCase				,		return_tensors="tf")
            _A   : str       					=				self.model.generate(
                model_inputs.input_ids				,		attention_mask=model_inputs.attention_mask				,		num_beams=2				,		max_length=1_2_8)
            _A   : int       					=				self.tokenizer.batch_decode(generated_ids.numpy()				,		skip_special_tokens=__lowerCamelCase)
            return generated_words







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"mt"
     __SCREAMING_SNAKE_CASE						    =				"en"
     __SCREAMING_SNAKE_CASE						    =				["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
     __SCREAMING_SNAKE_CASE						    =				["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)    ->       str:
            self._assert_generated_batch_equal_expected()







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"en"
     __SCREAMING_SNAKE_CASE						    =				"zh"
     __SCREAMING_SNAKE_CASE						    =				["My name is Wolfgang and I live in Berlin"]
     __SCREAMING_SNAKE_CASE						    =				["我叫沃尔夫冈 我住在柏林"]


     @unittest.skip("Skipping until #12647 is resolved.")
     @slow
     def 				_lowerCamelCase				(		self)    ->       int:
            self._assert_generated_batch_equal_expected()







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"en"
     __SCREAMING_SNAKE_CASE						    =				"ROMANCE"
     __SCREAMING_SNAKE_CASE						    =				[
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     __SCREAMING_SNAKE_CASE						    =				[
         "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)    ->       Dict:
            self._assert_generated_batch_equal_expected()


     @unittest.skip("Skipping until #12647 is resolved.")
     @slow
     def 				_lowerCamelCase				(		self)    ->       Optional[int]:
            _A   : List[Any]       					=				TranslationPipeline(self.model				,		self.tokenizer				,		framework="tf")
            _A   : List[Any]       					=				pipeline(self.src_text)
            self.assertEqual(self.expected_text				,		[x["translation_text"] for x in output])




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()
lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				'Hello world! cécé herlolip'
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool		):
       _A   : Union[str, Any]       					=				FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
       roberta.eval()  # disable dropout
       _A   : Dict       					=				roberta.model.encoder.sentence_encoder
       _A   : int       					=				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:
              _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our BERT config:"       ,							UpperCamelCase__		)

       _A   : Dict       					=				RobertaForSequenceClassification(UpperCamelCase__		) if classification_head else RobertaForMaskedLM(UpperCamelCase__		)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _A   : Optional[int]       					=				roberta_sent_encoder.embed_tokens.weight
       _A   : List[str]       					=				roberta_sent_encoder.embed_positions.weight
       _A   : Optional[int]       					=				torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
       _A   : List[Any]       					=				roberta_sent_encoder.emb_layer_norm.weight
       _A   : Tuple       					=				roberta_sent_encoder.emb_layer_norm.bias

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

              # self attention
              _A   : BertSelfAttention       					=				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   : List[str]       					=				roberta_layer.self_attn.q_proj.weight
              _A   : Optional[Any]       					=				roberta_layer.self_attn.q_proj.bias
              _A   : Union[str, Any]       					=				roberta_layer.self_attn.k_proj.weight
              _A   : Tuple       					=				roberta_layer.self_attn.k_proj.bias
              _A   : List[str]       					=				roberta_layer.self_attn.v_proj.weight
              _A   : Optional[Any]       					=				roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _A   : BertSelfOutput       					=				layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _A   : Tuple       					=				roberta_layer.self_attn.out_proj.weight
              _A   : List[str]       					=				roberta_layer.self_attn.out_proj.bias
              _A   : Dict       					=				roberta_layer.self_attn_layer_norm.weight
              _A   : Optional[int]       					=				roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _A   : BertIntermediate       					=				layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _A   : Tuple       					=				roberta_layer.fca.weight
              _A   : List[str]       					=				roberta_layer.fca.bias

              # output
              _A   : BertOutput       					=				layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _A   : int       					=				roberta_layer.fca.weight
              _A   : List[str]       					=				roberta_layer.fca.bias
              _A   : List[str]       					=				roberta_layer.final_layer_norm.weight
              _A   : Any       					=				roberta_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              _A   : int       					=				roberta.model.classification_heads["mnli"].dense.weight
              _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].dense.bias
              _A   : Optional[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight
              _A   : Optional[int]       					=				roberta.model.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              _A   : Any       					=				roberta.model.encoder.lm_head.dense.weight
              _A   : Dict       					=				roberta.model.encoder.lm_head.dense.bias
              _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.weight
              _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.bias
              _A   : List[Any]       					=				roberta.model.encoder.lm_head.weight
              _A   : Optional[Any]       					=				roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       _A   : torch.Tensor       					=				roberta.encode(UpperCamelCase__		).unsqueeze(0		)  # batch of size 1

       _A   : int       					=				model(UpperCamelCase__		)[0]
       if classification_head:
              _A   : str       					=				roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__		)		)
       else:
              _A   : int       					=				roberta.model(UpperCamelCase__		)[0]
       print(our_output.shape       ,							their_output.shape		)
       _A   : List[Any]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
       print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
       _A   : List[Any]       					=				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__":
    lowerCAmelCase__   	=				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.'
    )
    lowerCAmelCase__   	=				parser.parse_args()
    convert_roberta_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )
