import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   __lowerCamelCase     :					int				  =						'''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Tuple					,	__UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			List[str]=None					,	__UpperCamelCase       :			List[str]=None					,	__UpperCamelCase       :			List[str]=None					,	__UpperCamelCase       :			Union[str, Any]=None					,	__UpperCamelCase       :			Dict=None					,	__UpperCamelCase       :			List[str]=None					,	)				->   Tuple:







  """simple docstring"""





  if attention_mask is None:
    SCREAMING_SNAKE_CASE__     				= np.where(input_ids != config.pad_token_id					,	1					,	0      )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE__     				= np.where(decoder_input_ids != config.pad_token_id					,	1					,	0      )
  if head_mask is None:
    SCREAMING_SNAKE_CASE__     				= np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE__     				= np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE__     				= np.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": attention_mask,
  }







class __snake_case							:




   def __init__(       self     :							Dict			,      _lowercase     :							Dict			,      _lowercase     :							str=13			,      _lowercase     :							Union[str, Any]=7			,      _lowercase     :							List[Any]=True			,      _lowercase     :							Tuple=False			,      _lowercase     :							Optional[Any]=99			,      _lowercase     :							Any=16			,      _lowercase     :							Dict=2			,      _lowercase     :							Optional[int]=4			,      _lowercase     :							Tuple=4			,      _lowercase     :							Dict="gelu"			,      _lowercase     :							Any=0.1			,      _lowercase     :							List[Any]=0.1			,      _lowercase     :							Optional[int]=32			,      _lowercase     :							Tuple=2			,      _lowercase     :							Union[str, Any]=1			,      _lowercase     :							int=0			,      _lowercase     :							str=0.02			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= parent
     SCREAMING_SNAKE_CASE__     				= batch_size
     SCREAMING_SNAKE_CASE__     				= seq_length
     SCREAMING_SNAKE_CASE__     				= is_training
     SCREAMING_SNAKE_CASE__     				= use_labels
     SCREAMING_SNAKE_CASE__     				= vocab_size
     SCREAMING_SNAKE_CASE__     				= hidden_size
     SCREAMING_SNAKE_CASE__     				= num_hidden_layers
     SCREAMING_SNAKE_CASE__     				= num_attention_heads
     SCREAMING_SNAKE_CASE__     				= intermediate_size
     SCREAMING_SNAKE_CASE__     				= hidden_act
     SCREAMING_SNAKE_CASE__     				= hidden_dropout_prob
     SCREAMING_SNAKE_CASE__     				= attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__     				= max_position_embeddings
     SCREAMING_SNAKE_CASE__     				= eos_token_id
     SCREAMING_SNAKE_CASE__     				= pad_token_id
     SCREAMING_SNAKE_CASE__     				= bos_token_id
     SCREAMING_SNAKE_CASE__     				= initializer_range




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,      self.vocab_size )			,      3			,      self.vocab_size )
     SCREAMING_SNAKE_CASE__     				= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,      dtype=np.intaa ))			,      -1 )

     SCREAMING_SNAKE_CASE__     				= shift_tokens_right(_lowerCamelCase			,      1			,      2 )

     SCREAMING_SNAKE_CASE__     				= MarianConfig(
         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_id=self.eos_token_id			,      bos_token_id=self.bos_token_id			,      pad_token_id=self.pad_token_id			,      initializer_range=self.initializer_range			,      use_cache=_lowerCamelCase			,      )
     SCREAMING_SNAKE_CASE__     				= prepare_marian_inputs_dict(_lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase )
     return config, inputs_dict




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()
     return config, inputs_dict




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Any			,      _lowercase     :							Dict			,      _lowercase     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 20
     SCREAMING_SNAKE_CASE__     				= model_class_name(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= model.encode(inputs_dict["""input_ids"""] )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= (
         inputs_dict["""decoder_input_ids"""],
         inputs_dict["""decoder_attention_mask"""],
     )

     SCREAMING_SNAKE_CASE__     				= model.init_cache(decoder_input_ids.shape[0]			,      _lowerCamelCase			,      _lowerCamelCase )
     SCREAMING_SNAKE_CASE__     				= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,      dtype="""i4""" )

     SCREAMING_SNAKE_CASE__     				= jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]			,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,      )
     SCREAMING_SNAKE_CASE__     				= model.decode(
         decoder_input_ids[:, :-1]			,      _lowerCamelCase			,      decoder_attention_mask=_lowerCamelCase			,      past_key_values=_lowerCamelCase			,      decoder_position_ids=_lowerCamelCase			,      )

     SCREAMING_SNAKE_CASE__     				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,      dtype="""i4""" )
     SCREAMING_SNAKE_CASE__     				= model.decode(
         decoder_input_ids[:, -1:]			,      _lowerCamelCase			,      decoder_attention_mask=_lowerCamelCase			,      past_key_values=outputs_cache.past_key_values			,      decoder_position_ids=_lowerCamelCase			,      )

     SCREAMING_SNAKE_CASE__     				= model.decode(_lowerCamelCase			,      _lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
     self.parent.assertTrue(diff < 1E-3			,      msg=f"""Max diff is {diff}""" )




   def       __a		(       self     :							Dict			,      _lowercase     :							Dict			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 20
     SCREAMING_SNAKE_CASE__     				= model_class_name(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= model.encode(inputs_dict["""input_ids"""] )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= (
         inputs_dict["""decoder_input_ids"""],
         inputs_dict["""decoder_attention_mask"""],
     )

     SCREAMING_SNAKE_CASE__     				= jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
         ]			,      axis=-1			,      )

     SCREAMING_SNAKE_CASE__     				= model.init_cache(decoder_input_ids.shape[0]			,      _lowerCamelCase			,      _lowerCamelCase )
     SCREAMING_SNAKE_CASE__     				= jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]			,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,      )

     SCREAMING_SNAKE_CASE__     				= model.decode(
         decoder_input_ids[:, :-1]			,      _lowerCamelCase			,      decoder_attention_mask=_lowerCamelCase			,      past_key_values=_lowerCamelCase			,      decoder_position_ids=_lowerCamelCase			,      )
     SCREAMING_SNAKE_CASE__     				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,      dtype="""i4""" )
     SCREAMING_SNAKE_CASE__     				= model.decode(
         decoder_input_ids[:, -1:]			,      _lowerCamelCase			,      past_key_values=outputs_cache.past_key_values			,      decoder_attention_mask=_lowerCamelCase			,      decoder_position_ids=_lowerCamelCase			,      )

     SCREAMING_SNAKE_CASE__     				= model.decode(_lowerCamelCase			,      _lowerCamelCase			,      decoder_attention_mask=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
     self.parent.assertTrue(diff < 1E-3			,      msg=f"""Max diff is {diff}""" )







@require_flax
class __snake_case							(		_lowerCAmelCase  ,						unittest.TestCase  ,						_lowerCAmelCase						):
   lowerCAmelCase_								=				True
   lowerCAmelCase_								=				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   lowerCAmelCase_								=				(FlaxMarianMTModel,) if is_flax_available() else ()




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= FlaxMarianModelTester(self )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(_lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase )




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
         SCREAMING_SNAKE_CASE__     				= self._prepare_for_class(_lowerCamelCase			,      _lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= model_class(_lowerCamelCase )

         @jax.jit
         def encode_jitted(_lowercase     :							Optional[Any]			,      _lowercase     :							List[Any]=None			,      **_lowercase     :							str ):
           return model.encode(input_ids=_lowerCamelCase			,      attention_mask=_lowerCamelCase )

         with self.subTest("""JIT Enabled""" ):
           SCREAMING_SNAKE_CASE__     				= encode_jitted(**_lowerCamelCase ).to_tuple()

         with self.subTest("""JIT Disabled""" ):
           with jax.disable_jit():
             SCREAMING_SNAKE_CASE__     				= encode_jitted(**_lowerCamelCase ).to_tuple()

         self.assertEqual(len(_lowerCamelCase )			,      len(_lowerCamelCase ) )
         for jitted_output, output in zip(_lowerCamelCase			,      _lowerCamelCase ):
           self.assertEqual(jitted_output.shape			,      output.shape )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
         SCREAMING_SNAKE_CASE__     				= model_class(_lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= model.encode(inputs_dict["""input_ids"""]			,      inputs_dict["""attention_mask"""] )

         SCREAMING_SNAKE_CASE__     				= {
             """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
             """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
             """encoder_outputs""": encoder_outputs,
         }

         @jax.jit
         def decode_jitted(_lowercase     :							str			,      _lowercase     :							Tuple			,      _lowercase     :							Tuple ):
           return model.decode(
               decoder_input_ids=_lowerCamelCase			,      decoder_attention_mask=_lowerCamelCase			,      encoder_outputs=_lowerCamelCase			,      )

         with self.subTest("""JIT Enabled""" ):
           SCREAMING_SNAKE_CASE__     				= decode_jitted(**_lowerCamelCase ).to_tuple()

         with self.subTest("""JIT Disabled""" ):
           with jax.disable_jit():
             SCREAMING_SNAKE_CASE__     				= decode_jitted(**_lowerCamelCase ).to_tuple()

         self.assertEqual(len(_lowerCamelCase )			,      len(_lowerCamelCase ) )
         for jitted_output, output in zip(_lowerCamelCase			,      _lowerCamelCase ):
           self.assertEqual(jitted_output.shape			,      output.shape )




   @slow
   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de""" )
       # FlaxMarianForSequenceClassification expects eos token in input_ids
       SCREAMING_SNAKE_CASE__     				= np.ones((1, 1) ) * model.config.eos_token_id
       SCREAMING_SNAKE_CASE__     				= model(_lowerCamelCase )
       self.assertIsNotNone(_lowerCamelCase )







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		unittest.TestCase						):
   lowerCAmelCase_								=				None
   lowerCAmelCase_								=				None




   @classmethod
   def       __a		(       cls     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
     return cls




   @cached_property
   def       __a		(       self     :							Any ):



     """simple docstring"""






     return MarianTokenizer.from_pretrained(self.model_name )




   @property
   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     return self.tokenizer.eos_token_id




   @cached_property
   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= FlaxMarianMTModel.from_pretrained(self.model_name )
     self.assertEqual(model.config.decoder_start_token_id			,      model.config.pad_token_id )
     return model




   def       __a		(       self     :							List[Any]			,      **_lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.translate_src_text(**_lowerCamelCase )
     self.assertListEqual(self.expected_text			,      _lowerCamelCase )




   def       __a		(       self     :							Tuple			,      **_lowercase     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.tokenizer(self.src_text			,      padding=_lowerCamelCase			,      return_tensors="""np"""			,      **_lowerCamelCase )
     SCREAMING_SNAKE_CASE__     				= self.model.generate(
         model_inputs.input_ids			,      attention_mask=model_inputs.attention_mask			,      num_beams=2			,      max_length=1_28			,      ).sequences
     SCREAMING_SNAKE_CASE__     				= self.tokenizer.batch_decode(_lowerCamelCase			,      skip_special_tokens=_lowerCamelCase )
     return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"en"
   lowerCAmelCase_								=				"fr"
   lowerCAmelCase_								=				[
       "I am a small frog.",
       "Now I can forget the 100 words of german that I know.",
   ]
   lowerCAmelCase_								=				[
       "Je suis une petite grenouille.",
       "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
   ]




   @slow
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"fr"
   lowerCAmelCase_								=				"en"
   lowerCAmelCase_								=				[
       "Donnez moi le micro.",
       "Tom et Mary étaient assis à une table.",  # Accents
   ]
   lowerCAmelCase_								=				[
       "Give me the microphone.",
       "Tom and Mary were sitting at a table.",
   ]




   @slow
   def       __a		(       self     :							str ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"mt"
   lowerCAmelCase_								=				"en"
   lowerCAmelCase_								=				["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
   lowerCAmelCase_								=				["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]




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



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"en"
   lowerCAmelCase_								=				"de"
   lowerCAmelCase_								=				[
       "I am a small frog.",
       "Now I can forget the 100 words of german that I know.",
       "Tom asked his teacher for advice.",
       "That's how I would do it.",
       "Tom really admired Mary's courage.",
       "Turn around and close your eyes.",
   ]
   lowerCAmelCase_								=				[
       "Ich bin ein kleiner Frosch.",
       "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
       "Tom bat seinen Lehrer um Rat.",
       "So würde ich das machen.",
       "Tom bewunderte Marias Mut wirklich.",
       "Drehen Sie sich um und schließen Sie die Augen.",
   ]




   @slow
   def       __a		(       self     :							int ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"en"
   lowerCAmelCase_								=				"zh"
   lowerCAmelCase_								=				["My name is Wolfgang and I live in Berlin"]
   lowerCAmelCase_								=				["我叫沃尔夫冈 我住在柏林"]




   @slow
   def       __a		(       self     :							Dict ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				"ru"
   lowerCAmelCase_								=				"fr"
   lowerCAmelCase_								=				["Он показал мне рукопись своей новой пьесы."]
   lowerCAmelCase_								=				["Il m'a montré le manuscrit de sa nouvelle pièce."]




   @slow
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()







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




   @slow
   def       __a		(       self     :							int ):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()








"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




