

"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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
 a					:Tuple    = "platform"

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					)       ->				Any:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      str      		=	np.where(input_ids != config.pad_token_id  ,					1  ,					0							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	np.where(decoder_input_ids != config.pad_token_id  ,					1  ,					0							)
      if head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Any      		=	np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Any      		=	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      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=False	,    _a=99	,    _a=16	,    _a=2	,    _a=4	,    _a=4	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=32	,    _a=2	,    _a=1	,    _a=0	,    _a=0.02	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	parent
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      Dict      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	eos_token_id
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pad_token_id
           SCREAMING_SNAKE_CASE__   :      str      		=	bos_token_id
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	initializer_range

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	shift_tokens_right(_a	,    1	,    2  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	BlenderbotSmallConfig(
               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=_a	,    )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	prepare_blenderbot_inputs_dict(_a	,    _a	,    _a  )
           return config, inputs_dict

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.prepare_config_and_inputs()
           return config, inputs_dict

     def 					_a			(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	20
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_class_name(_a  )

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

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.init_cache(decoder_input_ids.shape[0]	,    _a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype="""i4"""  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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__   :      List[Any]      		=	model.decode(
               decoder_input_ids[:, :-1]	,    _a	,    decoder_attention_mask=_a	,    past_key_values=_a	,    decoder_position_ids=_a	,    )

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

           SCREAMING_SNAKE_CASE__   :      int      		=	model.decode(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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	,    _a	,    _a	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	20
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_class_name(_a  )

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

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

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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__   :      Any      		=	model.init_cache(decoder_input_ids.shape[0]	,    _a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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__   :      Any      		=	model.decode(
               decoder_input_ids[:, :-1]	,    _a	,    decoder_attention_mask=_a	,    past_key_values=_a	,    decoder_position_ids=_a	,    )
           SCREAMING_SNAKE_CASE__   :      int      		=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype="""i4"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model.decode(
               decoder_input_ids[:, -1:]	,    _a	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=_a	,    decoder_position_ids=_a	,    )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model.decode(_a	,    _a	,    decoder_attention_mask=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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      __a							(unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Dict       = 99

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]	,    dtype=np.intaa	,    )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	input_ids.shape[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	BlenderbotSmallConfig(
               vocab_size=self.vocab_size	,    d_model=24	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=32	,    decoder_ffn_dim=32	,    max_position_embeddings=48	,    eos_token_id=2	,    pad_token_id=1	,    bos_token_id=0	,    )
           return config, input_ids, batch_size

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self._get_config_and_data()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	FlaxBlenderbotSmallForConditionalGeneration(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	lm_model(input_ids=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape	,    _a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	BlenderbotSmallConfig(
               vocab_size=self.vocab_size	,    d_model=14	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=8	,    decoder_ffn_dim=8	,    max_position_embeddings=48	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	FlaxBlenderbotSmallForConditionalGeneration(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,    dtype=np.intaa  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,    dtype=np.intaa  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	lm_model(input_ids=_a	,    decoder_input_ids=_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	(*summary.shape, config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape	,    _a  )





     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,    dtype=np.intaa  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	shift_tokens_right(_a	,    1	,    2  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	np.equal(_a	,    1  ).astype(np.floataa  ).sum()
           SCREAMING_SNAKE_CASE__   :      Any      		=	np.equal(_a	,    1  ).astype(np.floataa  ).sum()
           self.assertEqual(shifted.shape	,    input_ids.shape  )
           self.assertEqual(_a	,    n_pad_before - 1  )
           self.assertTrue(np.equal(shifted[:, 0]	,    2  ).all()  )





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True
     _SCREAMING_SNAKE_CASE						:Optional[int]       = (
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	FlaxBlenderbotSmallModelTester(self  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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(_a	,    _a	,    _a  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	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__   :      Optional[int]      		=	self._prepare_for_class(_a	,    _a  )
                       SCREAMING_SNAKE_CASE__   :      str      		=	model_class(_a  )

                       @jax.jit
                       def encode_jitted(_a	,    _a=None	,    **_a  ):
                             return model.encode(input_ids=_a	,    attention_mask=_a  )

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

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

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

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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__   :      List[Any]      		=	model_class(_a  )
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	model.encode(inputs_dict["""input_ids"""]	,    inputs_dict["""attention_mask"""]  )

                       SCREAMING_SNAKE_CASE__   :      Any      		=	{
                           """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(_a	,    _a	,    _a  ):
                             return model.decode(
                                 decoder_input_ids=_a	,    decoder_attention_mask=_a	,    encoder_outputs=_a	,    )

                       with self.subTest("""JIT Enabled"""  ):
                             SCREAMING_SNAKE_CASE__   :      List[str]      		=	decode_jitted(**_a  ).to_tuple()

                       with self.subTest("""JIT Disabled"""  ):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decode_jitted(**_a  ).to_tuple()

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





     @slow
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      int      		=	model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""  )
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	np.ones((1, 1)  ) * model.config.eos_token_id
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model(_a  )
                 self.assertIsNotNone(_a  )







"""simple docstring"""


# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a					:str    = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a					:int    = {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
a					:Dict    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  a					:List[Any]    = "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  a					:str    = "allenai"




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE__   :      str      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[int]:
      # prep
      assert os.path.exists(__lowerCAmelCase							)
      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      print(F'''Writing results to {pytorch_dump_folder_path}'''							)

      # handle various types of models

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	basename(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dirname(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      int      		=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cls.hub_models()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""."""
      # note: since the model dump is old, fairseq has upgraded its model some
      # time later, and it does a whole lot of rewrites and splits on the saved
      # weights, therefore we can't use torch.load() directly on the model file.
      # see: upgrade_state_dict(state_dict) in fairseq_model.py
      print(F'''using checkpoint {checkpoint_file}'''							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hub_utils.from_pretrained(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					archive_map=__lowerCAmelCase  ,					**__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vars(chkpt["""args"""]["""model"""]							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""source_lang"""]
      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""target_lang"""]

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dirname(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	basename(__lowerCAmelCase							)

      # dicts
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{src_lang}.txt'''							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{tgt_lang}.txt'''							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	rewrite_dict_keys(src_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-src.json"""							)
      print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # detect whether this is a do_lower_case situation, which can be derived by checking whether we
      # have at least one uppercase letter in the source vocab
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True
      for k in src_vocab.keys():
            if not k.islower():
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                  break

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rewrite_dict_keys(tgt_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-tgt.json"""							)
      print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # merges_file (bpecodes)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					VOCAB_FILES_NAMES["""merges_file"""]							)
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
            if os.path.exists(__lowerCAmelCase							):
                  break
      with open(__lowerCAmelCase  ,					encoding="""utf-8"""							) as fin:
            SCREAMING_SNAKE_CASE__   :      Any      		=	fin.read()
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	re.sub(r""" \d+$"""  ,					""""""  ,					__lowerCAmelCase  ,					0  ,					re.M							)  # remove frequency number
      print(F'''Generating {merges_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as fout:
            fout.write(__lowerCAmelCase							)

      # model config
      SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(__lowerCAmelCase  ,					"""config.json"""							)

      # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
      # may have to modify the tokenizer if a different type is used by a future model
      assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
      assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """architectures""": ["""FSMTForConditionalGeneration"""],
          """model_type""": """fsmt""",
          """activation_dropout""": args["""activation_dropout"""],
          """activation_function""": """relu""",
          """attention_dropout""": args["""attention_dropout"""],
          """d_model""": args["""decoder_embed_dim"""],
          """dropout""": args["""dropout"""],
          """init_std""": 0.02,
          """max_position_embeddings""": args["""max_source_positions"""],
          """num_hidden_layers""": args["""encoder_layers"""],
          """src_vocab_size""": src_vocab_size,
          """tgt_vocab_size""": tgt_vocab_size,
          """langs""": [src_lang, tgt_lang],
          """encoder_attention_heads""": args["""encoder_attention_heads"""],
          """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
          """encoder_layerdrop""": args["""encoder_layerdrop"""],
          """encoder_layers""": args["""encoder_layers"""],
          """decoder_attention_heads""": args["""decoder_attention_heads"""],
          """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
          """decoder_layerdrop""": args["""decoder_layerdrop"""],
          """decoder_layers""": args["""decoder_layers"""],
          """bos_token_id""": 0,
          """pad_token_id""": 1,
          """eos_token_id""": 2,
          """is_encoder_decoder""": True,
          """scale_embedding""": not args["""no_scale_embedding"""],
          """tie_word_embeddings""": args["""share_all_embeddings"""],
      }

      # good hparam defaults to start with
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	5
      SCREAMING_SNAKE_CASE__   :      str      		=	False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	best_score_hparams[model_dir]["""length_penalty"""]
      else:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1.0

      print(F'''Generating {fsmt_model_config_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # tokenizer config
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
          """langs""": [src_lang, tgt_lang],
          """model_max_length""": 1024,
          """do_lower_case""": do_lower_case,
      }

      print(F'''Generating {fsmt_tokenizer_config_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # model
      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""models"""][0]
      SCREAMING_SNAKE_CASE__   :      int      		=	model.state_dict()

      # rename keys to start with 'model.'
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()							)

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      str      		=	[
          """model.model""",
          """model.encoder.version""",
          """model.decoder.version""",
          """model.encoder_embed_tokens.weight""",
          """model.decoder_embed_tokens.weight""",
          """model.encoder.embed_positions._float_tensor""",
          """model.decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            model_state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	FSMTConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FSMTForConditionalGeneration(__lowerCAmelCase							)

      # check that it loads ok
      model_new.load_state_dict(__lowerCAmelCase  ,					strict=__lowerCAmelCase							)

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
      print(F'''Generating {pytorch_weights_dump_path}'''							)
      torch.save(__lowerCAmelCase  ,					__lowerCAmelCase							)

      print("""Conversion is done!"""							)
      print("""\nLast step is to upload the files to s3"""							)
      print(F'''cd {data_root}'''							)
      print(F'''transformers-cli upload {model_dir}'''							)


if __name__ == "__main__":
  a					:Optional[Any]    = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fsmt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
          " bpecodes, etc."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a					:List[str]    = parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




