'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXModel,
  )


class 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=13       ,lowerCamelCase_=7       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=99       ,lowerCamelCase_=64       ,lowerCamelCase_=5       ,lowerCamelCase_=4       ,lowerCamelCase_=37       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=16       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=3       ,lowerCamelCase_=4       ,lowerCamelCase_=None       ,)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	parent
         UpperCAmelCase__				:					int 					=	batch_size
         UpperCAmelCase__				:					int 					=	seq_length
         UpperCAmelCase__				:					Union[str, Any] 					=	is_training
         UpperCAmelCase__				:					Optional[Any] 					=	use_input_mask
         UpperCAmelCase__				:					Union[str, Any] 					=	use_token_type_ids
         UpperCAmelCase__				:					Tuple 					=	use_labels
         UpperCAmelCase__				:					int 					=	vocab_size
         UpperCAmelCase__				:					Dict 					=	hidden_size
         UpperCAmelCase__				:					Optional[int] 					=	num_hidden_layers
         UpperCAmelCase__				:					Tuple 					=	num_attention_heads
         UpperCAmelCase__				:					List[Any] 					=	intermediate_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_act
         UpperCAmelCase__				:					Optional[Any] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Optional[int] 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					Optional[Any] 					=	max_position_embeddings
         UpperCAmelCase__				:					Any 					=	type_vocab_size
         UpperCAmelCase__				:					Union[str, Any] 					=	type_sequence_label_size
         UpperCAmelCase__				:					str 					=	initializer_range
         UpperCAmelCase__				:					Union[str, Any] 					=	num_labels
         UpperCAmelCase__				:					Optional[Any] 					=	num_choices
         UpperCAmelCase__				:					List[Any] 					=	scope
         UpperCAmelCase__				:					Tuple 					=	vocab_size - 1




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size				)

         UpperCAmelCase__				:					Dict 					=	None
         if self.use_input_mask:
            UpperCAmelCase__				:					Optional[int] 					=	random_attention_mask([self.batch_size, self.seq_length]				)

         UpperCAmelCase__				:					Optional[Any] 					=	None
         if self.use_labels:
            UpperCAmelCase__				:					Dict 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels				)

         UpperCAmelCase__				:					List[str] 					=	self.get_config()

         return config, input_ids, input_mask, token_labels




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         return GPTNeoXConfig(
             vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,is_decoder=lowerCamelCase_       ,initializer_range=self.initializer_range       ,pad_token_id=self.pad_token_id       ,)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	self.prepare_config_and_inputs()

         UpperCAmelCase__				:					List[str] 					=	True

         return config, input_ids, input_mask, token_labels




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	GPTNeoXModel(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Optional[int] 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	True
         UpperCAmelCase__				:					Dict 					=	GPTNeoXModel(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	GPTNeoXForCausalLM(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Dict 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	GPTNeoXForQuestionAnswering(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					List[Any] 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_				)
         self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length)				)
         self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.num_labels
         UpperCAmelCase__				:					Optional[int] 					=	GPTNeoXForSequenceClassification(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					str 					=	ids_tensor([self.batch_size]       ,self.type_sequence_label_size				)
         UpperCAmelCase__				:					Dict 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.num_labels
         UpperCAmelCase__				:					Dict 					=	GPTNeoXForTokenClassification(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Dict 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	True
         UpperCAmelCase__				:					List[str] 					=	GPTNeoXForCausalLM(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()

         # first forward pass
         UpperCAmelCase__				:					str 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,use_cache=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         UpperCAmelCase__				:					Optional[int] 					=	ids_tensor((self.batch_size, 3)       ,config.vocab_size				)
         UpperCAmelCase__				:					List[str] 					=	ids_tensor((self.batch_size, 3)       ,vocab_size=2				)

         # append to next input_ids and
         UpperCAmelCase__				:					Optional[int] 					=	torch.cat([input_ids, next_tokens]       ,dim=-1				)
         UpperCAmelCase__				:					int 					=	torch.cat([input_mask, next_mask]       ,dim=-1				)

         UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,output_hidden_states=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	output_from_no_past['''hidden_states'''][0]
         UpperCAmelCase__				:					int 					=	model(
             lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,past_key_values=lowerCamelCase_       ,output_hidden_states=lowerCamelCase_       ,)['''hidden_states'''][0]

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

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-3				)				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	config_and_inputs
         UpperCAmelCase__				:					Dict 					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict




@require_torch
class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Union[str, Any]   				= (
          (
              GPTNeoXModel,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_   :   Any   				= (GPTNeoXForCausalLM,) if is_torch_available() else ()
      UpperCAmelCase_   :   Dict   				= (
          {
              '''feature-extraction''': GPTNeoXModel,
              '''question-answering''': GPTNeoXForQuestionAnswering,
              '''text-classification''': GPTNeoXForSequenceClassification,
              '''text-generation''': GPTNeoXForCausalLM,
              '''token-classification''': GPTNeoXForTokenClassification,
              '''zero-shot''': GPTNeoXForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_   :   Union[str, Any]   				= False
      UpperCAmelCase_   :   List[str]   				= False
      UpperCAmelCase_   :   Dict   				= False
      UpperCAmelCase_   :   Union[str, Any]   				= False




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	GPTNeoXModelTester(self				)
         UpperCAmelCase__				:					int 					=	ConfigTester(self       ,config_class=lowerCamelCase_       ,hidden_size=64       ,num_attention_heads=8				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         self.config_tester.run_common_tests()




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs_for_decoder()

         UpperCAmelCase__				:					List[str] 					=	None

         self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Any 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_				)




      @unittest.skip(reason='''Feed forward chunking is not implemented'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         pass




      @parameterized.expand([('''linear''',), ('''dynamic''',)]				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__				:					Tuple 					=	ids_tensor([1, 10]       ,config.vocab_size				)
         UpperCAmelCase__				:					Union[str, Any] 					=	ids_tensor([1, int(config.max_position_embeddings * 1.5				)]       ,config.vocab_size				)

         set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
         UpperCAmelCase__				:					Union[str, Any] 					=	GPTNeoXModel(lowerCamelCase_				)
         original_model.to(lowerCamelCase_				)
         original_model.eval()
         UpperCAmelCase__				:					Optional[int] 					=	original_model(lowerCamelCase_				).last_hidden_state
         UpperCAmelCase__				:					int 					=	original_model(lowerCamelCase_				).last_hidden_state

         set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
         UpperCAmelCase__				:					Union[str, Any] 					=	{'''type''': scaling_type, '''factor''': 10.0}
         UpperCAmelCase__				:					Union[str, Any] 					=	GPTNeoXModel(lowerCamelCase_				)
         scaled_model.to(lowerCamelCase_				)
         scaled_model.eval()
         UpperCAmelCase__				:					int 					=	scaled_model(lowerCamelCase_				).last_hidden_state
         UpperCAmelCase__				:					str 					=	scaled_model(lowerCamelCase_				).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5				)				)
         else:
            self.assertFalse(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5				)				)

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5				)				)




@require_torch
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''				)
         for checkpointing in [True, False]:
            UpperCAmelCase__				:					str 					=	GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''				)

            if checkpointing:
               model.gradient_checkpointing_enable()
            else:
               model.gradient_checkpointing_disable()
            model.to(lowerCamelCase_				)

            UpperCAmelCase__				:					Any 					=	tokenizer('''My favorite food is'''       ,return_tensors='''pt'''				).to(lowerCamelCase_				)
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            UpperCAmelCase__				:					Tuple 					=	'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

            UpperCAmelCase__				:					Any 					=	model.generate(**lowerCamelCase_       ,do_sample=lowerCamelCase_       ,max_new_tokens=20				)
            UpperCAmelCase__				:					Optional[int] 					=	tokenizer.batch_decode(lowerCamelCase_				)[0]

            self.assertEqual(lowerCamelCase_       ,lowerCamelCase_				)





'''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()

UpperCamelCase__  :						Optional[Any]					=      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
UpperCamelCase__  :						str					=      {
    # 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
UpperCamelCase__  :						Tuple					=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCamelCase__  :						Tuple					=      '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",
]:
  UpperCamelCase__  :						Tuple					=      'allenai'







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   # (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}
   UpperCAmelCase__				:					int 					=	dict((re.sub(R'''@@$'''	,      ''''''	,      _A     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''	,      '''</w>'''	,      _A     ), v) for k, v in d.items()     )
   UpperCAmelCase__				:					List[str] 					=	'''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      UpperCAmelCase__				:					Optional[Any] 					=	d[k]  # restore
   return da







def __UpperCamelCase( _A       :					Tuple	,      _A       :					List[Any]     ):
   '''simple docstring'''
   # prep
   assert os.path.exists(_A     )
   os.makedirs(_A	,      exist_ok=_A     )
   print(F'''Writing results to {pytorch_dump_folder_path}'''     )

   # handle various types of models

   UpperCAmelCase__				:					Dict 					=	basename(_A     )
   UpperCAmelCase__				:					List[str] 					=	dirname(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   UpperCAmelCase__				:					Dict 					=	cls.hub_models()
   UpperCAmelCase__				:					Dict 					=	{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
   UpperCAmelCase__				:					int 					=	'''.'''
   # 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}'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	hub_utils.from_pretrained(
       _A	,      _A	,      _A	,      archive_map=_A	,      **_A     )

   UpperCAmelCase__				:					List[Any] 					=	vars(chkpt['''args''']['''model''']     )

   UpperCAmelCase__				:					str 					=	args['''source_lang''']
   UpperCAmelCase__				:					Tuple 					=	args['''target_lang''']

   UpperCAmelCase__				:					Optional[int] 					=	dirname(_A     )
   UpperCAmelCase__				:					int 					=	basename(_A     )

   # dicts
   UpperCAmelCase__				:					List[Any] 					=	os.path.join(_A	,      F'''dict.{src_lang}.txt'''     )
   UpperCAmelCase__				:					Tuple 					=	os.path.join(_A	,      F'''dict.{tgt_lang}.txt'''     )

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

   # 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
   UpperCAmelCase__				:					Tuple 					=	True
   for k in src_vocab.keys():
      if not k.islower():
         UpperCAmelCase__				:					int 					=	False
         break

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

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

   # model config
   UpperCAmelCase__				:					Optional[int] 					=	os.path.join(_A	,      '''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']}'''

   UpperCAmelCase__				:					Optional[Any] 					=	{
       '''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''': 0.0_2,
       '''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
   UpperCAmelCase__				:					Optional[int] 					=	5
   UpperCAmelCase__				:					Optional[Any] 					=	False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      UpperCAmelCase__				:					str 					=	best_score_hparams[model_dir]['''length_penalty''']
   else:
      UpperCAmelCase__				:					Dict 					=	1.0

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

   # tokenizer config
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )

   UpperCAmelCase__				:					Any 					=	{
       '''langs''': [src_lang, tgt_lang],
       '''model_max_length''': 10_24,
       '''do_lower_case''': do_lower_case,
   }

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

   # model
   UpperCAmelCase__				:					Dict 					=	chkpt['''models'''][0]
   UpperCAmelCase__				:					Any 					=	model.state_dict()

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

   # remove unneeded keys
   UpperCAmelCase__				:					Union[str, Any] 					=	[
       '''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(_A	,      _A     )

   UpperCAmelCase__				:					List[str] 					=	FSMTConfig.from_pretrained(_A     )
   UpperCAmelCase__				:					int 					=	FSMTForConditionalGeneration(_A     )

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

   # save
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )
   print(F'''Generating {pytorch_weights_dump_path}'''     )
   torch.save(_A	,      _A     )

   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__":
  UpperCamelCase__  :						int					=      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.'
  )
  UpperCamelCase__  :						List[str]					=      parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




