






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch

class 		_snake_case  :






   def __init__(       self		,					a		,					a=14		,					a=7		,					a=True		,					a=True		,					a=False		,					a=True		,					a=99		,					a=32		,					a=4		,					a=4		,					a=4		,					a=37		,					a="gelu"		,					a=0.1		,					a=0.1		,					a=512		,					a=0.02		,					)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= parent
      SCREAMING_SNAKE_CASE						= batch_size
      SCREAMING_SNAKE_CASE						= seq_length
      SCREAMING_SNAKE_CASE						= is_training
      SCREAMING_SNAKE_CASE						= use_input_mask
      SCREAMING_SNAKE_CASE						= use_token_type_ids
      SCREAMING_SNAKE_CASE						= use_labels
      SCREAMING_SNAKE_CASE						= vocab_size
      SCREAMING_SNAKE_CASE						= hidden_size
      SCREAMING_SNAKE_CASE						= rotary_dim
      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						= initializer_range
      SCREAMING_SNAKE_CASE						= None
      SCREAMING_SNAKE_CASE						= vocab_size - 1
      SCREAMING_SNAKE_CASE						= vocab_size - 1
      SCREAMING_SNAKE_CASE						= vocab_size - 1






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size)

      SCREAMING_SNAKE_CASE						= None
      if self.use_input_mask:
         SCREAMING_SNAKE_CASE						= random_attention_mask([self.batch_size, self.seq_length])

      SCREAMING_SNAKE_CASE						= GPTJConfig(
          vocab_size=self.vocab_size		,					n_embd=self.hidden_size		,					n_layer=self.num_hidden_layers		,					n_head=self.num_attention_heads		,					n_positions=self.max_position_embeddings		,					use_cache=__A		,					bos_token_id=self.bos_token_id		,					eos_token_id=self.eos_token_id		,					pad_token_id=self.pad_token_id		,					rotary_dim=self.rotary_dim		,					)

      return (config, input_ids, input_mask)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= config_and_inputs
      SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a)      ->       Dict:
      SCREAMING_SNAKE_CASE						= 20
      SCREAMING_SNAKE_CASE						= model_class_name(__A)

      SCREAMING_SNAKE_CASE						= model.init_cache(input_ids.shape[0]		,					__A)
      SCREAMING_SNAKE_CASE						= jnp.ones((input_ids.shape[0], max_decoder_length)		,					dtype='i4')

      SCREAMING_SNAKE_CASE						= jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1)[None, :]		,					(input_ids.shape[0], input_ids.shape[-1] - 1))
      SCREAMING_SNAKE_CASE						= model(
          input_ids[:, :-1]		,					attention_mask=__A		,					past_key_values=__A		,					position_ids=__A		,					)

      SCREAMING_SNAKE_CASE						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,					dtype='i4')
      SCREAMING_SNAKE_CASE						= model(
          input_ids[:, -1:]		,					attention_mask=__A		,					past_key_values=outputs_cache.past_key_values		,					position_ids=__A		,					)

      SCREAMING_SNAKE_CASE						= model(__A)

      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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a)      ->       Optional[int]:
      SCREAMING_SNAKE_CASE						= 20
      SCREAMING_SNAKE_CASE						= model_class_name(__A)

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

      SCREAMING_SNAKE_CASE						= model.init_cache(input_ids.shape[0]		,					__A)
      SCREAMING_SNAKE_CASE						= jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1)[None, :]		,					(input_ids.shape[0], input_ids.shape[-1] - 1))

      SCREAMING_SNAKE_CASE						= model(
          input_ids[:, :-1]		,					attention_mask=__A		,					past_key_values=__A		,					position_ids=__A		,					)
      SCREAMING_SNAKE_CASE						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,					dtype='i4')
      SCREAMING_SNAKE_CASE						= model(
          input_ids[:, -1:]		,					past_key_values=outputs_cache.past_key_values		,					attention_mask=__A		,					position_ids=__A		,					)

      SCREAMING_SNAKE_CASE						= model(__A		,					attention_mask=__A)

      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  (  UpperCamelCase__   ,    UpperCamelCase__   ,    unittest.TestCase					):
   _lowercase		:    Optional[Any]			     =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   _lowercase		:    Dict			     =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= FlaxGPTJModelTester(self)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:



      for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(__A		,					__A		,					__A		,					__A)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:



      for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             __A		,					__A		,					__A		,					__A)






   @tooslow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      SCREAMING_SNAKE_CASE						= GPTaTokenizer.from_pretrained('gpt2'		,					pad_token='<|endoftext|>'		,					padding_side='left')
      SCREAMING_SNAKE_CASE						= tokenizer(['Hello this is a long string', 'Hey']		,					return_tensors='np'		,					padding=__A		,					truncation=__A)

      SCREAMING_SNAKE_CASE						= FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
      SCREAMING_SNAKE_CASE						= False
      SCREAMING_SNAKE_CASE						= model.config.eos_token_id

      SCREAMING_SNAKE_CASE						= jax.jit(model.generate)

      SCREAMING_SNAKE_CASE						= jit_generate(
          inputs['input_ids']		,					attention_mask=inputs['attention_mask']		,					pad_token_id=tokenizer.pad_token_id).sequences

      SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(__A		,					skip_special_tokens=__A)

      SCREAMING_SNAKE_CASE						= [
          'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
          'Hey, I\'m a little late to the party. I\'m going to',
      ]

      self.assertListEqual(__A		,					__A)






   @is_pt_flax_cross_test
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      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__):
            # prepare inputs
            SCREAMING_SNAKE_CASE						= self._prepare_for_class(__A		,					__A)
            SCREAMING_SNAKE_CASE						= {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            SCREAMING_SNAKE_CASE						= model_class.__name__[4:]  # Skip the "Flax" at the beginning
            SCREAMING_SNAKE_CASE						= getattr(__A		,					__A)

            SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pt_inputs['input_ids'].shape
            SCREAMING_SNAKE_CASE						= np.random.randint(0		,					seq_length - 1		,					size=(batch_size,))
            for batch_idx, start_index in enumerate(__A):
               SCREAMING_SNAKE_CASE						= 0
               SCREAMING_SNAKE_CASE						= 1
               SCREAMING_SNAKE_CASE						= 0
               SCREAMING_SNAKE_CASE						= 1
            SCREAMING_SNAKE_CASE						= pt_model_class(__A).eval()
            SCREAMING_SNAKE_CASE						= model_class(__A		,					dtype=jnp.floataa)

            SCREAMING_SNAKE_CASE						= convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,					__A)
            SCREAMING_SNAKE_CASE						= fx_state

            with torch.no_grad():
               SCREAMING_SNAKE_CASE						= pt_model(**__A).to_tuple()

            SCREAMING_SNAKE_CASE						= fx_model(**__A).to_tuple()
            self.assertEqual(len(__A)		,					len(__A)		,					'Output lengths differ between Flax and PyTorch')
            for fx_output, pt_output in zip(__A		,					__A):
               self.assert_almost_equals(fx_output[:, -1]		,					pt_output[:, -1].numpy()		,					4E-2)

            with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(__A)
               SCREAMING_SNAKE_CASE						= model_class.from_pretrained(__A		,					from_pt=__A)

            SCREAMING_SNAKE_CASE						= fx_model_loaded(**__A).to_tuple()
            self.assertEqual(
                len(__A)		,					len(__A)		,					'Output lengths differ between Flax and PyTorch')
            for fx_output_loaded, pt_output in zip(__A		,					__A):
               self.assert_almost_equals(fx_output_loaded[:, -1]		,					pt_output[:, -1].numpy()		,					4E-2)






   @is_pt_flax_cross_test
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      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__):
            # prepare inputs
            SCREAMING_SNAKE_CASE						= self._prepare_for_class(__A		,					__A)
            SCREAMING_SNAKE_CASE						= {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            SCREAMING_SNAKE_CASE						= model_class.__name__[4:]  # Skip the "Flax" at the beginning
            SCREAMING_SNAKE_CASE						= getattr(__A		,					__A)

            SCREAMING_SNAKE_CASE						= pt_model_class(__A).eval()
            SCREAMING_SNAKE_CASE						= model_class(__A		,					dtype=jnp.floataa)

            SCREAMING_SNAKE_CASE						= load_flax_weights_in_pytorch_model(__A		,					fx_model.params)
            SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pt_inputs['input_ids'].shape
            SCREAMING_SNAKE_CASE						= np.random.randint(0		,					seq_length - 1		,					size=(batch_size,))
            for batch_idx, start_index in enumerate(__A):
               SCREAMING_SNAKE_CASE						= 0
               SCREAMING_SNAKE_CASE						= 1
               SCREAMING_SNAKE_CASE						= 0
               SCREAMING_SNAKE_CASE						= 1

            # make sure weights are tied in PyTorch
            pt_model.tie_weights()

            with torch.no_grad():
               SCREAMING_SNAKE_CASE						= pt_model(**__A).to_tuple()

            SCREAMING_SNAKE_CASE						= fx_model(**__A).to_tuple()
            self.assertEqual(len(__A)		,					len(__A)		,					'Output lengths differ between Flax and PyTorch')
            for fx_output, pt_output in zip(__A		,					__A):
               self.assert_almost_equals(fx_output[:, -1]		,					pt_output[:, -1].numpy()		,					4E-2)

            with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(__A)
               SCREAMING_SNAKE_CASE						= pt_model_class.from_pretrained(__A		,					from_flax=__A)

            with torch.no_grad():
               SCREAMING_SNAKE_CASE						= pt_model_loaded(**__A).to_tuple()

            self.assertEqual(
                len(__A)		,					len(__A)		,					'Output lengths differ between Flax and PyTorch')
            for fx_output, pt_output in zip(__A		,					__A):
               self.assert_almost_equals(fx_output[:, -1]		,					pt_output[:, -1].numpy()		,					4E-2)







   @tooslow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:



      for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
         SCREAMING_SNAKE_CASE						= model(np.ones((1, 1)))
         self.assertIsNotNone(__A)












import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a_		:						Optional[Any]             =    logging.getLogger(__name__)

@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,    )
			_lowercase		:    bool			     =							field(default=A__   ,    metadata={'''help''': '''Whether tp freeze the encoder.'''}					)
			_lowercase		:    bool			     =							field(default=A__   ,    metadata={'''help''': '''Whether  to freeze the embeddings.'''}					)



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default='''summarization'''   ,    metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=10_24   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded. '''
			            '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
			            '''during ``evaluate`` and ``predict``.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# training examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# validation examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# test examples. -1 means use all.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Source language id for translation.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Target language id for translation.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=A__   ,    metadata={'''help''': '''# num_beams to use for evaluation.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}   ,    )






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			logger.info(F'''***** {split} metrics *****''')
			for key in sorted(metrics.keys()):
						logger.info(F'''  {key} = {metrics[key]}''')
			save_json(_UpperCAmelCase							,       os.path.join(_UpperCAmelCase							,       F'''{split}_results.json'''))






def 				lowerCamelCase__				():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			SCREAMING_SNAKE_CASE						= HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments))

			if len(sys.argv) == 2 and sys.argv[1].endswith('.json'):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			check_output_dir(_UpperCAmelCase)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,       )
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,       training_args.local_rank							,       training_args.device							,       training_args.n_gpu							,       bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED)							,       training_args.fpaa							,       )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank):
						transformers.utils.logging.set_verbosity_info()
			logger.info('Training/evaluation parameters %s'							,       _UpperCAmelCase)

			# Set seed
			set_seed(training_args.seed)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			SCREAMING_SNAKE_CASE						= AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       cache_dir=model_args.cache_dir							,       )

			SCREAMING_SNAKE_CASE						= ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
			for p in extra_model_params:
						if getattr(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
									assert hasattr(_UpperCAmelCase							,       _UpperCAmelCase), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
									setattr(_UpperCAmelCase							,       _UpperCAmelCase							,       getattr(_UpperCAmelCase							,       _UpperCAmelCase))

			SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       cache_dir=model_args.cache_dir							,       )
			SCREAMING_SNAKE_CASE						= AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path							,       from_tf='.ckpt' in model_args.model_name_or_path							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       )

			# use task specific params
			use_task_specific_params(_UpperCAmelCase							,       data_args.task)

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						SCREAMING_SNAKE_CASE						= model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(_UpperCAmelCase							,       (MBartTokenizer, MBartTokenizerFast)):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(_UpperCAmelCase							,       _UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(data_args.tgt_lang)

			if model_args.freeze_embeds:
						freeze_embeds(_UpperCAmelCase)
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder())
						assert_all_frozen(model.get_encoder())

			SCREAMING_SNAKE_CASE						= SeqaSeqDataset

			# Get datasets
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='train'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_train							,       max_target_length=data_args.max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_train
			    else None
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='val'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_val							,       max_target_length=data_args.val_max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='test'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_test							,       max_target_length=data_args.test_max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE						= (
			    build_compute_metrics_fn(data_args.task							,       _UpperCAmelCase) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE						= SeqaSeqTrainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       data_args=_UpperCAmelCase							,       train_dataset=_UpperCAmelCase							,       eval_dataset=_UpperCAmelCase							,       data_collator=SeqaSeqDataCollator(
			        _UpperCAmelCase							,       _UpperCAmelCase							,       model.config.decoder_start_token_id							,       training_args.tpu_num_cores)							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       )

			SCREAMING_SNAKE_CASE						= {}
			# Training
			if training_args.do_train:
						logger.info('*** Train ***')

						SCREAMING_SNAKE_CASE						= trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
						SCREAMING_SNAKE_CASE						= train_result.metrics
						SCREAMING_SNAKE_CASE						= data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics('train'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir							,       'trainer_state.json'))

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***')

						SCREAMING_SNAKE_CASE						= trainer.evaluate(metric_key_prefix='val')
						SCREAMING_SNAKE_CASE						= data_args.n_val
						SCREAMING_SNAKE_CASE						= round(metrics['val_loss']							,       4)

						if trainer.is_world_process_zero():
									handle_metrics('val'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

			if training_args.do_predict:
						logger.info('*** Predict ***')

						SCREAMING_SNAKE_CASE						= trainer.predict(test_dataset=_UpperCAmelCase							,       metric_key_prefix='test')
						SCREAMING_SNAKE_CASE						= test_output.metrics
						SCREAMING_SNAKE_CASE						= data_args.n_test

						if trainer.is_world_process_zero():
									SCREAMING_SNAKE_CASE						= round(metrics['test_loss']							,       4)
									handle_metrics('test'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									if training_args.predict_with_generate:
												SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(
												    test_output.predictions							,       skip_special_tokens=_UpperCAmelCase							,       clean_up_tokenization_spaces=_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= lmap(str.strip							,       _UpperCAmelCase)
												write_txt_file(_UpperCAmelCase							,       os.path.join(training_args.output_dir							,       'test_generations.txt'))

			if trainer.is_world_process_zero():
						save_json(_UpperCAmelCase							,       os.path.join(training_args.output_dir							,       'all_results.json'))

			return all_metrics






def 				lowerCamelCase__				(_UpperCAmelCase):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()




