






from abc import ABC, abstractmethod
from typing import List, Optional

class 		_snake_case  (  A__					):






			def __init__(       self)      ->       int:
						# test for the above condition
						self.test()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= False
						while not completed:
									if counter == 1:
												self.reset()
									SCREAMING_SNAKE_CASE						= self.advance()
									if not self.does_advance(a):
												raise Exception(
												    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.')

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.update(a)
									counter += 1

									if counter > 1_0000:
												raise Exception('update() does not fulfill the constraint.')

						if self.remaining() != 0:
									raise Exception('Custom Constraint is not defined correctly.')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')







			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       int:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')



class 		_snake_case  (  A__					):






			def __init__(       self		,					a)      ->       Dict:
						super(a		,					self).__init__()

						if not isinstance(a		,					a) or len(a) == 0:
									raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''')
						if any((not isinstance(a		,					a) or token_id < 0) for token_id in token_ids):
									raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''')

						SCREAMING_SNAKE_CASE						= token_ids

						SCREAMING_SNAKE_CASE						= len(self.token_ids)
						SCREAMING_SNAKE_CASE						= -1  # the index of the currently fulfilled step
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						if self.completed:
									return None
						return self.token_ids[self.fulfilled_idx + 1]






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a)}''')

						if self.completed:
									return False

						return token_id == self.token_ids[self.fulfilled_idx + 1]






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

						if self.does_advance(a):
									self.fulfilled_idx += 1
									SCREAMING_SNAKE_CASE						= True
									if self.fulfilled_idx == (self.seqlen - 1):
												SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= completed
						else:
									# failed to make progress.
									SCREAMING_SNAKE_CASE						= True
									self.reset()
						return stepped, completed, reset






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= 0






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return self.seqlen - (self.fulfilled_idx + 1)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       int:
						SCREAMING_SNAKE_CASE						= PhrasalConstraint(self.token_ids)

						if stateful:
									SCREAMING_SNAKE_CASE						= self.seqlen
									SCREAMING_SNAKE_CASE						= self.fulfilled_idx
									SCREAMING_SNAKE_CASE						= self.completed

						return new_constraint



class 		_snake_case  :






			def __init__(       self		,					a		,					a=True)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= max([len(a) for one in nested_token_ids])

						SCREAMING_SNAKE_CASE						= {}
						for token_ids in nested_token_ids:
									SCREAMING_SNAKE_CASE						= root
									for tidx, token_id in enumerate(a):
												if token_id not in level:
															SCREAMING_SNAKE_CASE						= {}

												SCREAMING_SNAKE_CASE						= level[token_id]

						if no_subsets and self.has_subsets(a		,					a):
									raise ValueError(
									    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
									    f''' {nested_token_ids}.''')

						SCREAMING_SNAKE_CASE						= root






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

						for current_token in current_seq:
									SCREAMING_SNAKE_CASE						= start[current_token]

						SCREAMING_SNAKE_CASE						= list(start.keys())

						return next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= self.next_tokens(a)

						return len(a) == 0






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= list(root.values())
						if len(a) == 0:
									return 1
						else:
									return sum([self.count_leaves(a) for nn in next_nodes])







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.count_leaves(a)
						return len(a) != leaf_count



class 		_snake_case  (  A__					):






			def __init__(       self		,					a)      ->       Dict:
						super(a		,					self).__init__()

						if not isinstance(a		,					a) or len(a) == 0:
									raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''')
						if any(not isinstance(a		,					a) for token_ids in nested_token_ids):
									raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''')
						if any(
						    any((not isinstance(a		,					a) or token_id < 0) for token_id in token_ids)
						    for token_ids in nested_token_ids):
									raise ValueError(
									    f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''')

						SCREAMING_SNAKE_CASE						= DisjunctiveTrie(a)
						SCREAMING_SNAKE_CASE						= nested_token_ids

						SCREAMING_SNAKE_CASE						= self.trie.max_height
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.trie.next_tokens(self.current_seq)

						if len(a) == 0:
									return None
						else:
									return token_list






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= self.trie.next_tokens(self.current_seq)

						return token_id in next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

						if self.does_advance(a):
									self.current_seq.append(a)
									SCREAMING_SNAKE_CASE						= True
						else:
									SCREAMING_SNAKE_CASE						= True
									self.reset()

						SCREAMING_SNAKE_CASE						= self.trie.reached_leaf(self.current_seq)
						SCREAMING_SNAKE_CASE						= completed

						return stepped, completed, reset






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= []






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						if self.completed:
									# since this can be completed without reaching max height
									return 0
						else:
									return self.seqlen - len(self.current_seq)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= DisjunctiveConstraint(self.token_ids)

						if stateful:
									SCREAMING_SNAKE_CASE						= self.seqlen
									SCREAMING_SNAKE_CASE						= self.current_seq
									SCREAMING_SNAKE_CASE						= self.completed

						return new_constraint



class 		_snake_case  :






			def __init__(       self		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= constraints

						# max # of steps required to fulfill a given constraint
						SCREAMING_SNAKE_CASE						= max([c.seqlen for c in constraints])
						SCREAMING_SNAKE_CASE						= len(a)
						SCREAMING_SNAKE_CASE						= False

						self.init_state()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= [constraint.copy(stateful=a) for constraint in self.constraints]






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= 0
						if self.inprogress_constraint:
									# extra points for having a constraint mid-fulfilled
									add += self.max_seqlen - self.inprogress_constraint.remaining()

						return (len(self.complete_constraints) * self.max_seqlen) + add






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= []
						if self.inprogress_constraint is None:
									for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
												SCREAMING_SNAKE_CASE						= constraint.advance()
												if isinstance(a		,					a):
															token_list.append(a)
												elif isinstance(a		,					a):
															token_list.extend(a)
						else:
									SCREAMING_SNAKE_CASE						= self.inprogress_constraint.advance()
									if isinstance(a		,					a):
												token_list.append(a)
									elif isinstance(a		,					a):
												token_list.extend(a)

						if len(a) == 0:
									return None
						else:
									return token_list






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						self.init_state()

						if token_ids is not None:
									for token in token_ids:
												# completes or steps **one** constraint
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.add(a)

												# the entire list of constraints are fulfilled
												if self.completed:
															break






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''')

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= False, False

						if self.completed:
									SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= False
									return complete, stepped

						if self.inprogress_constraint is not None:
									# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
									# job, simply update the state

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.inprogress_constraint.update(a)
									if reset:
												# 1. If the next token breaks the progress, then we must restart.
												#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

												#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
												#     constraint, not the full list of constraints.

												self.pending_constraints.append(self.inprogress_constraint.copy(stateful=a))
												SCREAMING_SNAKE_CASE						= None

									if complete:
												# 2. If the next token completes the constraint, move it to completed list, set
												#     inprogress to None. If there are no pending constraints either, then this full list of constraints
												#     is complete.

												self.complete_constraints.append(self.inprogress_constraint)
												SCREAMING_SNAKE_CASE						= None

												if len(self.pending_constraints) == 0:
															# we're done!
															SCREAMING_SNAKE_CASE						= True

						else:
									# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
									# of constraints?

									for cidx, pending_constraint in enumerate(self.pending_constraints):
												if pending_constraint.does_advance(a):
															SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pending_constraint.update(a)

															if not stepped:
																		raise Exception(
																		    '`constraint.update(token_id)` is not yielding incremental progress, '
																		    'even though `constraint.does_advance(token_id)` is true.')

															if complete:
																		self.complete_constraints.append(a)
																		SCREAMING_SNAKE_CASE						= None

															if not complete and stepped:
																		SCREAMING_SNAKE_CASE						= pending_constraint

															if complete or stepped:
																		# If we made any progress at all, then it's at least not a "pending constraint".

																		SCREAMING_SNAKE_CASE						= (
																		    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																		)

																		if len(self.pending_constraints) == 0 and self.inprogress_constraint is None:
																					# If there's no longer any pending after this and no inprogress either, then we must be
																					# complete.

																					SCREAMING_SNAKE_CASE						= True

																		break  # prevent accidentally stepping through multiple constraints with just one token.

						return complete, stepped







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=True)      ->       str:
						SCREAMING_SNAKE_CASE						= ConstraintListState(self.constraints)  # we actually never though self.constraints objects
						# throughout this process. So it's at initialization state.

						if stateful:
									SCREAMING_SNAKE_CASE						= [
									    constraint.copy(stateful=a) for constraint in self.complete_constraints
									]
									if self.inprogress_constraint is not None:
												SCREAMING_SNAKE_CASE						= self.inprogress_constraint.copy(stateful=a)
									SCREAMING_SNAKE_CASE						= [constraint.copy() for constraint in self.pending_constraints]

						return new_state












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()




