

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


A_       : Optional[Any]         =			random.Random()

def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: List[str]=1.0    ,      lowercase_: Optional[Any]=None    ,      lowercase_: Dict=None					)     ->      Union[str, Any]:
	if rng is None:
		A__							:    Tuple        =		global_rng

	A__							:    Dict        =		[]
	for batch_idx in range(shape[0]					):
		values.append([]					)
		for _ in range(shape[1]					):
			values[-1].append(rng.random() * scale					)

	return values



@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=400    ,       A__=2000    ,       A__=1    ,       A__=0.0    ,       A__=1_6000    ,       A__=True    ,       A__=80    ,       A__=16    ,       A__=64    ,       A__="hann_window"    ,       A__=80    ,       A__=7600    ,       A__=1e-10    ,       A__=True    ,       ):
						A__							:    List[str]        =		parent
						A__							:    Union[str, Any]        =		batch_size
						A__							:    Union[str, Any]        =		min_seq_length
						A__							:    Tuple        =		max_seq_length
						A__							:    List[Any]        =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A__							:    List[Any]        =		feature_size
						A__							:    Union[str, Any]        =		padding_value
						A__							:    Tuple        =		sampling_rate
						A__							:    Optional[Any]        =		do_normalize
						A__							:    Dict        =		num_mel_bins
						A__							:    int        =		hop_length
						A__							:    Optional[int]        =		win_length
						A__							:    Tuple        =		win_function
						A__							:    Union[str, Any]        =		fmin
						A__							:    Any        =		fmax
						A__							:    Tuple        =		mel_floor
						A__							:    Optional[Any]        =		return_attention_mask






					def   __A			( self			):
						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "do_normalize": self.do_normalize,
						    "num_mel_bins": self.num_mel_bins,
						    "hop_length": self.hop_length,
						    "win_length": self.win_length,
						    "win_function": self.win_function,
						    "fmin": self.fmin,
						    "fmax": self.fmax,
						    "mel_floor": self.mel_floor,
						    "return_attention_mask": self.return_attention_mask,
						}






					def   __A			( self    ,       A__=False    ,       A__=False			):
						def _flatten(A__			):
							return list(itertools.chain(*A__			)			)

						if equal_length:
							A__							:    Any        =		floats_list((self.batch_size, self.max_seq_length)			)
						else:
							# make sure that inputs increase in size
							A__							:    int        =		[
							    _flatten(floats_list((x, self.feature_size)			)			)
							    for x in range(self.min_seq_length    ,       self.max_seq_length    ,       self.seq_length_diff			)
							]

						if numpify:
							A__							:    Optional[int]        =		[np.asarray(A__			) for x in speech_inputs]

						return speech_inputs






					def   __A			( self    ,       A__=False    ,       A__=False			):
						if equal_length:
							A__							:    Tuple        =		[floats_list((self.max_seq_length, self.num_mel_bins)			) for _ in range(self.batch_size			)]
						else:
							# make sure that inputs increase in size
							A__							:    Optional[int]        =		[
							    floats_list((x, self.num_mel_bins)			)
							    for x in range(self.min_seq_length    ,       self.max_seq_length    ,       self.seq_length_diff			)
							]

						if numpify:
							A__							:    Optional[Any]        =		[np.asarray(A__			) for x in speech_inputs]

						return speech_inputs



@require_torch
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Any  							=       SpeechTaFeatureExtractor






					def   __A			( self			):
						A__							:    List[Any]        =		SpeechTaFeatureExtractionTester(self			)






					def   __A			( self    ,       A__			):
						self.assertTrue(np.all(np.mean(A__    ,       axis=0			) < 1e-3			)			)
						self.assertTrue(np.all(np.abs(np.var(A__    ,       axis=0			) - 1			) < 1e-3			)			)






					def   __A			( self			):
						A__							:    Any        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						# create three inputs of length 800, 1000, and 1200
						A__							:    Dict        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Union[str, Any]        =		[np.asarray(A__			) for speech_input in speech_inputs]

						# Test not batched input
						A__							:    List[str]        =		feat_extract(speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						A__							:    Optional[Any]        =		feat_extract(np_speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)

						# Test batched
						A__							:    List[Any]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						A__							:    Optional[Any]        =		feat_extract(A__    ,       return_tensors="""np"""			).input_values
						for enc_seq_a, enc_seq_a in zip(A__    ,       A__			):
							self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)






					def   __A			( self			):
						A__							:    Any        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    int        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]

						A__							:    Optional[Any]        =		["""longest""", """max_length""", """do_not_pad"""]
						A__							:    Optional[int]        =		[None, 1600, None]
						for max_length, padding in zip(A__    ,       A__			):
							A__							:    Tuple        =		feat_extract(A__    ,       padding=A__    ,       max_length=A__    ,       return_tensors="""np"""			)
							A__							:    str        =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800]			)
							self.assertTrue(input_values[0][800:].sum() < 1e-6			)
							self._check_zero_mean_unit_variance(input_values[1][:1000]			)
							self.assertTrue(input_values[0][1000:].sum() < 1e-6			)
							self._check_zero_mean_unit_variance(input_values[2][:1200]			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    List[Any]        =		range(800    ,       1400    ,       200			)
						A__							:    int        =		[floats_list((1, x)			)[0] for x in lengths]

						A__							:    Optional[int]        =		["""longest""", """max_length""", """do_not_pad"""]
						A__							:    List[Any]        =		[None, 1600, None]

						for max_length, padding in zip(A__    ,       A__			):
							A__							:    Optional[Any]        =		feat_extract(A__    ,       max_length=A__    ,       padding=A__			)
							A__							:    List[Any]        =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800]			)
							self._check_zero_mean_unit_variance(input_values[1][:1000]			)
							self._check_zero_mean_unit_variance(input_values[2][:1200]			)






					def   __A			( self			):
						A__							:    Optional[int]        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    List[Any]        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Tuple        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=1000    ,       padding="""max_length"""    ,       return_tensors="""np"""			)
						A__							:    Tuple        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1]			)
						self._check_zero_mean_unit_variance(input_values[2]			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    Optional[int]        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Dict        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=1000    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						A__							:    List[Any]        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1, :1000]			)
						self._check_zero_mean_unit_variance(input_values[2]			)

						# make sure that if max_length < longest -> then pad to max_length
						self.assertTrue(input_values.shape == (3, 1000)			)

						A__							:    Any        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    Optional[int]        =		feat_extract(
						    A__    ,       truncation=A__    ,       max_length=2000    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						A__							:    Optional[Any]        =		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]			)
						self._check_zero_mean_unit_variance(input_values[1, :1000]			)
						self._check_zero_mean_unit_variance(input_values[2]			)

						# make sure that if max_length > longest -> then pad to longest
						self.assertTrue(input_values.shape == (3, 1200)			)






					def   __A			( self			):
						A__							:    str        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						A__							:    Tuple        =		np.random.rand(100			).astype(np.floataa			)
						A__							:    Optional[int]        =		np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
							A__							:    Tuple        =		feature_extractor.pad([{"""input_values""": inputs}]    ,       return_tensors="""np"""			)
							self.assertTrue(np_processed.input_values.dtype == np.floataa			)
							A__							:    Dict        =		feature_extractor.pad([{"""input_values""": inputs}]    ,       return_tensors="""pt"""			)
							self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)






					def   __A			( self			):
						A__							:    List[Any]        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
						# create three inputs of length 800, 1000, and 1200
						A__							:    str        =		[floats_list((1, x)			)[0] for x in range(800    ,       1400    ,       200			)]
						A__							:    List[str]        =		[np.asarray(A__			) for speech_input in speech_inputs]

						# Test feature size
						A__							:    Optional[Any]        =		feature_extractor(audio_target=A__    ,       padding=A__    ,       return_tensors="""np"""			).input_values
						self.assertTrue(input_values.ndim == 3			)
						self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins			)

						# Test not batched input
						A__							:    Optional[Any]        =		feature_extractor(speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						A__							:    int        =		feature_extractor(np_speech_inputs[0]    ,       return_tensors="""np"""			).input_values
						self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)

						# Test batched
						A__							:    Optional[int]        =		feature_extractor(A__    ,       return_tensors="""np"""			).input_values
						A__							:    Union[str, Any]        =		feature_extractor(A__    ,       return_tensors="""np"""			).input_values
						for enc_seq_a, enc_seq_a in zip(A__    ,       A__			):
							self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)

						# Test 2-D numpy arrays are batched.
						A__							:    Dict        =		[floats_list((1, x)			)[0] for x in (800, 800, 800)]
						A__							:    int        =		np.asarray(A__			)
						A__							:    Optional[Any]        =		feature_extractor(A__    ,       return_tensors="""np"""			).input_values
						A__							:    List[Any]        =		feature_extractor(A__    ,       return_tensors="""np"""			).input_values
						for enc_seq_a, enc_seq_a in zip(A__    ,       A__			):
							self.assertTrue(np.allclose(A__    ,       A__    ,       atol=1e-3			)			)






					def   __A			( self			):
						A__							:    Optional[Any]        =		self.feat_extract_tester.prepare_inputs_for_target()
						A__							:    List[Any]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Optional[int]        =		feat_extract.model_input_names[0]

						A__							:    Tuple        =		BatchFeature({input_name: speech_inputs}			)

						self.assertTrue(all(len(A__			) == len(A__			) for x, y in zip(A__    ,       processed_features[input_name]			)			)			)

						A__							:    Any        =		self.feat_extract_tester.prepare_inputs_for_target(equal_length=A__			)
						A__							:    Optional[int]        =		BatchFeature({input_name: speech_inputs}    ,       tensor_type="""np"""			)

						A__							:    Tuple        =		processed_features[input_name]

						if len(batch_features_input.shape			) < 3:
							A__							:    Any        =		batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.num_mel_bins)			)






					@require_torch
					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.feat_extract_tester.prepare_inputs_for_target(equal_length=A__			)
						A__							:    int        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Union[str, Any]        =		feat_extract.model_input_names[0]

						A__							:    Dict        =		BatchFeature({input_name: speech_inputs}    ,       tensor_type="""pt"""			)

						A__							:    Dict        =		processed_features[input_name]

						if len(batch_features_input.shape			) < 3:
							A__							:    int        =		batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.num_mel_bins)			)






					@require_torch
					def   __A			( self			):
						A__							:    Dict        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Union[str, Any]        =		self.feat_extract_tester.prepare_inputs_for_target()
						A__							:    int        =		feat_extract.model_input_names[0]

						A__							:    Union[str, Any]        =		BatchFeature({input_name: speech_inputs}			)

						A__							:    Any        =		feat_extract.num_mel_bins  # hack!

						A__							:    Tuple        =		feat_extract.pad(A__    ,       padding="""longest"""    ,       return_tensors="""np"""			)[input_name]
						A__							:    Optional[int]        =		feat_extract.pad(A__    ,       padding="""longest"""    ,       return_tensors="""pt"""			)[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_pt.numpy().astype(np.floataa			).sum()			) < 1e-2			)






					def   __A			( self			):
						A__							:    List[str]        =		self.feat_extract_dict
						A__							:    List[Any]        =		True
						A__							:    int        =		self.feature_extraction_class(**A__			)
						A__							:    Union[str, Any]        =		self.feat_extract_tester.prepare_inputs_for_target()
						A__							:    Dict        =		[len(A__			) for x in speech_inputs]
						A__							:    Union[str, Any]        =		feat_extract.model_input_names[0]

						A__							:    str        =		BatchFeature({input_name: speech_inputs}			)

						A__							:    Union[str, Any]        =		feat_extract.num_mel_bins  # hack!

						A__							:    List[str]        =		feat_extract.pad(A__    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						self.assertIn("""attention_mask"""    ,       A__			)
						self.assertListEqual(list(processed.attention_mask.shape			)    ,       list(processed[input_name].shape[:2]			)			)
						self.assertListEqual(processed.attention_mask.sum(-1			).tolist()    ,       A__			)






					def   __A			( self			):
						A__							:    Optional[Any]        =		self.feat_extract_dict
						A__							:    str        =		True
						A__							:    Optional[int]        =		self.feature_extraction_class(**A__			)
						A__							:    Optional[int]        =		self.feat_extract_tester.prepare_inputs_for_target()
						A__							:    Dict        =		[len(A__			) for x in speech_inputs]
						A__							:    Optional[Any]        =		feat_extract.model_input_names[0]

						A__							:    Dict        =		BatchFeature({input_name: speech_inputs}			)
						A__							:    List[Any]        =		min(A__			)

						A__							:    Union[str, Any]        =		feat_extract.num_mel_bins  # hack!

						A__							:    Optional[Any]        =		feat_extract.pad(
						    A__    ,       padding="""max_length"""    ,       max_length=A__    ,       truncation=A__    ,       return_tensors="""np"""			)
						self.assertIn("""attention_mask"""    ,       A__			)
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape			)    ,       [processed_pad[input_name].shape[0], max_length]			)
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1			).tolist()    ,       [max_length for x in speech_inputs]			)






					def   __A			( self    ,       A__			):
						from datasets import load_dataset

						A__							:    Tuple        =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,       """clean"""    ,       split="""validation"""			)
						# automatic decoding with librispeech
						A__							:    Any        =		ds.sort("""id"""			).select(range(A__			)			)[:num_samples]["""audio"""]

						return [x["array"] for x in speech_samples]






					def   __A			( self			):
						A__							:    Optional[Any]        =		torch.tensor(
						    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
						     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
						     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
						     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
						     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
						     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]			)
						# fmt: on

						A__							:    Any        =		self._load_datasamples(1			)
						A__							:    Tuple        =		SpeechTaFeatureExtractor()
						A__							:    Optional[int]        =		feature_extractor(A__    ,       return_tensors="""pt"""			).input_values
						self.assertEquals(input_values.shape    ,       (1, 9_3680)			)
						self.assertTrue(torch.allclose(input_values[0, :30]    ,       A__    ,       atol=1e-6			)			)






					def   __A			( self			):
						A__							:    str        =		torch.tensor(
						    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
						     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
						     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
						     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]			)
						# fmt: on

						A__							:    int        =		self._load_datasamples(1			)
						A__							:    str        =		SpeechTaFeatureExtractor()
						A__							:    List[Any]        =		feature_extractor(audio_target=A__    ,       return_tensors="""pt"""			).input_values
						self.assertEquals(input_values.shape    ,       (1, 366, 80)			)
						self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,       A__    ,       atol=1e-4			)			)







'''simple docstring'''

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    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Whether tp freeze the encoder."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Whether  to freeze the embeddings."}			)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
  _lowerCamelCase		    =  field(
      default="summarization"			,						metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}			,						)
  _lowerCamelCase		    =  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."
          )
      }			,						)
  _lowerCamelCase		    =  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."
          )
      }			,						)
  _lowerCamelCase		    =  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``."
          )
      }			,						)
  _lowerCamelCase		    =  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."
          )
      }			,						)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# training examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# validation examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# test examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Source language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Target language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "# num_beams to use for evaluation."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,						)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 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 __snake_case					(      ):
 # 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.

 lowerCamelCase_      =  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.
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  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.

 lowerCamelCase_      =  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					,				)

 lowerCamelCase_      =  ("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_		)		)

 lowerCamelCase_      =  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					,				)
 lowerCamelCase_      =  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:
  lowerCamelCase_      =  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_		):
   lowerCamelCase_      =  tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   lowerCamelCase_      =  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()		)

 lowerCamelCase_      =  SeqaSeqDataset

 # Get datasets
 lowerCamelCase_      =  (
     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
 )
 lowerCamelCase_      =  (
     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
 )
 lowerCamelCase_      =  (
     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
 lowerCamelCase_      =  (
     build_compute_metrics_fn(data_args.task					,				UpperCAmelCase_		) if training_args.predict_with_generate else None
 )
 lowerCamelCase_      =  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_					,				)

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

  lowerCamelCase_      =  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
  lowerCamelCase_      =  train_result.metrics
  lowerCamelCase_      =  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 ***"		)

  lowerCamelCase_      =  trainer.evaluate(metric_key_prefix="val"		)
  lowerCamelCase_      =  data_args.n_val
  lowerCamelCase_      =  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 ***"		)

  lowerCamelCase_      =  trainer.predict(test_dataset=UpperCAmelCase_					,				metric_key_prefix="test"		)
  lowerCamelCase_      =  test_output.metrics
  lowerCamelCase_      =  data_args.n_test

  if trainer.is_world_process_zero():
   lowerCamelCase_      =  round(metrics["test_loss"]					,				4		)
   handle_metrics("test"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

   if training_args.predict_with_generate:
    lowerCamelCase_      =  tokenizer.batch_decode(
        test_output.predictions					,				skip_special_tokens=UpperCAmelCase_					,				clean_up_tokenization_spaces=UpperCAmelCase_		)
    lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						Dict		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()



