






import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


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

				from transformers.generation import (
				    FlaxForcedBOSTokenLogitsProcessor,
				    FlaxForcedEOSTokenLogitsProcessor,
				    FlaxLogitsProcessorList,
				    FlaxMinLengthLogitsProcessor,
				    FlaxTemperatureLogitsWarper,
				    FlaxTopKLogitsWarper,
				    FlaxTopPLogitsWarper,
				)


@require_flax
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   jnp.ones((batch_size, length)       ) / length
						return scores


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	Tuple													=   20

						SCREAMING_SNAKE_CASE_   :	Tuple													=   self._get_uniform_logits(batch_size=2 ,    length=UpperCamelCase__       )

						# tweak scores to not be uniform anymore
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   scores.at[1, 5].set((1 / length) + 0.1       )  # peak, 1st batch
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   scores.at[1, 10].set((1 / length) - 0.4       )  # valley, 1st batch

						# compute softmax
						SCREAMING_SNAKE_CASE_   :	Dict													=   jax.nn.softmax(UpperCamelCase__ ,    axis=-1       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   FlaxTemperatureLogitsWarper(temperature=0.5       )
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTemperatureLogitsWarper(temperature=1.3       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase__ ,    scores.copy() ,    cur_len=UpperCamelCase__       ) ,    axis=-1       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase__ ,    scores.copy() ,    cur_len=UpperCamelCase__       ) ,    axis=-1       )

						# uniform distribution stays uniform
						self.assertTrue(jnp.allclose(probs[0, :] ,    warped_prob_sharp[0, :] ,    atol=1E-3       )       )
						self.assertTrue(jnp.allclose(probs[0, :] ,    warped_prob_smooth[0, :] ,    atol=1E-3       )       )

						# sharp peaks get higher, valleys get lower
						self.assertLess(probs[1, :].max() ,    warped_prob_sharp[1, :].max()       )
						self.assertGreater(probs[1, :].min() ,    warped_prob_sharp[1, :].min()       )

						# smooth peaks get lower, valleys get higher
						self.assertGreater(probs[1, :].max() ,    warped_prob_smooth[1, :].max()       )
						self.assertLess(probs[1, :].min() ,    warped_prob_smooth[1, :].min()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   None
						SCREAMING_SNAKE_CASE_   :	Any													=   10
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   2

						# create ramp distribution
						SCREAMING_SNAKE_CASE_   :	Tuple													=   np.broadcast_to(np.arange(UpperCamelCase__       )[None, :] ,    (batch_size, vocab_size)       ).copy()
						SCREAMING_SNAKE_CASE_   :	Dict													=   ramp_logits[1:, : vocab_size // 2] + vocab_size

						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTopKLogitsWarper(3       )

						SCREAMING_SNAKE_CASE_   :	int													=   top_k_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# check that correct tokens are filtered
						self.assertListEqual(jnp.isinf(scores[0]       ).tolist() ,    7 * [True] + 3 * [False]       )
						self.assertListEqual(jnp.isinf(scores[1]       ).tolist() ,    2 * [True] + 3 * [False] + 5 * [True]       )

						# check special case
						SCREAMING_SNAKE_CASE_   :	Dict													=   5
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   FlaxTopKLogitsWarper(top_k=1 ,    filter_value=0.0 ,    min_tokens_to_keep=3       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   np.broadcast_to(np.arange(UpperCamelCase__       )[None, :] ,    (batch_size, length)       ).copy()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   top_k_warp_safety_check(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
						self.assertListEqual((scores == 0.0).sum(axis=-1       ).tolist() ,    [2, 2]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	str													=   10
						SCREAMING_SNAKE_CASE_   :	List[str]													=   2

						# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
						SCREAMING_SNAKE_CASE_   :	Tuple													=   np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]       )       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   FlaxTopPLogitsWarper(0.8       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   np.exp(top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )       )

						# dist should be filtered to keep min num values so that sum is >= top_p
						# exp (-inf) => 0
						SCREAMING_SNAKE_CASE_   :	str													=   np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]       )
						self.assertTrue(np.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-3       )       )

						# check edge cases with negative and extreme logits
						SCREAMING_SNAKE_CASE_   :	Tuple													=   np.broadcast_to(np.arange(UpperCamelCase__       )[None, :] ,    (batch_size, vocab_size)       ).copy() - (
						    vocab_size // 2
						)

						# make ramp_logits more extreme
						SCREAMING_SNAKE_CASE_   :	Any													=   ramp_logits[1] * 100.0

						# make sure at least 2 tokens are kept
						SCREAMING_SNAKE_CASE_   :	List[str]													=   FlaxTopPLogitsWarper(0.9 ,    min_tokens_to_keep=2 ,    filter_value=0.0       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
						self.assertListEqual((filtered_dist != 0.0).sum(axis=-1       ).tolist() ,    [3, 2]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   20
						SCREAMING_SNAKE_CASE_   :	Dict													=   4
						SCREAMING_SNAKE_CASE_   :	Dict													=   0

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   FlaxMinLengthLogitsProcessor(min_length=10 ,    eos_token_id=UpperCamelCase__       )

						# check that min length is applied at length 5
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ids_tensor((batch_size, 20) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   5
						SCREAMING_SNAKE_CASE_   :	int													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   min_dist_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,    4 * [-float('inf'       )]       )

						# check that min length is not applied anymore at length 15
						SCREAMING_SNAKE_CASE_   :	Any													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   15
						SCREAMING_SNAKE_CASE_   :	str													=   min_dist_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   20
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   4
						SCREAMING_SNAKE_CASE_   :	Dict													=   0

						SCREAMING_SNAKE_CASE_   :	str													=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__       )

						# check that all scores are -inf except the bos_token_id score
						SCREAMING_SNAKE_CASE_   :	Dict													=   ids_tensor((batch_size, 1) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	int													=   1
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]       ).all()       )
						self.assertListEqual(scores[:, bos_token_id].tolist() ,    4 * [0]       )  # score for bos_token_id shold be zero

						# check that bos_token_id is not forced if current length is greater than 1
						SCREAMING_SNAKE_CASE_   :	Dict													=   3
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   20
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   4
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   0
						SCREAMING_SNAKE_CASE_   :	int													=   5

						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )

						# check that all scores are -inf except the eos_token_id when max_length is reached
						SCREAMING_SNAKE_CASE_   :	Any													=   ids_tensor((batch_size, 4) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	int													=   4
						SCREAMING_SNAKE_CASE_   :	str													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]       ).all()       )
						self.assertListEqual(scores[:, eos_token_id].tolist() ,    4 * [0]       )  # score for eos_token_id should be zero

						# check that eos_token_id is not forced if max_length is not reached
						SCREAMING_SNAKE_CASE_   :	List[str]													=   3
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   4
						SCREAMING_SNAKE_CASE_   :	Any													=   10
						SCREAMING_SNAKE_CASE_   :	List[str]													=   15
						SCREAMING_SNAKE_CASE_   :	Dict													=   2
						SCREAMING_SNAKE_CASE_   :	List[str]													=   1
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   15

						# dummy input_ids and scores
						SCREAMING_SNAKE_CASE_   :	int													=   ids_tensor((batch_size, sequence_length) ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   input_ids.copy()

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   scores.copy()

						# instantiate all dist processors
						SCREAMING_SNAKE_CASE_   :	str													=   FlaxTemperatureLogitsWarper(temperature=0.5       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   FlaxTopKLogitsWarper(3       )
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTopPLogitsWarper(0.8       )

						# instantiate all logits processors
						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxMinLengthLogitsProcessor(min_length=10 ,    eos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   10

						# no processor list
						SCREAMING_SNAKE_CASE_   :	Tuple													=   temp_dist_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   top_k_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   min_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   bos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   eos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# with processor list
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxLogitsProcessorList(
						    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# scores should be equal
						self.assertTrue(jnp.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-3       )       )

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist() ,    input_ids_comp.tolist()       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   4
						SCREAMING_SNAKE_CASE_   :	List[str]													=   10
						SCREAMING_SNAKE_CASE_   :	str													=   15
						SCREAMING_SNAKE_CASE_   :	int													=   2
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   1
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   15

						# dummy input_ids and scores
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ids_tensor((batch_size, sequence_length) ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   input_ids.copy()

						SCREAMING_SNAKE_CASE_   :	str													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   scores.copy()

						# instantiate all dist processors
						SCREAMING_SNAKE_CASE_   :	Tuple													=   FlaxTemperatureLogitsWarper(temperature=0.5       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   FlaxTopKLogitsWarper(3       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   FlaxTopPLogitsWarper(0.8       )

						# instantiate all logits processors
						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxMinLengthLogitsProcessor(min_length=10 ,    eos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   10

						# no processor list
						def run_no_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Dict													=   temp_dist_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Tuple													=   top_k_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	int													=   top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Any													=   min_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Any													=   bos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   eos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										return scores

						# with processor list
						def run_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   FlaxLogitsProcessorList(
										    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										return scores

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   jax.jit(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   jax.jit(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   jitted_run_no_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   jitted_run_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )

						# scores should be equal
						self.assertTrue(jnp.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-3       )       )

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist() ,    input_ids_comp.tolist()       )







import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


A        =			logging.getLogger(__name__)

A        =			tf.data.AUTOTUNE



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   argparse.ArgumentParser(description='Train a masked language model on TPU.'      )
				parser.add_argument(
				    '--pretrained_model_config'    ,    type=lowerCAmelCase__    ,    default='roberta-base'    ,    help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'    ,    )
				parser.add_argument(
				    '--tokenizer'    ,    type=lowerCAmelCase__    ,    default='unigram-tokenizer-wikitext'    ,    help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'    ,    )

				parser.add_argument(
				    '--per_replica_batch_size'    ,    type=lowerCAmelCase__    ,    default=8    ,    help='Batch size per TPU core.'    ,    )

				parser.add_argument(
				    '--no_tpu'    ,    action='store_true'    ,    help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'    ,    )

				parser.add_argument(
				    '--tpu_name'    ,    type=lowerCAmelCase__    ,    help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'    ,    default='local'    ,    )

				parser.add_argument(
				    '--tpu_zone'    ,    type=lowerCAmelCase__    ,    help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'    ,    )

				parser.add_argument(
				    '--gcp_project'    ,    type=lowerCAmelCase__    ,    help='Google cloud project name. Only used for non-Colab TPU nodes.'      )

				parser.add_argument(
				    '--bfloat16'    ,    action='store_true'    ,    help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'    ,    )

				parser.add_argument(
				    '--train_dataset'    ,    type=lowerCAmelCase__    ,    help='Path to training dataset to load. If the path begins with `gs://`'
				    ' then the dataset will be loaded from a Google Cloud Storage bucket.'    ,    )

				parser.add_argument(
				    '--shuffle_buffer_size'    ,    type=lowerCAmelCase__    ,    default=2**18    ,    help='Size of the shuffle buffer (in samples)'    ,    )

				parser.add_argument(
				    '--eval_dataset'    ,    type=lowerCAmelCase__    ,    help='Path to evaluation dataset to load. If the path begins with `gs://`'
				    ' then the dataset will be loaded from a Google Cloud Storage bucket.'    ,    )

				parser.add_argument(
				    '--num_epochs'    ,    type=lowerCAmelCase__    ,    default=1    ,    help='Number of epochs to train for.'    ,    )

				parser.add_argument(
				    '--learning_rate'    ,    type=lowerCAmelCase__    ,    default=1E-4    ,    help='Learning rate to use for training.'    ,    )

				parser.add_argument(
				    '--weight_decay_rate'    ,    type=lowerCAmelCase__    ,    default=1E-3    ,    help='Weight decay rate to use for training.'    ,    )

				parser.add_argument(
				    '--max_length'    ,    type=lowerCAmelCase__    ,    default=512    ,    help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'    ,    )

				parser.add_argument(
				    '--mlm_probability'    ,    type=lowerCAmelCase__    ,    default=0.15    ,    help='Fraction of tokens to mask during training.'    ,    )

				parser.add_argument('--output_dir'    ,    type=lowerCAmelCase__    ,    required=lowerCAmelCase__    ,    help='Path to save model checkpoints to.'      )
				parser.add_argument('--hub_model_id'    ,    type=lowerCAmelCase__    ,    help='Model ID to upload to on the Hugging Face Hub.'      )

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   parser.parse_args()
				return args



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				try:
								if args.tpu_name:
												SCREAMING_SNAKE_CASE_   :	Dict													=   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,    zone=args.tpu_zone    ,    project=args.gcp_project      )
								else:
												SCREAMING_SNAKE_CASE_   :	Any													=   tf.distribute.cluster_resolver.TPUClusterResolver()
				except ValueError:
								raise RuntimeError(
								    'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
								    '--gcp_project. When running on a TPU VM, use --tpu_name local.'      )

				tf.config.experimental_connect_to_cluster(lowerCAmelCase__      )
				tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase__      )

				return tpu



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0
				for file in file_list:
								SCREAMING_SNAKE_CASE_   :	Dict													=   file.split('/'      )[-1]
								SCREAMING_SNAKE_CASE_   :	Any													=   re.search(R'-\d+-(\d+)\.tfrecord'    ,    lowerCAmelCase__      ).group(1      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   int(lowerCAmelCase__      )
								num_samples += sample_count

				return num_samples



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]=None      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   count_samples(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   tf.data.Dataset.from_tensor_slices(lowerCAmelCase__      )
				if shuffle:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   dataset.shuffle(len(lowerCAmelCase__      )      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tf.data.TFRecordDataset(lowerCAmelCase__    ,    num_parallel_reads=lowerCAmelCase__      )
				# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   dataset.apply(tf.data.experimental.assert_cardinality(lowerCAmelCase__      )      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   dataset.map(lowerCAmelCase__    ,    num_parallel_calls=lowerCAmelCase__      )
				if shuffle:
								assert shuffle_buffer_size is not None
								SCREAMING_SNAKE_CASE_   :	Any													=   dataset.shuffle(args.shuffle_buffer_size      )
				SCREAMING_SNAKE_CASE_   :	str													=   dataset.batch(lowerCAmelCase__    ,    drop_remainder=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Any													=   dataset.map(lowerCAmelCase__    ,    num_parallel_calls=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   dataset.prefetch(lowerCAmelCase__      )
				return dataset



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if not args.no_tpu:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   initialize_tpu(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	str													=   tf.distribute.TPUStrategy(lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   tf.distribute.OneDeviceStrategy(device='/gpu:0'      )

				if args.bfloataa:
								tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'      )

				SCREAMING_SNAKE_CASE_   :	str													=   AutoTokenizer.from_pretrained(args.tokenizer      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   AutoConfig.from_pretrained(args.pretrained_model_config      )
				SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.vocab_size

				SCREAMING_SNAKE_CASE_   :	str													=   tf.io.gfile.glob(os.path.join(args.train_dataset    ,    '*.tfrecord'      )      )
				if not training_records:
								raise ValueError(F'''No .tfrecord files found in {args.train_dataset}.'''      )
				SCREAMING_SNAKE_CASE_   :	str													=   tf.io.gfile.glob(os.path.join(args.eval_dataset    ,    '*.tfrecord'      )      )
				if not eval_records:
								raise ValueError(F'''No .tfrecord files found in {args.eval_dataset}.'''      )

				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   count_samples(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	int													=   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   steps_per_epoch * args.num_epochs

				with strategy.scope():
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   TFAutoModelForMaskedLM.from_config(lowerCAmelCase__      )
								model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   create_optimizer(
								    num_train_steps=lowerCAmelCase__    ,    num_warmup_steps=total_train_steps // 20    ,    init_lr=args.learning_rate    ,    weight_decay_rate=args.weight_decay_rate    ,    )

								# Transformers models compute the right loss for their task by default when labels are passed, and will
								# use this for training unless you specify your own loss function in compile().
								model.compile(optimizer=lowerCAmelCase__    ,    metrics=['accuracy']      )

				def decode_fn(lowerCAmelCase__				:		Optional[int]      ):
								SCREAMING_SNAKE_CASE_   :	int													=   {
								    'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa    ,    shape=(args.max_length,)      ),
								    'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa    ,    shape=(args.max_length,)      ),
								}
								return tf.io.parse_single_example(lowerCAmelCase__    ,    lowerCAmelCase__      )

				# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
				# use their methods in our data pipeline.
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   DataCollatorForLanguageModeling(
				    tokenizer=lowerCAmelCase__    ,    mlm_probability=args.mlm_probability    ,    mlm=lowerCAmelCase__    ,    return_tensors='tf'      )

				def mask_with_collator(lowerCAmelCase__				:		Optional[Any]      ):
								# TF really needs an isin() function
								SCREAMING_SNAKE_CASE_   :	Tuple													=   (
								    ~tf.cast(batch['attention_mask']    ,    tf.bool      )
								    | (batch['input_ids'] == tokenizer.cls_token_id)
								    | (batch['input_ids'] == tokenizer.sep_token_id)
								)
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   data_collator.tf_mask_tokens(
								    batch['input_ids']    ,    vocab_size=len(lowerCAmelCase__      )    ,    mask_token_id=tokenizer.mask_token_id    ,    special_tokens_mask=lowerCAmelCase__    ,    )
								return batch

				SCREAMING_SNAKE_CASE_   :	Any													=   args.per_replica_batch_size * strategy.num_replicas_in_sync

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    shuffle_buffer_size=args.shuffle_buffer_size    ,    )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    )

				SCREAMING_SNAKE_CASE_   :	str													=   []
				if args.hub_model_id:
								callbacks.append(
								    PushToHubCallback(output_dir=args.output_dir    ,    hub_model_id=args.hub_model_id    ,    tokenizer=lowerCAmelCase__      )      )

				model.fit(
				    lowerCAmelCase__    ,    validation_data=lowerCAmelCase__    ,    epochs=args.num_epochs    ,    callbacks=lowerCAmelCase__    ,    )

				model.save_pretrained(args.output_dir      )


if __name__ == "__main__":
				A        =			parse_args()
				main(args)