


import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


lowerCamelCase											=       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark




@dataclass
class A  (   datasets.BuilderConfig     ):
      UpperCamelCase__							:  Optional[datasets.Features]      =None




def 	a_      (    SCREAMING_SNAKE_CASE__					:      "pyspark.sql.DataFrame"		,			SCREAMING_SNAKE_CASE__					:      List[int]		,			):
       '''simple docstring'''





       import pyspark

       def generate_fn():
              _lowerCamelCase       :		str		=df.select('*'		,			pyspark.sql.functions.spark_partition_id().alias('part_id'     )     )
              for partition_id in partition_order:
                     _lowerCamelCase       :		List[str]		=df_with_partition_id.select('*'     ).where(F'''part_id = {partition_id}'''     ).drop('part_id'     )
                     _lowerCamelCase       :		Tuple		=partition_df.collect()
                     _lowerCamelCase       :		int		=0
                     for row in rows:
                            yield F'''{partition_id}_{row_id}''', row.asDict()
                            row_id += 1

       return generate_fn




class A  (   _BaseExamplesIterable     ):






      def __init__(					self						:     Dict			,   lowercase_						:     "pyspark.sql.DataFrame"			,   lowercase_						:     Optional[Any]=None			,   )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=df
             _lowerCamelCase       :		int		=partition_order or range(self.df.rdd.getNumPartitions() )
             _lowerCamelCase       :		Optional[Any]		=_generate_iterable_examples(self.df			,   self.partition_order )






      def __iter__(					self						:     List[str] )					->						List[str]:
             """simple docstring"""




             yield from self.generate_examples_fn()






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     np.random.Generator )					->						"SparkExamplesIterable":
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=list(range(self.df.rdd.getNumPartitions() ) )
             generator.shuffle(lowercase_ )
             return SparkExamplesIterable(self.df			,   partition_order=lowercase_ )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     int			,   lowercase_						:     int )					->						"SparkExamplesIterable":
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.split_shard_indices_by_worker(lowercase_			,   lowercase_ )
             return SparkExamplesIterable(self.df			,   partition_order=lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             return len(self.partition_order )




class A  (   datasets.DatasetBuilder     ):
      UpperCamelCase__							:  str      =SparkConfig






      def __init__(					self						:     List[str]			,   lowercase_						:     "pyspark.sql.DataFrame"			,   lowercase_						:     str = None			,   lowercase_						:     str = None			,   **lowercase_						:     Tuple			,   )					->						Tuple:
             """simple docstring"""




             import pyspark

             _lowerCamelCase       :		str		=pyspark.sql.SparkSession.builder.getOrCreate()
             _lowerCamelCase       :		int		=df
             _lowerCamelCase       :		Union[str, Any]		=working_dir

             super().__init__(
                 cache_dir=lowercase_			,   config_name=str(self.df.semanticHash() )			,   **lowercase_			,   )






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             def create_cache_and_write_probe(lowercase_						:     Optional[Any] ):
                    # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
                    # already exist.
                    os.makedirs(self._cache_dir			,   exist_ok=lowercase_ )
                    _lowerCamelCase       :		int		=os.path.join(self._cache_dir			,   'fs_test' + uuid.uuida().hex )
                    # Opening the file in append mode will create a new file unless it already exists, in which case it will not
                    # change the file contents.
                    open(lowercase_			,   'a' )
                    return [probe_file]

             if self._spark.conf.get('spark.master'			,   '' ).startswith('local' ):
                    return

             # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
             # accessible to the driver.
             # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
             if self._cache_dir:
                    _lowerCamelCase       :		List[str]		=(
                        self._spark.sparkContext.parallelize(range(1 )			,   1 ).mapPartitions(lowercase_ ).collect()
                    )
                    if os.path.isfile(probe[0] ):
                           return

             raise ValueError(
                 'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Any:
             """simple docstring"""




             return datasets.DatasetInfo(features=self.config.features )






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     datasets.download.download_manager.DownloadManager )					->						List[Any]:
             """simple docstring"""




             return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any] )					->						List[str]:
             """simple docstring"""




             import pyspark

             def get_arrow_batch_size(lowercase_						:     List[str] ):
                    for batch in it:
                           yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )

             _lowerCamelCase       :		Dict		=self.df.count()
             _lowerCamelCase       :		Any		=df_num_rows if df_num_rows <= 100 else 100
             # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
             _lowerCamelCase       :		List[Any]		=(
                 self.df.limit(lowercase_ )
                 .repartition(1 )
                 .mapInArrow(lowercase_			,   'batch_bytes: long' )
                 .agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
                 .collect()[0]
                 .sample_bytes
                 / sample_num_rows
             )
             _lowerCamelCase       :		List[Any]		=approx_bytes_per_row * df_num_rows
             if approx_total_size > max_shard_size:
                    # Make sure there is at least one row per partition.
                    _lowerCamelCase       :		str		=min(lowercase_			,   int(approx_total_size / max_shard_size ) )
                    _lowerCamelCase       :		Any		=self.df.repartition(lowercase_ )






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     str			,   lowercase_						:     str			,   lowercase_						:     int			,   )					->						Iterable[Tuple[int, bool, Union[int, tuple]]]:
             """simple docstring"""




             import pyspark

             _lowerCamelCase       :		Any		=ParquetWriter if file_format == 'parquet' else ArrowWriter
             _lowerCamelCase       :		List[Any]		=os.path.join(self._working_dir			,   os.path.basename(lowercase_ ) ) if self._working_dir else fpath
             _lowerCamelCase       :		Optional[Any]		=file_format == 'parquet'

             # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
             # pickling the SparkContext.
             _lowerCamelCase       :		Optional[Any]		=self.config.features
             _lowerCamelCase       :		Optional[int]		=self._writer_batch_size
             _lowerCamelCase       :		List[Any]		=self._fs.storage_options

             def write_arrow(lowercase_						:     Tuple ):
                    # Within the same SparkContext, no two task attempts will share the same attempt ID.
                    _lowerCamelCase       :		List[str]		=pyspark.TaskContext().taskAttemptId()
                    _lowerCamelCase       :		Tuple		=next(lowercase_			,   lowercase_ )
                    if first_batch is None:
                           # Some partitions might not receive any data.
                           return pa.RecordBatch.from_arrays(
                               [[task_id], [0], [0]]			,   names=['task_id', 'num_examples', 'num_bytes']			,   )
                    _lowerCamelCase       :		str		=0
                    _lowerCamelCase       :		Optional[int]		=writer_class(
                        features=lowercase_			,   path=working_fpath.replace('SSSSS'			,   F'''{shard_id:05d}''' ).replace('TTTTT'			,   F'''{task_id:05d}''' )			,   writer_batch_size=lowercase_			,   storage_options=lowercase_			,   embed_local_files=lowercase_			,   )
                    _lowerCamelCase       :		Optional[Any]		=pa.Table.from_batches([first_batch] )
                    writer.write_table(lowercase_ )
                    for batch in it:
                           if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                                  _lowerCamelCase	,     _lowerCamelCase       :		Dict		=writer.finalize()
                                  writer.close()
                                  yield pa.RecordBatch.from_arrays(
                                      [[task_id], [num_examples], [num_bytes]]			,   names=['task_id', 'num_examples', 'num_bytes']			,   )
                                  shard_id += 1
                                  _lowerCamelCase       :		Union[str, Any]		=writer_class(
                                      features=writer._features			,   path=working_fpath.replace('SSSSS'			,   F'''{shard_id:05d}''' ).replace('TTTTT'			,   F'''{task_id:05d}''' )			,   writer_batch_size=lowercase_			,   storage_options=lowercase_			,   embed_local_files=lowercase_			,   )
                           _lowerCamelCase       :		Optional[Any]		=pa.Table.from_batches([batch] )
                           writer.write_table(lowercase_ )

                    if writer._num_bytes > 0:
                           _lowerCamelCase	,     _lowerCamelCase       :		List[Any]		=writer.finalize()
                           writer.close()
                           yield pa.RecordBatch.from_arrays(
                               [[task_id], [num_examples], [num_bytes]]			,   names=['task_id', 'num_examples', 'num_bytes']			,   )

                    if working_fpath != fpath:
                           for file in os.listdir(os.path.dirname(lowercase_ ) ):
                                  _lowerCamelCase       :		Tuple		=os.path.join(os.path.dirname(lowercase_ )			,   os.path.basename(lowercase_ ) )
                                  shutil.move(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		Dict		=(
                 self.df.mapInArrow(lowercase_			,   'task_id: long, num_examples: long, num_bytes: long' )
                 .groupBy('task_id' )
                 .agg(
                     pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' )			,   pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' )			,   pyspark.sql.functions.count('num_bytes' ).alias('num_shards' )			,   pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' )			,   )
                 .collect()
             )
             for row in stats:
                    yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     "datasets.SplitGenerator"			,   lowercase_						:     str = "arrow"			,   lowercase_						:     Optional[Union[str, int]] = None			,   lowercase_						:     Optional[int] = None			,   **lowercase_						:     Optional[int]			,   )					->						List[Any]:
             """simple docstring"""




             self._validate_cache_dir()

             _lowerCamelCase       :		Dict		=convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
             self._repartition_df_if_needed(lowercase_ )
             _lowerCamelCase       :		List[Any]		=not is_remote_filesystem(self._fs )
             _lowerCamelCase       :		Tuple		=os.path.join if is_local else posixpath.join

             _lowerCamelCase       :		Optional[Any]		='-TTTTT-SSSSS-of-NNNNN'
             _lowerCamelCase       :		Optional[int]		=F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
             _lowerCamelCase       :		int		=path_join(self._output_dir			,   lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=0
             _lowerCamelCase       :		str		=0
             _lowerCamelCase       :		Optional[Any]		=0
             _lowerCamelCase       :		Any		=[]
             _lowerCamelCase       :		Any		=[]

             for task_id, content in self._prepare_split_single(lowercase_			,   lowercase_			,   lowercase_ ):
                    (
                        (
                        _lowerCamelCase
                    )	,     (
                        _lowerCamelCase
                    )	,     (
                        _lowerCamelCase
                    )	,     (
                        _lowerCamelCase
                    )	,     
                    )       :		Tuple		=content
                    if num_bytes > 0:
                           total_num_examples += num_examples
                           total_num_bytes += num_bytes
                           total_shards += num_shards
                           task_id_and_num_shards.append((task_id, num_shards) )
                           all_shard_lengths.extend(lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=total_num_examples
             _lowerCamelCase       :		Tuple		=total_num_bytes

             # should rename everything at the end
             logger.debug(F'''Renaming {total_shards} shards.''' )
             if total_shards > 1:
                    _lowerCamelCase       :		Any		=all_shard_lengths

                    # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
                    # pickling error due to pickling the SparkContext.
                    _lowerCamelCase       :		Union[str, Any]		=self._fs

                    # use the -SSSSS-of-NNNNN pattern
                    def _rename_shard(
                        lowercase_						:     int			,   lowercase_						:     int			,   lowercase_						:     int			,   ):
                           rename(
                               lowercase_			,   fpath.replace('SSSSS'			,   F'''{shard_id:05d}''' ).replace('TTTTT'			,   F'''{task_id:05d}''' )			,   fpath.replace('TTTTT-SSSSS'			,   F'''{global_shard_id:05d}''' ).replace('NNNNN'			,   F'''{total_shards:05d}''' )			,   )

                    _lowerCamelCase       :		str		=[]
                    _lowerCamelCase       :		Optional[Any]		=0
                    for i in range(len(lowercase_ ) ):
                           _lowerCamelCase	,     _lowerCamelCase       :		str		=task_id_and_num_shards[i]
                           for shard_id in range(lowercase_ ):
                                  args.append([task_id, shard_id, global_shard_id] )
                                  global_shard_id += 1
                    self._spark.sparkContext.parallelize(lowercase_			,   len(lowercase_ ) ).map(lambda lowercase_ : _rename_shard(*lowercase_ ) ).collect()
             else:
                    # don't use any pattern
                    _lowerCamelCase       :		Any		=0
                    _lowerCamelCase       :		Tuple		=task_id_and_num_shards[0][0]
                    self._rename(
                        fpath.replace('SSSSS'			,   F'''{shard_id:05d}''' ).replace('TTTTT'			,   F'''{task_id:05d}''' )			,   fpath.replace(lowercase_			,   '' )			,   )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     "datasets.SplitGenerator"			,   )					->						SparkExamplesIterable:
             """simple docstring"""




             return SparkExamplesIterable(self.df )






from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )




@require_tf
class A  :
      UpperCamelCase__							:  Union[str, Any]      =XGLMConfig
      UpperCamelCase__							:  Dict      ={}
      UpperCamelCase__							:  Tuple      ='gelu'






      def __init__(					self						:     List[Any]			,   lowercase_						:     List[str]			,   lowercase_						:     Union[str, Any]=14			,   lowercase_						:     Dict=7			,   lowercase_						:     Union[str, Any]=True			,   lowercase_						:     Optional[Any]=True			,   lowercase_						:     Any=True			,   lowercase_						:     Optional[int]=99			,   lowercase_						:     List[Any]=32			,   lowercase_						:     List[Any]=2			,   lowercase_						:     Dict=4			,   lowercase_						:     List[str]=37			,   lowercase_						:     int="gelu"			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     List[str]=512			,   lowercase_						:     Union[str, Any]=0.02			,   )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=parent
             _lowerCamelCase       :		Optional[Any]		=batch_size
             _lowerCamelCase       :		Optional[int]		=seq_length
             _lowerCamelCase       :		Union[str, Any]		=is_training
             _lowerCamelCase       :		Tuple		=use_input_mask
             _lowerCamelCase       :		str		=use_labels
             _lowerCamelCase       :		Any		=vocab_size
             _lowerCamelCase       :		List[str]		=d_model
             _lowerCamelCase       :		List[Any]		=num_hidden_layers
             _lowerCamelCase       :		Union[str, Any]		=num_attention_heads
             _lowerCamelCase       :		List[Any]		=ffn_dim
             _lowerCamelCase       :		Optional[Any]		=activation_function
             _lowerCamelCase       :		Dict		=activation_dropout
             _lowerCamelCase       :		Tuple		=attention_dropout
             _lowerCamelCase       :		List[str]		=max_position_embeddings
             _lowerCamelCase       :		int		=initializer_range
             _lowerCamelCase       :		Optional[int]		=None
             _lowerCamelCase       :		Optional[Any]		=0
             _lowerCamelCase       :		List[str]		=2
             _lowerCamelCase       :		Any		=1






      def        lowerCamelCase       (					self						:     str )					->						int:
             """simple docstring"""




             return XGLMConfig.from_pretrained('facebook/xglm-564M' )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=tf.clip_by_value(
                 ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size )			,   clip_value_min=0			,   clip_value_max=3 )

             _lowerCamelCase       :		Any		=None
             if self.use_input_mask:
                    _lowerCamelCase       :		str		=random_attention_mask([self.batch_size, self.seq_length] )

             _lowerCamelCase       :		Optional[int]		=self.get_config()

             _lowerCamelCase       :		Optional[Any]		=floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,   2 )

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
             )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             return XGLMConfig(
                 vocab_size=self.vocab_size			,   d_model=self.hidden_size			,   num_layers=self.num_hidden_layers			,   attention_heads=self.num_attention_heads			,   ffn_dim=self.ffn_dim			,   activation_function=self.activation_function			,   activation_dropout=self.activation_dropout			,   attention_dropout=self.attention_dropout			,   max_position_embeddings=self.max_position_embeddings			,   initializer_range=self.initializer_range			,   use_cache=lowercase_			,   bos_token_id=self.bos_token_id			,   eos_token_id=self.eos_token_id			,   pad_token_id=self.pad_token_id			,   return_dict=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     
             )       :		Any		=config_and_inputs

             _lowerCamelCase       :		Union[str, Any]		={
                 'input_ids': input_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict




@require_tf
class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Union[str, Any]      =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      UpperCamelCase__							:  List[str]      =(TFXGLMForCausalLM,) if is_tf_available() else ()
      UpperCamelCase__							:  Any      =(
          {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
      )
      UpperCamelCase__							:  str      =False
      UpperCamelCase__							:  int      =False
      UpperCamelCase__							:  int      =False






      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=TFXGLMModelTester(self )
             _lowerCamelCase       :		str		=ConfigTester(self			,   config_class=lowercase_			,   n_embd=37 )






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.config_tester.run_common_tests()






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						int:
             """simple docstring"""




             for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase       :		int		=TFXGLMModel.from_pretrained(lowercase_ )
                    self.assertIsNotNone(lowercase_ )






      @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             super().test_resize_token_embeddings()




@require_tf
class A  (   unittest.TestCase     ):






      @slow
      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=True )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		List[Any]		=tf.convert_to_tensor([[2, 268, 9865]]			,   dtype=tf.intaa )  # The dog
             # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
             # fmt: off
             _lowerCamelCase       :		int		=[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
             # fmt: on
             _lowerCamelCase       :		Dict		=model.generate(lowercase_			,   do_sample=lowercase_			,   num_beams=1 )
             if verify_outputs:
                    self.assertListEqual(output_ids[0].numpy().tolist()			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

             tf.random.set_seed(0 )
             _lowerCamelCase       :		Tuple		=tokenizer('Today is a nice day and'			,   return_tensors='tf' )
             _lowerCamelCase       :		Optional[int]		=tokenized.input_ids
             # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
             with tf.device(':/CPU:0' ):
                    _lowerCamelCase       :		List[Any]		=model.generate(lowercase_			,   do_sample=lowercase_			,   seed=[7, 0] )
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.decode(output_ids[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=(
                 'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
             )
             self.assertEqual(lowercase_			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

             _lowerCamelCase       :		Optional[Any]		='left'

             # use different length sentences to test batching
             _lowerCamelCase       :		int		=[
                 'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                 'left-padding, such as in batched generation. The output for the sequence below should be the same '
                 'regardless of whether left padding is applied or not. When',
                 'Hello, my dog is a little',
             ]

             _lowerCamelCase       :		List[Any]		=tokenizer(lowercase_			,   return_tensors='tf'			,   padding=lowercase_ )
             _lowerCamelCase       :		int		=inputs['input_ids']

             _lowerCamelCase       :		str		=model.generate(input_ids=lowercase_			,   attention_mask=inputs['attention_mask']			,   max_new_tokens=12 )

             _lowerCamelCase       :		Optional[Any]		=tokenizer(sentences[0]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		List[str]		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		Tuple		=tokenizer(sentences[1]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		Dict		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		str		=tokenizer.batch_decode(lowercase_			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=tokenizer.decode(output_non_padded[0]			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		int		=tokenizer.decode(output_padded[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		List[str]		=[
                 'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                 'left-padding, such as in batched generation. The output for the sequence below should be the same '
                 'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                 'a single',
                 'Hello, my dog is a little bit of a shy one, but he is very friendly',
             ]
             self.assertListEqual(lowercase_			,   lowercase_ )
             self.assertListEqual(lowercase_			,   [non_padded_sentence, padded_sentence] )


