




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode





def 	__A	(			__lowerCamelCase							,    **__lowerCamelCase					)   -> Optional[Any]:
	return RunBeamCommand(
	    args.dataset							,    args.name							,    args.cache_dir							,    args.beam_pipeline_options							,    args.data_dir							,    args.all_configs							,    args.save_info or args.save_infos							,    args.ignore_verifications							,    args.force_redownload							,    **__lowerCamelCase							,    )



class 	__lowerCAmelCase			(			__magic_name__				):
							@staticmethod
							def    lowerCamelCase__							(		__magic_name__		:ArgumentParser       ):







								'''simple docstring'''







								a		       =							parser.add_parser("""run_beam"""	,   help="""Run a Beam dataset processing pipeline"""       )
								run_beam_parser.add_argument("""dataset"""	,   type=__magic_name__	,   help="""Name of the dataset to download"""       )
								run_beam_parser.add_argument("""--name"""	,   type=__magic_name__	,   default=__magic_name__	,   help="""Dataset config name"""       )
								run_beam_parser.add_argument(
								    """--cache_dir"""	,   type=__magic_name__	,   default=__magic_name__	,   help="""Cache directory where the datasets are stored"""	,   )
								run_beam_parser.add_argument(
								    """--beam_pipeline_options"""	,   type=__magic_name__	,   default=""""""	,   help="""Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"""	,   )
								run_beam_parser.add_argument(
								    """--data_dir"""	,   type=__magic_name__	,   default=__magic_name__	,   help="""Can be used to specify a manual directory to get the files from"""	,   )
								run_beam_parser.add_argument("""--all_configs"""	,   action="""store_true"""	,   help="""Test all dataset configurations"""       )
								run_beam_parser.add_argument("""--save_info"""	,   action="""store_true"""	,   help="""Save the dataset infos file"""       )
								run_beam_parser.add_argument(
								    """--ignore_verifications"""	,   action="""store_true"""	,   help="""Run the test without checksums and splits checks"""       )
								run_beam_parser.add_argument("""--force_redownload"""	,   action="""store_true"""	,   help="""Force dataset redownload"""       )
								# aliases
								run_beam_parser.add_argument("""--save_infos"""	,   action="""store_true"""	,   help="""alias for save_info"""       )
								run_beam_parser.set_defaults(func=__magic_name__       )
							def __init__(		self		:Tuple	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:bool	,   __magic_name__		:bool	,   __magic_name__		:bool	,   __magic_name__		:bool	,   **__magic_name__		:Optional[Any]	,   ):







								'''simple docstring'''







								a		       =							dataset
								a		       =							name
								a		       =							cache_dir
								a		       =							beam_pipeline_options
								a		       =							data_dir
								a		       =							all_configs
								a		       =							save_infos
								a		       =							ignore_verifications
								a		       =							force_redownload
								a		       =							config_kwargs





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								import apache_beam as beam

								if self._name is not None and self._all_configs:
									print("""Both parameters `name` and `all_configs` can't be used at once."""       )
									exit(1       )
								a       ,				a		       =							self._dataset, self._name
								a		       =							dataset_module_factory(__magic_name__       )
								a		       =							import_main_class(dataset_module.module_path       )
								a		       =							[]
								if self._beam_pipeline_options:
									a		       =							beam.options.pipeline_options.PipelineOptions(
									    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(""","""       ) if opt]       )
								else:
									a		       =							None
								if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
									for builder_config in builder_cls.BUILDER_CONFIGS:
										builders.append(
										    builder_cls(
										        config_name=builder_config.name	,   data_dir=self._data_dir	,   hash=dataset_module.hash	,   beam_options=__magic_name__	,   cache_dir=self._cache_dir	,   base_path=dataset_module.builder_kwargs.get("""base_path"""       )	,   )       )
								else:
									builders.append(
									    builder_cls(
									        config_name=__magic_name__	,   data_dir=self._data_dir	,   beam_options=__magic_name__	,   cache_dir=self._cache_dir	,   base_path=dataset_module.builder_kwargs.get("""base_path"""       )	,   **self._config_kwargs	,   )       )

								for builder in builders:
									builder.download_and_prepare(
									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
									    if not self._force_redownload
									    else DownloadMode.FORCE_REDOWNLOAD	,   download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )	,   verification_mode=VerificationMode.NO_CHECKS
									    if self._ignore_verifications
									    else VerificationMode.ALL_CHECKS	,   try_from_hf_gcs=__magic_name__	,   )
									if self._save_infos:
										builder._save_infos()

								print("""Apache beam run successful."""       )

								# If save_infos=True, the dataset infos file is created next to the loaded module file.
								# Let's move it to the original directory of the dataset script, to allow the user to
								# upload them on S3 at the same time afterwards.
								if self._save_infos:
									a		       =							os.path.join(builder_cls.get_imported_module_dir()	,   config.DATASETDICT_INFOS_FILENAME       )

									a		       =							Path(__magic_name__       ).name + """.py"""

									a		       =							os.path.join(__magic_name__	,   __magic_name__       )
									if os.path.isfile(__magic_name__       ):
										a		       =							os.path.dirname(__magic_name__       )
									elif os.path.isfile(__magic_name__       ):
										a		       =							path
									else:  # in case of a remote dataset
										print(F'Dataset Infos file saved at {dataset_infos_path}'       )
										exit(1       )

									# Move datasetinfo back to the user
									a		       =							os.path.join(__magic_name__	,   config.DATASETDICT_INFOS_FILENAME       )
									copyfile(__magic_name__	,   __magic_name__       )
									print(F'Dataset Infos file saved at {user_dataset_infos_path}'       )







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)

class 	__lowerCAmelCase			:
							def __init__(		self		:Tuple	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							parent
								a		       =							13
								a		       =							7
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							99
								a		       =							32
								a		       =							4
								a		       =							2
								a		       =							4
								a		       =							37
								a		       =							"""gelu"""
								a		       =							0.1
								a		       =							0.1
								a		       =							512
								a		       =							16
								a		       =							2
								a		       =							0.02
								a		       =							3
								a		       =							4
								a		       =							None
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size       )

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

								a		       =							None
								if self.use_token_type_ids:
									a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.type_vocab_size       )

								a		       =							None
								if self.use_mc_token_ids:
									a		       =							ids_tensor([self.batch_size, self.num_choices]	,   self.seq_length       )

								a		       =							None
								a		       =							None
								a		       =							None
								if self.use_labels:
									a		       =							ids_tensor([self.batch_size]	,   self.type_sequence_label_size       )
									a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.num_labels       )
									a		       =							ids_tensor([self.batch_size]	,   self.num_choices       )

								a		       =							GPTJConfig(
								    vocab_size=self.vocab_size	,   n_embd=self.hidden_size	,   n_layer=self.num_hidden_layers	,   n_head=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   n_positions=self.max_position_embeddings	,   type_vocab_size=self.type_vocab_size	,   initializer_range=self.initializer_range	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   rotary_dim=self.rotary_dim	,   return_dict=__magic_name__	,   )

								a		       =							ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,   2       )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Any	,   __magic_name__		:Tuple	,   __magic_name__		:str	,   __magic_name__		:Any	,   __magic_name__		:Optional[Any]	,   *__magic_name__		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )

								a		       =							[input_ids, None, input_mask]  # None is the input for 'past'
								a		       =							model(__magic_name__       )

								a		       =							model(__magic_name__       )

								self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:Tuple	,   __magic_name__		:Union[str, Any]	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								# first forward pass
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

								self.parent.assertTrue(len(__magic_name__       ) == len(__magic_name__       )       )
								self.parent.assertTrue(len(__magic_name__       ) == len(__magic_name__       ) + 1       )

								a       ,				a		       =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 1)	,   config.vocab_size       )
								a		       =							ids_tensor([self.batch_size, 1]	,   self.type_vocab_size       )

								# append to next input_ids and token_type_ids
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([token_type_ids, next_token_types]	,   axis=-1       )

								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   past_key_values=__magic_name__       )[
								    """last_hidden_state"""
								]

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -1, random_slice_idx]
								a		       =							output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-6       )
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:str	,   __magic_name__		:str	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								# create attention mask
								a		       =							self.seq_length // 2
								a		       =							tf.ones((self.batch_size, half_seq_length)	,   dtype=tf.intaa       )
								a		       =							tf.zeros((self.batch_size, self.seq_length - half_seq_length)	,   dtype=tf.intaa       )
								a		       =							tf.concat([attn_mask_begin, attn_mask_end]	,   axis=1       )

								# first forward pass
								a       ,				a		       =							model(__magic_name__	,   attention_mask=__magic_name__       ).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 1)	,   config.vocab_size       )

								# change a random masked slice from input_ids
								a		       =							ids_tensor((1,)	,   __magic_name__       ).numpy() + 1
								a		       =							ids_tensor((self.batch_size, self.seq_length)	,   config.vocab_size       )
								a		       =							tf.range(self.seq_length       ) == (self.seq_length - random_seq_idx_to_change)
								a		       =							tf.transpose(
								    tf.broadcast_to(tf.expand_dims(__magic_name__	,   -1       )	,   (self.seq_length, self.batch_size)       )       )
								a		       =							tf.where(__magic_name__	,   __magic_name__	,   __magic_name__       )

								# append to next input_ids and attn_mask
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([attn_mask, tf.ones((shape_list(__magic_name__       )[0], 1)	,   dtype=tf.intaa       )]	,   axis=1       )

								# get two different outputs
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   past_key_values=__magic_name__	,   attention_mask=__magic_name__       )[
								    """last_hidden_state"""
								]

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -1, random_slice_idx]
								a		       =							output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-1_2       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:Optional[int]	,   *__magic_name__		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								a		       =							input_ids[:1, :]
								a		       =							input_mask[:1, :]
								a		       =							token_type_ids[:1, :]
								a		       =							1

								# first forward pass
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

								a       ,				a		       =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 3)	,   config.vocab_size       )
								a		       =							ids_tensor((self.batch_size, 3)	,   2       )
								a		       =							ids_tensor((self.batch_size, 3)	,   self.type_vocab_size       )

								# append to next input_ids and token_type_ids
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([input_mask, next_attn_mask]	,   axis=-1       )
								a		       =							tf.concat([token_type_ids, next_token_types]	,   axis=-1       )

								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__	,   past_key_values=__magic_name__	,   )["""last_hidden_state"""]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]       )

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -3:, random_slice_idx]
								a		       =							output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-3       )
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Optional[int]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str	,   __magic_name__		:int	,   *__magic_name__		:List[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )
								self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)       )





							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.prepare_config_and_inputs()

								(
								    (
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				
								)		       =							config_and_inputs

								a		       =							{
								    """input_ids""": input_ids,
								    """token_type_ids""": token_type_ids,
								    """attention_mask""": input_mask,
								}
								return config, inputs_dict







@require_tf
class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						(
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							UpperCamelCase__        =						(TFGPTJForCausalLM,) if is_tf_available() else ()
							UpperCamelCase__        =						(
							    {
							        '''feature-extraction''': TFGPTJModel,
							        '''question-answering''': TFGPTJForQuestionAnswering,
							        '''text-classification''': TFGPTJForSequenceClassification,
							        '''text-generation''': TFGPTJForCausalLM,
							        '''zero-shot''': TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							def    lowerCamelCase__							(		self		:Union[str, Any]	,   __magic_name__		:Any	,   __magic_name__		:Optional[int]	,   __magic_name__		:int	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]       ):







								'''simple docstring'''







								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("""Fast"""       )
								):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

								return False
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							TFGPTJModelTester(self       )
								a		       =							ConfigTester(self	,   config_class=__magic_name__	,   n_embd=37       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*__magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*__magic_name__       )
							@slow
							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) > 0	,   """skip testing on GPU for now to avoid GPU OOM."""	,   )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								self.assertIsNotNone(__magic_name__       )





							@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 	__lowerCAmelCase			(			unittest.TestCase				):
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								a		       =							tf.convert_to_tensor([[464, 3290]]	,   dtype=tf.intaa       )  # The dog
								# fmt: off
								# The dog is a man's best friend. It is a loyal companion, and it is a friend
								a		       =							[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
								# fmt: on
								a		       =							model.generate(__magic_name__	,   do_sample=__magic_name__       )
								self.assertListEqual(output_ids[0].numpy().tolist()	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )
								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )

								a		       =							tokenizer("""Today is a nice day and"""	,   return_tensors="""tf"""       )
								# forces the generation to happen on CPU, to avoid GPU-related quirks
								with tf.device(""":/CPU:0"""       ):
									a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   seed=[42, 0]       )
								a		       =							tokenizer.decode(output_ids[0]	,   skip_special_tokens=__magic_name__       )

								a		       =							"""Today is a nice day and I’m going to go for a walk. I’"""
								self.assertEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )
								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )

								a		       =							"""left"""

								# Define PAD Token = EOS Token = 50256
								a		       =							tokenizer.eos_token
								a		       =							model.config.eos_token_id

								# use different length sentences to test batching
								a		       =							[
								    """Hello, my dog is a little""",
								    """Today, I""",
								]
								a		       =							[
								    """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
								    """Today, I’m going to be talking about a topic that’""",
								]
								return model, tokenizer, sentences, expected_output_sentences
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							tokenizer(sentences[0]	,   return_tensors="""tf"""       )
								a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							(
								    shape_list(inputs_non_padded["""input_ids"""]       )[-1]
								    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]	,   tf.intaa       )       ).numpy()
								)
								a		       =							tokenizer(sentences[1]	,   return_tensors="""tf"""       )
								a		       =							model.generate(
								    **__magic_name__	,   do_sample=__magic_name__	,   num_beams=2	,   max_length=model.config.max_length - num_paddings       )
								a		       =							tokenizer.decode(output_non_padded[0]	,   skip_special_tokens=__magic_name__       )
								a		       =							tokenizer.decode(output_padded[0]	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   [non_padded_sentence, padded_sentence]       )





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							tf.function(model.generate	,   jit_compile=__magic_name__       )
								a		       =							xla_generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )

