






import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union

import datasets
import numpy as np
import torch
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

A_       :Union[str, Any]       =						logging.getLogger(__name__)




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      str			 =field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,     )
				UpperCamelCase__ :      bool			 =field(
				    default=a      ,     metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}      ,     )
				UpperCamelCase__ :      str			 =field(
				    default="""main"""      ,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      ,     )
				UpperCamelCase__ :      bool			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }      ,     )




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      Optional[str]			 =field(default=a      ,     metadata={"""help""": """The input training data file (a text file)."""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}      ,     )
				UpperCamelCase__ :      bool			 =field(
				    default=a      ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. If passed, sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }      ,     )
				UpperCamelCase__ :      bool			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """Whether to pad all samples to the maximum sentence length. """
				            """If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
				            """efficient on GPU but very bad for TPU."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }      ,     )


				def       __lowercase							( self    ):




									"""simple docstring"""

									if self.train_file is not None:
														__UpperCamelCase      :					Any       		=self.train_file.split('.'    )[-1]
														assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
									if self.validation_file is not None:
														__UpperCamelCase      :					int       		=self.validation_file.split('.'    )[-1]
														assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      PreTrainedTokenizerBase
				UpperCamelCase__ :      Union[bool, str, PaddingStrategy]			 =True
				UpperCamelCase__ :      Optional[int]			 =None
				UpperCamelCase__ :      Optional[int]			 =None


				def __call__( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		='label' if 'label' in features[0].keys() else 'labels'
									__UpperCamelCase      :					List[str]       		=[feature.pop(UpperCAmelCase__    ) for feature in features]
									__UpperCamelCase      :					Optional[Any]       		=len(UpperCAmelCase__    )
									__UpperCamelCase      :					Dict       		=len(features[0]['input_ids']    )
									__UpperCamelCase      :					int       		=[
									    [{k: v[i] for k, v in feature.items()} for i in range(UpperCAmelCase__    )] for feature in features
									]
									__UpperCamelCase      :					List[str]       		=list(chain(*UpperCAmelCase__    )    )

									__UpperCamelCase      :					Dict       		=self.tokenizer.pad(
									    UpperCAmelCase__							,  padding=self.padding							,  max_length=self.max_length							,  pad_to_multiple_of=self.pad_to_multiple_of							,  return_tensors='pt'							,  )

									# Un-flatten
									__UpperCamelCase      :					Optional[int]       		={k: v.view(UpperCAmelCase__							,  UpperCAmelCase__							,  -1    ) for k, v in batch.items()}
									# Add back labels
									__UpperCamelCase      :					int       		=torch.tensor(UpperCAmelCase__							,  dtype=torch.intaa    )
									return batch






def 							A     (       )     ->						int:
					__UpperCamelCase      :					Optional[Any]       		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
					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.
										__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					int       		=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
					else:
										__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry('run_swag'							,UpperCamelCase_							,UpperCamelCase_					)

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,datefmt='%m/%d/%Y %H:%M:%S'							,handlers=[logging.StreamHandler(sys.stdout					)]							,)

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					__UpperCamelCase      :					int       		=training_args.get_process_log_level()
					logger.setLevel(UpperCamelCase_					)
					datasets.utils.logging.set_verbosity(UpperCamelCase_					)
					transformers.utils.logging.set_verbosity(UpperCamelCase_					)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
					    + F'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
					logger.info(F'Training/evaluation parameters {training_args}'					)

					# Detecting last checkpoint.
					__UpperCamelCase      :					Optional[int]       		=None
					if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
										__UpperCamelCase      :					int       		=get_last_checkpoint(training_args.output_dir					)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
															raise ValueError(
															    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
															    'Use --overwrite_output_dir to overcome.'					)
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
															    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Set seed before initializing model.
					set_seed(training_args.seed					)

					# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
					# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
					# (the dataset will be downloaded automatically from the datasets Hub).

					# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
					# 'text' is found. You can easily tweak this behavior (see below).

					# In distributed training, the load_dataset function guarantee that only one local process can concurrently
					# download the dataset.
					if data_args.train_file is not None or data_args.validation_file is not None:
										__UpperCamelCase      :					Any       		={}
										if data_args.train_file is not None:
															__UpperCamelCase      :					int       		=data_args.train_file
										if data_args.validation_file is not None:
															__UpperCamelCase      :					Dict       		=data_args.validation_file
										__UpperCamelCase      :					Union[str, Any]       		=data_args.train_file.split('.'					)[-1]
										__UpperCamelCase      :					Dict       		=load_dataset(
										    UpperCamelCase_							,data_files=UpperCamelCase_							,cache_dir=model_args.cache_dir							,use_auth_token=True if model_args.use_auth_token else None							,)
					else:
										# Downloading and loading the swag dataset from the hub.
										__UpperCamelCase      :					int       		=load_dataset(
										    'swag'							,'regular'							,cache_dir=model_args.cache_dir							,use_auth_token=True if model_args.use_auth_token else None							,)
					# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
					# https://huggingface.co/docs/datasets/loading_datasets.html.

					# Load pretrained model and tokenizer

					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					__UpperCamelCase      :					str       		=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							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)
					__UpperCamelCase      :					Tuple       		=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							,use_fast=model_args.use_fast_tokenizer							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)
					__UpperCamelCase      :					List[str]       		=AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path							,from_tf=bool('.ckpt' in model_args.model_name_or_path					)							,config=UpperCamelCase_							,cache_dir=model_args.cache_dir							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)

					# When using your own dataset or a different dataset from swag, you will probably need to change this.
					__UpperCamelCase      :					Optional[int]       		=[F'ending{i}' for i in range(4					)]
					__UpperCamelCase      :					str       		='sent1'
					__UpperCamelCase      :					Tuple       		='sent2'

					if data_args.max_seq_length is None:
										__UpperCamelCase      :					List[str]       		=tokenizer.model_max_length
										if max_seq_length > 1_024:
															logger.warning(
															    'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
															    ' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
															    ' override this default with `--block_size xxx`.'					)
															__UpperCamelCase      :					Any       		=1_024
					else:
										if data_args.max_seq_length > tokenizer.model_max_length:
															logger.warning(
															    F'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
															    F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'					)
										__UpperCamelCase      :					Optional[Any]       		=min(data_args.max_seq_length							,tokenizer.model_max_length					)

					# Preprocessing the datasets.
					def preprocess_function(a_					):
										__UpperCamelCase      :					Dict       		=[[context] * 4 for context in examples[context_name]]
										__UpperCamelCase      :					int       		=examples[question_header_name]
										__UpperCamelCase      :					Tuple       		=[
										    [F'{header} {examples[end][i]}' for end in ending_names] for i, header in enumerate(UpperCamelCase_					)
										]

										# Flatten out
										__UpperCamelCase      :					int       		=list(chain(*UpperCamelCase_					)					)
										__UpperCamelCase      :					List[Any]       		=list(chain(*UpperCamelCase_					)					)

										# Tokenize
										__UpperCamelCase      :					Optional[int]       		=tokenizer(
										    UpperCamelCase_							,UpperCamelCase_							,truncation=UpperCamelCase_							,max_length=UpperCamelCase_							,padding='max_length' if data_args.pad_to_max_length else False							,)
										# Un-flatten
										return {k: [v[i : i + 4] for i in range(0							,len(UpperCamelCase_					)							,4					)] for k, v in tokenized_examples.items()}

					if training_args.do_train:
										if "train" not in raw_datasets:
															raise ValueError('--do_train requires a train dataset'					)
										__UpperCamelCase      :					List[Any]       		=raw_datasets['train']
										if data_args.max_train_samples is not None:
															__UpperCamelCase      :					Any       		=min(len(UpperCamelCase_					)							,data_args.max_train_samples					)
															__UpperCamelCase      :					Union[str, Any]       		=train_dataset.select(range(UpperCamelCase_					)					)
										with training_args.main_process_first(desc='train dataset map pre-processing'					):
															__UpperCamelCase      :					Tuple       		=train_dataset.map(
															    UpperCamelCase_							,batched=UpperCamelCase_							,num_proc=data_args.preprocessing_num_workers							,load_from_cache_file=not data_args.overwrite_cache							,)

					if training_args.do_eval:
										if "validation" not in raw_datasets:
															raise ValueError('--do_eval requires a validation dataset'					)
										__UpperCamelCase      :					str       		=raw_datasets['validation']
										if data_args.max_eval_samples is not None:
															__UpperCamelCase      :					Optional[Any]       		=min(len(UpperCamelCase_					)							,data_args.max_eval_samples					)
															__UpperCamelCase      :					Any       		=eval_dataset.select(range(UpperCamelCase_					)					)
										with training_args.main_process_first(desc='validation dataset map pre-processing'					):
															__UpperCamelCase      :					Tuple       		=eval_dataset.map(
															    UpperCamelCase_							,batched=UpperCamelCase_							,num_proc=data_args.preprocessing_num_workers							,load_from_cache_file=not data_args.overwrite_cache							,)

    # Data collator
					__UpperCamelCase      :					Union[str, Any]       		=(
					    default_data_collator
					    if data_args.pad_to_max_length
					    else DataCollatorForMultipleChoice(tokenizer=UpperCamelCase_							,pad_to_multiple_of=8 if training_args.fpaa else None					)
					)

					# Metric
					def compute_metrics(a_					):
										__UpperCamelCase		,				__UpperCamelCase      :					Union[str, Any]       		=eval_predictions
										__UpperCamelCase      :					Optional[int]       		=np.argmax(UpperCamelCase_							,axis=1					)
										return {"accuracy": (preds == label_ids).astype(np.floataa					).mean().item()}

					# Initialize our Trainer
					__UpperCamelCase      :					Any       		=Trainer(
					    model=UpperCamelCase_							,args=UpperCamelCase_							,train_dataset=train_dataset if training_args.do_train else None							,eval_dataset=eval_dataset if training_args.do_eval else None							,tokenizer=UpperCamelCase_							,data_collator=UpperCamelCase_							,compute_metrics=UpperCamelCase_							,)

					# Training
					if training_args.do_train:
										__UpperCamelCase      :					List[str]       		=None
										if training_args.resume_from_checkpoint is not None:
															__UpperCamelCase      :					str       		=training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__UpperCamelCase      :					Tuple       		=last_checkpoint
										__UpperCamelCase      :					Union[str, Any]       		=trainer.train(resume_from_checkpoint=UpperCamelCase_					)
										trainer.save_model()  # Saves the tokenizer too for easy upload
										__UpperCamelCase      :					Union[str, Any]       		=train_result.metrics

										__UpperCamelCase      :					str       		=(
										    data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase_					)
										)
										__UpperCamelCase      :					Tuple       		=min(UpperCamelCase_							,len(UpperCamelCase_					)					)

										trainer.log_metrics('train'							,UpperCamelCase_					)
										trainer.save_metrics('train'							,UpperCamelCase_					)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										logger.info('*** Evaluate ***'					)

										__UpperCamelCase      :					Optional[int]       		=trainer.evaluate()
										__UpperCamelCase      :					Dict       		=data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase_					)
										__UpperCamelCase      :					int       		=min(UpperCamelCase_							,len(UpperCamelCase_					)					)

										trainer.log_metrics('eval'							,UpperCamelCase_					)
										trainer.save_metrics('eval'							,UpperCamelCase_					)

					__UpperCamelCase      :					List[str]       		={
					    'finetuned_from': model_args.model_name_or_path,
					    'tasks': 'multiple-choice',
					    'dataset_tags': 'swag',
					    'dataset_args': 'regular',
					    'dataset': 'SWAG',
					    'language': 'en',
					}

					if training_args.push_to_hub:
										trainer.push_to_hub(**UpperCamelCase_					)
					else:
										trainer.create_model_card(**UpperCamelCase_					)






def 							A     (       a_					)     ->						Optional[int]:
					main()


if __name__ == "__main__":
							main()




"""simple docstring"""





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 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	XGLMConfig
 __UpperCAmelCase	:					Optional[Any]   		=	{}
 __UpperCAmelCase	:					Union[str, Any]   		=	"gelu"


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=1_4,					UpperCAmelCase__							:			str=7,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[Any]=True,					UpperCAmelCase__							:			int=True,					UpperCAmelCase__							:			List[str]=9_9,					UpperCAmelCase__							:			Union[str, Any]=3_2,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			Union[str, Any]=4,					UpperCAmelCase__							:			Tuple=3_7,					UpperCAmelCase__							:			List[Any]="gelu",					UpperCAmelCase__							:			List[str]=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Tuple=5_1_2,					UpperCAmelCase__							:			Optional[Any]=0.02,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						d_model
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						ffn_dim
    __lowercase								=						activation_function
    __lowercase								=						activation_dropout
    __lowercase								=						attention_dropout
    __lowercase								=						max_position_embeddings
    __lowercase								=						initializer_range
    __lowercase								=						None
    __lowercase								=						0
    __lowercase								=						2
    __lowercase								=						1


 def 					_lowercase (			self							:			Union[str, Any]	):
    return XGLMConfig.from_pretrained("facebook/xglm-564M"	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	),					clip_value_min=0,					clip_value_max=3	)

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

    __lowercase								=						self.get_config()

    __lowercase								=						floats_tensor([self.num_hidden_layers, self.num_attention_heads],					2	)

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )


 def 					_lowercase (			self							:			List[Any]	):
    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=UpperCAmelCase__,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					return_dict=UpperCAmelCase__,					)



 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.prepare_config_and_inputs()

    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs

    __lowercase								=						{
        "input_ids": input_ids,
        "head_mask": head_mask,
    }

    return config, inputs_dict







@require_tf
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __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	:					Optional[Any]   		=	False
 __UpperCAmelCase	:					List[str]   		=	False
 __UpperCAmelCase	:					int   		=	False


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						TFXGLMModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					n_embd=3_7	)


 def 					_lowercase (			self							:			Any	):
    self.config_tester.run_common_tests()


 @slow
 def 					_lowercase (			self							:			List[str]	):
    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						TFXGLMModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."	)
 def 					_lowercase (			self							:			int	):
    super().test_resize_token_embeddings()







@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]=True	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]],					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
    __lowercase								=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
    # fmt: on
    __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					num_beams=1	)
    if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist(),					UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)

    tf.random.set_seed(0	)
    __lowercase								=						tokenizer("Today is a nice day and",					return_tensors="tf"	)
    __lowercase								=						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"	):
       __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					seed=[7, 0]	)
    __lowercase								=						tokenizer.decode(output_ids[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						(
        "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
    )
    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)

    __lowercase								=						"left"

    # use different length sentences to test batching
    __lowercase								=						[
        "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",
    ]

    __lowercase								=						tokenizer(UpperCAmelCase__,					return_tensors="tf",					padding=UpperCAmelCase__	)
    __lowercase								=						inputs["input_ids"]

    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					attention_mask=inputs["attention_mask"],					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[0],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[1],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_non_padded[0],					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_padded[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						[
        "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(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__,					[non_padded_sentence, padded_sentence]	)




