



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowercase     							=						logging.getLogger(__name__)
@dataclass
class 				__A					:
			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='''NER'''					, metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}      )
			UpperCamelCase					:Optional[str]     =			field(
			    default=A_					, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
			UpperCamelCase					:bool     =			field(default=A_					, metadata={'''help''': '''Set this flag to use fast tokenization.'''}      )
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			UpperCamelCase					:Optional[str]     =			field(
			    default=A_					, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					, )







@dataclass
class 				__A					:
			UpperCamelCase					:str     =			field(
			    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}      )
			UpperCamelCase					:Optional[str]     =			field(
			    default=A_					, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					, )
			UpperCamelCase					:int     =			field(
			    default=128					, metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }					, )
			UpperCamelCase					:bool     =			field(
			    default=A_					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )







def      _A				()  ->List[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		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.
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
			else:
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir )
			    and os.listdir(training_args.output_dir )
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
						    """ --overwrite_output_dir to overcome.""" )

			lowerCamelCase__ :      List[Any]								=		import_module("""tasks""" )
			try:
						lowerCamelCase__ :      Union[str, Any]								=		getattr(UpperCamelCase      ,			model_args.task_type )
						lowerCamelCase__ :      TokenClassificationTask								=		token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
						    f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,			datefmt="""%m/%d/%Y %H:%M:%S"""      ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,			)
			logger.warning(
			    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""      ,			training_args.local_rank      ,			training_args.device      ,			training_args.n_gpu      ,			bool(training_args.local_rank != -1 )      ,			training_args.fpaa      ,			)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank ):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info("""Training/evaluation parameters %s"""      ,			UpperCamelCase )

			# Set seed
			set_seed(training_args.seed )

			# Prepare CONLL-2003 task
			lowerCamelCase__ :      Any								=		token_classification_task.get_labels(data_args.labels )
			lowerCamelCase__ :      Dict[int, str]								=		dict(enumerate(UpperCamelCase ) )
			lowerCamelCase__ :      Dict								=		len(UpperCamelCase )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			lowerCamelCase__ :      Dict								=		AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,			num_labels=UpperCamelCase      ,			idalabel=UpperCamelCase      ,			labelaid={label: i for i, label in enumerate(UpperCamelCase )}      ,			cache_dir=model_args.cache_dir      ,			)
			lowerCamelCase__ :      List[str]								=		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      ,			)
			lowerCamelCase__ :      Dict								=		AutoModelForTokenClassification.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      ,			)

			# Get datasets
			lowerCamelCase__ :      Any								=		(
			    TokenClassificationDataset(
			        token_classification_task=UpperCamelCase      ,			data_dir=data_args.data_dir      ,			tokenizer=UpperCamelCase      ,			labels=UpperCamelCase      ,			model_type=config.model_type      ,			max_seq_length=data_args.max_seq_length      ,			overwrite_cache=data_args.overwrite_cache      ,			mode=Split.train      ,			)
			    if training_args.do_train
			    else None
			)
			lowerCamelCase__ :      Optional[Any]								=		(
			    TokenClassificationDataset(
			        token_classification_task=UpperCamelCase      ,			data_dir=data_args.data_dir      ,			tokenizer=UpperCamelCase      ,			labels=UpperCamelCase      ,			model_type=config.model_type      ,			max_seq_length=data_args.max_seq_length      ,			overwrite_cache=data_args.overwrite_cache      ,			mode=Split.dev      ,			)
			    if training_args.do_eval
			    else None
			)

			def align_predictions(UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray ) -> Tuple[List[int], List[int]]:
						lowerCamelCase__ :      Dict								=		np.argmax(UpperCamelCase      ,			axis=2 )

						lowerCamelCase__							,lowerCamelCase__ :      int								=		preds.shape

						lowerCamelCase__ :      int								=		[[] for _ in range(UpperCamelCase )]
						lowerCamelCase__ :      Optional[Any]								=		[[] for _ in range(UpperCamelCase )]

						for i in range(UpperCamelCase ):
									for j in range(UpperCamelCase ):
												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
															out_label_list[i].append(label_map[label_ids[i][j]] )
															preds_list[i].append(label_map[preds[i][j]] )

						return preds_list, out_label_list

			def compute_metrics(UpperCamelCase		:      EvalPrediction ) -> Dict:
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		align_predictions(p.predictions      ,			p.label_ids )
						return {
						    "accuracy_score": accuracy_score(UpperCamelCase      ,			UpperCamelCase ),
						    "precision": precision_score(UpperCamelCase      ,			UpperCamelCase ),
						    "recall": recall_score(UpperCamelCase      ,			UpperCamelCase ),
						    "f1": fa_score(UpperCamelCase      ,			UpperCamelCase ),
						}

			# Data collator
			lowerCamelCase__ :      Optional[Any]								=		DataCollatorWithPadding(UpperCamelCase      ,			pad_to_multiple_of=8 ) if training_args.fpaa else None

			# Initialize our Trainer
			lowerCamelCase__ :      Tuple								=		Trainer(
			    model=UpperCamelCase      ,			args=UpperCamelCase      ,			train_dataset=UpperCamelCase      ,			eval_dataset=UpperCamelCase      ,			compute_metrics=UpperCamelCase      ,			data_collator=UpperCamelCase      ,			)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_process_zero():
									tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
			lowerCamelCase__ :      Dict								=		{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***""" )

						lowerCamelCase__ :      Dict								=		trainer.evaluate()

						lowerCamelCase__ :      List[Any]								=		os.path.join(training_args.output_dir      ,			"""eval_results.txt""" )
						if trainer.is_world_process_zero():
									with open(UpperCamelCase      ,			"""w""" ) as writer:
												logger.info("""***** Eval results *****""" )
												for key, value in result.items():
															logger.info("""  %s = %s"""      ,			UpperCamelCase      ,			UpperCamelCase )
															writer.write("""%s = %s\n""" % (key, value) )

									results.update(UpperCamelCase )

    # Predict
			if training_args.do_predict:
						lowerCamelCase__ :      Tuple								=		TokenClassificationDataset(
						    token_classification_task=UpperCamelCase      ,			data_dir=data_args.data_dir      ,			tokenizer=UpperCamelCase      ,			labels=UpperCamelCase      ,			model_type=config.model_type      ,			max_seq_length=data_args.max_seq_length      ,			overwrite_cache=data_args.overwrite_cache      ,			mode=Split.test      ,			)

						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      str								=		trainer.predict(UpperCamelCase )
						lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		align_predictions(UpperCamelCase      ,			UpperCamelCase )

						lowerCamelCase__ :      List[str]								=		os.path.join(training_args.output_dir      ,			"""test_results.txt""" )
						if trainer.is_world_process_zero():
									with open(UpperCamelCase      ,			"""w""" ) as writer:
												for key, value in metrics.items():
															logger.info("""  %s = %s"""      ,			UpperCamelCase      ,			UpperCamelCase )
															writer.write("""%s = %s\n""" % (key, value) )

        # Save predictions
						lowerCamelCase__ :      List[Any]								=		os.path.join(training_args.output_dir      ,			"""test_predictions.txt""" )
						if trainer.is_world_process_zero():
									with open(UpperCamelCase      ,			"""w""" ) as writer:
												with open(os.path.join(data_args.data_dir      ,			"""test.txt""" )      ,			"""r""" ) as f:
															token_classification_task.write_predictions_to_file(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			return results






def      _A				(UpperCamelCase		:      Optional[int] )  ->Optional[Any]:



			'''simple docstring'''







			main()


if __name__ == "__main__":
				main()








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
				import os

				import jax.numpy as jnp
				from jax import jit

				from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
				from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

				_lowercase     							=						'''0.12'''  # assumed parallelism: 8


if is_torch_available():
				import torch






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Optional[Any]=None )  ->Optional[int]:



			'''simple docstring'''







			if rng is None:
						lowerCamelCase__ :      List[Any]								=		random.Random()

			lowerCamelCase__ :      Tuple								=		1
			for dim in shape:
						total_dims *= dim

			lowerCamelCase__ :      str								=		[]
			for _ in range(UpperCamelCase ):
						values.append(rng.randint(0      ,			vocab_size - 1 ) )

			lowerCamelCase__ :      str								=		np.array(UpperCamelCase      ,			dtype=jnp.intaa ).reshape(UpperCamelCase )

			return output






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Dict=None )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		ids_tensor(UpperCamelCase      ,			vocab_size=2      ,			rng=UpperCamelCase )
			# make sure that at least one token is attended to for each batch
			lowerCamelCase__ :      int								=		1
			return attn_mask
@require_flax
class 				__A					:
			UpperCamelCase					:List[Any]     =			None
			UpperCamelCase					:Dict     =			()





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      Any								=		inputs["""input_ids"""].shape[-1] // 2
						lowerCamelCase__ :      Dict								=		inputs["""input_ids"""][:max_batch_size, :sequence_length]

						lowerCamelCase__ :      List[Any]								=		jnp.ones_like(__magic_name__				)
						lowerCamelCase__ :      Dict								=		attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						lowerCamelCase__ :      Tuple								=		input_ids.shape[-1] + 5
						if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									lowerCamelCase__ :      List[Any]								=		config.eos_token_id
						return config, input_ids, attention_mask, max_length





			@is_pt_flax_cross_test
			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		False
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      List[Any]								=		0

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      List[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCamelCase__ :      Union[str, Any]								=		getattr(__magic_name__							,    __magic_name__				)
									lowerCamelCase__ :      Dict								=		pt_model_class(__magic_name__				).eval()
									lowerCamelCase__ :      Union[str, Any]								=		load_flax_weights_in_pytorch_model(__magic_name__							,    flax_model.params				)

									lowerCamelCase__ :      str								=		flax_model.generate(__magic_name__				).sequences
									lowerCamelCase__ :      str								=		pt_model.generate(torch.tensor(__magic_name__							,    dtype=torch.long				)				)

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
												lowerCamelCase__ :      Optional[Any]								=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist()							,    flax_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Tuple								=		model_class(__magic_name__				)

									lowerCamelCase__ :      int								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      List[str]								=		True
						lowerCamelCase__ :      Dict								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      str								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Dict								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Union[str, Any]								=		False
						lowerCamelCase__ :      Dict								=		max_length
						lowerCamelCase__ :      str								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Dict								=		jit(model.generate				)
									lowerCamelCase__ :      Optional[int]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      List[Any]								=		max_length
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      List[str]								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[0]							,    input_ids.shape[0] * config.num_return_sequences				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Optional[int]								=		True
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      Optional[int]								=		0.8
						lowerCamelCase__ :      List[Any]								=		10
						lowerCamelCase__ :      int								=		0.3
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      int								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Any								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      List[str]								=		1
						lowerCamelCase__ :      Optional[int]								=		8
						lowerCamelCase__ :      Union[str, Any]								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      Dict								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      Optional[int]								=		2
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      Tuple								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      Union[str, Any]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Any								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      List[str]								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[str]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Optional[Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      int								=		True
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      int								=		model_class(__magic_name__				)

									lowerCamelCase__ :      str								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Union[str, Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      Tuple								=		2
						lowerCamelCase__ :      List[Any]								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)







@require_flax
class 				__A					( unittest.TestCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
						lowerCamelCase__ :      Tuple								=		FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

						lowerCamelCase__ :      Dict								=		"""Hello world"""
						lowerCamelCase__ :      Union[str, Any]								=		tokenizer(__magic_name__							,    return_tensors="""np"""				).input_ids

						# typos are quickly detected (the correct argument is `do_sample`)
						with self.assertRaisesRegex(__magic_name__							,    """do_samples"""				):
									model.generate(__magic_name__							,    do_samples=__magic_name__				)

						# arbitrary arguments that will not be used anywhere are also not accepted
						with self.assertRaisesRegex(__magic_name__							,    """foo"""				):
									lowerCamelCase__ :      Tuple								=		{"""foo""": """bar"""}
									model.generate(__magic_name__							,    **__magic_name__				)



