




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase__         =      getLogger(__name__)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    int = 8				,				SCREAMING_SNAKE_CASE_							:    int = 10_24				,				SCREAMING_SNAKE_CASE_							:    List[str]="val"				,				SCREAMING_SNAKE_CASE_							:    Any=None				,				SCREAMING_SNAKE_CASE_							:    str=False				,				SCREAMING_SNAKE_CASE_							:    Any="summarization"				,				SCREAMING_SNAKE_CASE_							:    Optional[int]=None				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=1				,				SCREAMING_SNAKE_CASE_							:    Dict = None				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]=""				,				**SCREAMING_SNAKE_CASE_							:    str				,				):
					__lowerCAmelCase           =	str(SCREAMING_SNAKE_CASE_					)
					assert local_rank is not None
					torch.distributed.init_process_group(backend="nccl"				,				rank=SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	save_dir.joinpath(F"""rank_{local_rank}_output.json"""					)
					torch.cuda.set_device(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_					).cuda()
					if fpaa:
										__lowerCAmelCase           =	model.half()
					# determine if we need to increase num_beams
					use_task_specific_params(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)  # update config with task specific params
					__lowerCAmelCase           =	generate_kwargs.pop("num_beams"				,				model.config.num_beams					)  # AttributeError risk?
					if num_return_sequences > num_beams:
										__lowerCAmelCase           =	num_return_sequences

					__lowerCAmelCase           =	AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_					)
					logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""					)  # if this is wrong, check config.model_type.

					if max_source_length is None:
										__lowerCAmelCase           =	tokenizer.model_max_length
					if prefix is None:
										__lowerCAmelCase           =	prefix or getattr(model.config				,				"prefix"				,				""					) or ""
					__lowerCAmelCase           =	SeqaSeqDataset(
					    SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				max_target_length=10_24				,				type_path=SCREAMING_SNAKE_CASE_				,				n_obs=SCREAMING_SNAKE_CASE_				,				prefix=SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_				,				)
					# I set shuffle=True for a more accurate progress bar.
					# If all the longest samples are first, the prog bar estimate is too high at the beginning.
					__lowerCAmelCase           =	ds.make_sortish_sampler(SCREAMING_SNAKE_CASE_				,				distributed=SCREAMING_SNAKE_CASE_				,				add_extra_examples=SCREAMING_SNAKE_CASE_				,				shuffle=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	DataLoader(SCREAMING_SNAKE_CASE_				,				sampler=SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_				,				collate_fn=ds.collate_fn					)
					__lowerCAmelCase           =	[]
					for batch in tqdm(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	model.generate(
										    input_ids=batch["input_ids"].to(model.device					)				,				attention_mask=batch["attention_mask"].to(model.device					)				,				num_return_sequences=SCREAMING_SNAKE_CASE_				,				num_beams=SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_				,				)
										__lowerCAmelCase           =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,				skip_special_tokens=SCREAMING_SNAKE_CASE_				,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	batch["ids"]
										if num_return_sequences > 1:
															__lowerCAmelCase           =	chunks(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)  # batch size chunks, each of size num_return_seq
										for i, pred in enumerate(SCREAMING_SNAKE_CASE_					):
															results.append({"pred": pred, "id": ids[i].item()}					)
					save_json(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					return results, sampler.num_replicas







def      _a				(						):
					__lowerCAmelCase           =	argparse.ArgumentParser(
					    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"					)
					parser.add_argument("--data_dir"				,				type=SCREAMING_SNAKE_CASE_				,				help="like cnn_dm/test.source"					)
					parser.add_argument(
					    "--model_name"				,				type=SCREAMING_SNAKE_CASE_				,				help="like facebook/bart-large-cnn,t5-base, etc."				,				default="sshleifer/distilbart-xsum-12-3"				,				)
					parser.add_argument("--save_dir"				,				type=SCREAMING_SNAKE_CASE_				,				help="where to save"				,				default="tmp_gen"					)
					parser.add_argument("--max_source_length"				,				type=SCREAMING_SNAKE_CASE_				,				default=SCREAMING_SNAKE_CASE_					)
					parser.add_argument(
					    "--type_path"				,				type=SCREAMING_SNAKE_CASE_				,				default="test"				,				help="which subset to evaluate typically train/val/test"					)
					parser.add_argument("--task"				,				type=SCREAMING_SNAKE_CASE_				,				default="summarization"				,				help="used for task_specific_params + metrics"					)
					parser.add_argument("--bs"				,				type=SCREAMING_SNAKE_CASE_				,				default=8				,				required=SCREAMING_SNAKE_CASE_				,				help="batch size"					)
					parser.add_argument(
					    "--local_rank"				,				type=SCREAMING_SNAKE_CASE_				,				default=-1				,				required=SCREAMING_SNAKE_CASE_				,				help="should be passed by distributed.launch"					)

					parser.add_argument(
					    "--n_obs"				,				type=SCREAMING_SNAKE_CASE_				,				default=SCREAMING_SNAKE_CASE_				,				required=SCREAMING_SNAKE_CASE_				,				help="How many observations. Defaults to all."					)
					parser.add_argument(
					    "--num_return_sequences"				,				type=SCREAMING_SNAKE_CASE_				,				default=1				,				required=SCREAMING_SNAKE_CASE_				,				help="How many sequences to return"					)
					parser.add_argument(
					    "--sync_timeout"				,				type=SCREAMING_SNAKE_CASE_				,				default=6_00				,				required=SCREAMING_SNAKE_CASE_				,				help="How long should master process wait for other processes to finish."				,				)
					parser.add_argument("--src_lang"				,				type=SCREAMING_SNAKE_CASE_				,				default=SCREAMING_SNAKE_CASE_				,				required=SCREAMING_SNAKE_CASE_					)
					parser.add_argument("--tgt_lang"				,				type=SCREAMING_SNAKE_CASE_				,				default=SCREAMING_SNAKE_CASE_				,				required=SCREAMING_SNAKE_CASE_					)
					parser.add_argument(
					    "--prefix"				,				type=SCREAMING_SNAKE_CASE_				,				required=SCREAMING_SNAKE_CASE_				,				default=SCREAMING_SNAKE_CASE_				,				help="will be added to the begininng of src examples"					)
					parser.add_argument("--fp16"				,				action="store_true"					)
					parser.add_argument("--debug"				,				action="store_true"					)
					__lowerCAmelCase           =	time.time()
					__lowerCAmelCase       ,							__lowerCAmelCase           =	parser.parse_known_args()
					__lowerCAmelCase           =	parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE_					)
					if generate_kwargs and args.local_rank <= 0:
										print(F"""parsed the following generate kwargs: {generate_kwargs}"""					)
					__lowerCAmelCase           =	Path(args.save_dir + "_tmp"					)
					Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)  # this handles locking.
					__lowerCAmelCase           =	list(json_save_dir.glob("rank_*.json"					)					)
					if intermediate_files:
										raise ValueError(F"""Found files at {json_save_dir} please move or remove them."""					)
										# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
					__lowerCAmelCase           =	{}
					if args.src_lang is not None:
										__lowerCAmelCase           =	args.src_lang
					if args.tgt_lang is not None:
										__lowerCAmelCase           =	args.tgt_lang

					Path(args.save_dir					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	eval_data_dir(
					    args.data_dir				,				SCREAMING_SNAKE_CASE_				,				args.model_name				,				type_path=args.type_path				,				bs=args.bs				,				fpaa=args.fpaa				,				task=args.task				,				local_rank=args.local_rank				,				n_obs=args.n_obs				,				max_source_length=args.max_source_length				,				num_return_sequences=args.num_return_sequences				,				prefix=args.prefix				,				dataset_kwargs=SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_				,				)

					if args.local_rank <= 0:
										__lowerCAmelCase           =	Path(args.save_dir					)
										save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	gather_results_from_each_node(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				args.sync_timeout					)
										__lowerCAmelCase           =	combine_partial_results(SCREAMING_SNAKE_CASE_					)
										if args.num_return_sequences > 1:
															__lowerCAmelCase           =	save_dir.joinpath("pseudolabel_results.json"					)
															print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""					)
															save_json(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
															return
										__lowerCAmelCase           =	Path(args.data_dir					).joinpath(args.type_path + ".target"					)
										with open(SCREAMING_SNAKE_CASE_					) as f:
															__lowerCAmelCase           =	[x.rstrip() for x in f.readlines()][: len(SCREAMING_SNAKE_CASE_					)]

										# Calculate metrics, save metrics,  and save _generations.txt
										__lowerCAmelCase           =	"translation" in args.task
										__lowerCAmelCase           =	calculate_bleu if calc_bleu else calculate_rouge
										__lowerCAmelCase           =	"bleu" if calc_bleu else "rouge"
										__lowerCAmelCase           =	score_fn(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	time.time() - start_time
										__lowerCAmelCase           =	round(runtime / metrics["n_obs"]				,				4					)
										__lowerCAmelCase           =	num_replicas
										# TODO(@stas00): add whatever metadata to metrics
										__lowerCAmelCase           =	save_dir.joinpath(F"""{args.type_path}_{metric_name}.json"""					)
										save_json(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				indent=SCREAMING_SNAKE_CASE_					)
										print(SCREAMING_SNAKE_CASE_					)
										write_txt_file(SCREAMING_SNAKE_CASE_				,				save_dir.joinpath(F"""{args.type_path}_generations.txt"""					)					)
										if args.debug:
															write_txt_file(SCREAMING_SNAKE_CASE_				,				save_dir.joinpath(F"""{args.type_path}.target"""					)					)
										else:
															shutil.rmtree(SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =	[]
					for partial_result in partial_results:
										records.extend(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	sorted(SCREAMING_SNAKE_CASE_				,				key=lambda SCREAMING_SNAKE_CASE_					: x["id"]					)
					__lowerCAmelCase           =	[x["pred"] for x in records]
					return preds







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    str					):
					# WAIT FOR lots of .json files
					__lowerCAmelCase           =	time.time()
					logger.info("waiting for all nodes to finish"					)
					__lowerCAmelCase           =	None
					while (time.time() - start_wait) < timeout:
										__lowerCAmelCase           =	list(save_dir.glob("rank_*.json"					)					)
										if len(SCREAMING_SNAKE_CASE_					) < num_replicas:
															continue
										try:
															# make sure all json files are fully saved
															__lowerCAmelCase           =	lmap(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
															return json_data
										except JSONDecodeError:
															continue
					else:
										raise TimeoutError("Rank 0 gave up on waiting for other processes"					)
					# Unreachable


if __name__ == "__main__":
					# Usage for MT:
					run_generate()










from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
					from transformers import (
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelForTableQuestionAnswering,
					    TFAutoModelForTokenClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFFunnelBaseModel,
					    TFFunnelModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					    TFTapasForQuestionAnswering,
					)
					from transformers.models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TF_MODEL_MAPPING,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class       a__   ( snake_case__				):
						_a							:   Optional[int] =   """new-model"""


if is_tf_available():
					class       a__   ( snake_case__				):
											_a							:   Dict =   NewModelConfig
@require_tf
class       a__   ( unittest.TestCase				):






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"bert-base-cased"
											__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"bert-base-cased"
											__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	TFAutoModelForPreTraining.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForCausalLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForCausalLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForMaskedLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForMaskedLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForSeqaSeqLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForSeqaSeqLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForSequenceClassification.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForQuestionAnswering.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						@require_tensorflow_probability
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForTableQuestionAnswering.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForTableQuestionAnswering.from_pretrained(
																    _A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
											self.assertIsInstance(_A		,      _A      )
											self.assertEqual(model.num_parameters()		,      1_4_4_1_0      )
											self.assertEqual(model.num_parameters(only_trainable=_A      )		,      1_4_4_1_0      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
											self.assertIsInstance(_A		,      _A      )
											self.assertEqual(model.num_parameters()		,      1_4_4_1_0      )
											self.assertEqual(model.num_parameters(only_trainable=_A      )		,      1_4_4_1_0      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	copy.deepcopy(model.config      )
											__lowerCAmelCase           =	["FunnelBaseModel"]
											__lowerCAmelCase           =	TFAutoModel.from_config(_A      )
											self.assertIsInstance(_A		,      _A      )

											with tempfile.TemporaryDirectory() as tmp_dir:
																model.save_pretrained(_A      )
																__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A      )
																self.assertIsInstance(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											try:
																AutoConfig.register("new-model"		,      _A      )

																__lowerCAmelCase           =	[
																    TFAutoModel,
																    TFAutoModelForCausalLM,
																    TFAutoModelForMaskedLM,
																    TFAutoModelForPreTraining,
																    TFAutoModelForQuestionAnswering,
																    TFAutoModelForSequenceClassification,
																    TFAutoModelForTokenClassification,
																]

																for auto_class in auto_classes:
																					with self.subTest(auto_class.__name__      ):
																										# Wrong config class will raise an error
																										with self.assertRaises(_A      ):
																															auto_class.register(_A		,      _A      )
																										auto_class.register(_A		,      _A      )
																										# Trying to register something existing in the Transformers library will raise an error
																										with self.assertRaises(_A      ):
																															auto_class.register(_A		,      _A      )

																										# Now that the config is registered, it can be used as any other config with the auto-API
																										__lowerCAmelCase           =	BertModelTester(self      ).get_config()
																										__lowerCAmelCase           =	NewModelConfig(**tiny_config.to_dict()      )
																										__lowerCAmelCase           =	auto_class.from_config(_A      )
																										self.assertIsInstance(_A		,      _A      )

																										with tempfile.TemporaryDirectory() as tmp_dir:
																															model.save_pretrained(_A      )
																															__lowerCAmelCase           =	auto_class.from_pretrained(_A      )
																															self.assertIsInstance(_A		,      _A      )

											finally:
																if "new-model" in CONFIG_MAPPING._extra_content:
																					del CONFIG_MAPPING._extra_content["new-model"]
																for mapping in (
																    TF_MODEL_MAPPING,
																    TF_MODEL_FOR_PRETRAINING_MAPPING,
																    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																    TF_MODEL_FOR_MASKED_LM_MAPPING,
																):
																					if NewModelConfig in mapping._extra_content:
																										del mapping._extra_content[NewModelConfig]






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      "bert-base is not a local folder and is not a valid model identifier"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("bert-base"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A		,      revision="aaaaaa"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"		,      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(_A		,      "Use `from_pt=True` to load this model"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
											with RequestCounter() as counter:
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
																self.assertEqual(counter.get_request_count		,      0      )
																self.assertEqual(counter.head_request_count		,      1      )
																self.assertEqual(counter.other_request_count		,      0      )

											# With a sharded checkpoint
											__lowerCAmelCase           =	TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
											with RequestCounter() as counter:
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
																self.assertEqual(counter.get_request_count		,      0      )
																self.assertEqual(counter.head_request_count		,      1      )
																self.assertEqual(counter.other_request_count		,      0      )




