

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a		="""true"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Optional[int] 	=			Accelerator(dispatch_batches=lowerCamelCase__     ,			split_batches=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			get_dataloader(lowerCamelCase__     ,			not dispatch_batches      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'     ,			return_dict=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = False     ,			lowerCamelCase__ = False      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )
	__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			get_mrpc_setup(lowerCamelCase__     ,			lowerCamelCase__      )
	# First do baseline
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			setup['no']
	model.to(lowerCamelCase__      )
	model.eval()
	for batch in dataloader:
		batch.to(lowerCamelCase__      )
		with torch.inference_mode():
			__lowerCamelCase    :    Dict 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			outputs.logits.argmax(dim=-1      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=batch['labels']      )
	__lowerCamelCase    :    str 	=			metric.compute()

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]     ,			distributed[key]      ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('**Testing gather_for_metrics**'      )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
				test_mrpc(lowerCamelCase__     ,			lowerCamelCase__      )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test torch metrics**'      )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			__lowerCamelCase    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					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

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)






@is_pt_tf_cross_test
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      List[str]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    Optional[int] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			AutoModel.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			AutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Union[str, Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Union[str, Any] 	=			TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			TFAutoModelForCausalLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[Any] 	=			AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			AutoModelForCausalLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      List[Any]):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    List[str] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[str] 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[Any] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Tuple):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Optional[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			TFAutoModelForMaskedLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[Any] 	=			AutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			AutoModelForMaskedLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Dict 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[Any] 	=			TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			TFAutoModelForSeqaSeqLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Any 	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			AutoModelForSeqaSeqLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    List[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    int 	=			AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Any):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    List[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			AutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    List[Any] 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)

								__lowerCamelCase    :    Union[str, Any] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    str 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)

								__lowerCamelCase    :    Optional[int] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)




