





import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


UpperCamelCase__   : Any					  =							os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"{bindir}/../../examples/pytorch/translation"):
				from run_translation import main  # noqa


set_seed(42)
UpperCamelCase__   : int					  =							'sshleifer/student_marian_en_ro_6_1'
UpperCamelCase__   : Dict					  =							'sshleifer/tiny-mbart'







@require_torch
class   _UpperCamelCase  (       lowerCAmelCase__  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Union[str, Any]=False ,					__lowercase    :  Tuple=None ,					__lowercase    :  int=True ,					__lowercase    :  List[str]=True ,					__lowercase    :  Optional[Any]=True ,					__lowercase    :  Dict=True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     self.run_trainer(
						    eval_steps=1 ,					max_len=12 ,					model_name=_SCREAMING_SNAKE_CASE ,					num_train_epochs=1 ,					distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str=_SCREAMING_SNAKE_CASE ,					predict_with_generate=_SCREAMING_SNAKE_CASE ,					do_train=_SCREAMING_SNAKE_CASE ,					do_eval=_SCREAMING_SNAKE_CASE ,					do_predict=_SCREAMING_SNAKE_CASE ,					)
						UpperCAmelCase_								=     TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE ,					"""trainer_state.json"""		)		).log_history

						if not do_eval:
							return

						UpperCAmelCase_								=     [log for log in logs if """eval_loss""" in log.keys()]

						UpperCAmelCase_								=     eval_metrics[0]
						if predict_with_generate:
							assert "eval_bleu" in first_step_stats

							UpperCAmelCase_								=     eval_metrics[-1]
							assert isinstance(last_step_stats["""eval_bleu"""] ,					_SCREAMING_SNAKE_CASE		)
							assert not math.isnan(float(last_step_stats["""eval_loss"""]		)		), "eval_loss must not be `nan`"





					@require_torch_non_multi_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						self.run_seqaseq_quick()





					@require_torch_multi_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE		)





					@require_torch_multi_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE		)





					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--sharded_ddp simple"""		)





					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--sharded_ddp simple --fp16"""		)





					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--sharded_ddp zero_dp_2""" ,					predict_with_generate=_SCREAMING_SNAKE_CASE		)





					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						self.run_seqaseq_quick(
						    distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" ,					predict_with_generate=_SCREAMING_SNAKE_CASE		)





					@require_apex
					@require_torch_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--fp16 --fp16_backend=apex"""		)
						# test 2nd time - was getting eval_loss': nan'
						# to reproduce the problem set distributed=False
						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str="""--fp16 --fp16_backend=apex"""		)





					@parameterized.expand(["""base""", """low""", """high""", """mixed"""]		)
					@require_torch_multi_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     {
						    # test with the default log_level - should be info and thus log info once
						    """base""": {"""extra_args_str""": """""", """n_matches""": 1},
						    # test with low log_level and log_level_replica - should be noisy on all processes
						    # now the info string should appear twice on 2 processes
						    """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
						    # test with high log_level and low log_level_replica
						    # now the info string should appear once only on the replica
						    """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
						    # test with high log_level and log_level_replica - should be quiet on all processes
						    """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
						}

						UpperCAmelCase_								=     experiments[experiment_id]
						UpperCAmelCase_								=     {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
						UpperCAmelCase_								=     """Running training"""
						with CaptureStderr() as cl:
							self.run_seqaseq_quick(**_SCREAMING_SNAKE_CASE ,					extra_args_str=data["""extra_args_str"""]		)
						UpperCAmelCase_								=     len(re.findall(_SCREAMING_SNAKE_CASE ,					cl.err		)		)
						self.assertEqual(_SCREAMING_SNAKE_CASE ,					data["""n_matches"""]		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.run_trainer(
						    eval_steps=2 ,					max_len=1_28 ,					model_name=_SCREAMING_SNAKE_CASE ,					learning_rate=3e-4 ,					num_train_epochs=10 ,					distributed=_SCREAMING_SNAKE_CASE ,					)

						# Check metrics
						UpperCAmelCase_								=     TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE ,					"""trainer_state.json"""		)		).log_history
						UpperCAmelCase_								=     [log for log in logs if """eval_loss""" in log.keys()]
						UpperCAmelCase_								=     eval_metrics[0]
						UpperCAmelCase_								=     eval_metrics[-1]

						assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
						assert isinstance(last_step_stats["""eval_bleu"""] ,					_SCREAMING_SNAKE_CASE		)

						# test if do_predict saves generations and metrics
						UpperCAmelCase_								=     os.listdir(_SCREAMING_SNAKE_CASE		)
						UpperCAmelCase_								=     {os.path.basename(_SCREAMING_SNAKE_CASE		) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents





					@slow
					@require_bitsandbytes
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(__lowercase    :  Any		) -> Tuple[int, float]:
							UpperCAmelCase_								=     """--skip_memory_metrics 0"""

							UpperCAmelCase_								=     self.run_trainer(
							    max_len=1_28 ,					model_name=_SCREAMING_SNAKE_CASE ,					learning_rate=3e-4 ,					num_train_epochs=1 ,					optim=_SCREAMING_SNAKE_CASE ,					distributed=_SCREAMING_SNAKE_CASE ,					extra_args_str=_SCREAMING_SNAKE_CASE ,					do_eval=_SCREAMING_SNAKE_CASE ,					do_predict=_SCREAMING_SNAKE_CASE ,					n_gpus_to_use=1 ,					)

							# Check metrics
							UpperCAmelCase_								=     TrainerState.load_from_json(Path(_SCREAMING_SNAKE_CASE ,					"""trainer_state.json"""		)		).log_history
							UpperCAmelCase_								=     int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20		)
							UpperCAmelCase_								=     int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20		)

							UpperCAmelCase_								=     logs[0]["""train_loss"""]
							return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

						UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
						UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

						UpperCAmelCase_								=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						UpperCAmelCase_								=     gpu_peak_mem_orig + gpu_alloc_mem_orig
						UpperCAmelCase_								=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						UpperCAmelCase_								=     gpu_total_mem_orig - gpu_total_mem_bnb

						# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
						# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
						# in 2 bytes and the diff in optim memory usage is derived as so:
						#
						# - normal 25*8=~200MB (8 bytes per param)
						# - bnb    25*2= ~50MB (2 bytes per param)
						#
						# Thus we should expect ~150MB total memory saved.
						#
						# Peak memory should be the same - the total should be different by about that same margin
						#
						# After leaving a small margin to accommodate for differences between gpus let's check
						# that we have at least 120MB in savings
						UpperCAmelCase_								=     1_20

						# uncomment the following if this test starts failing - requires py38 for a new print feature
						# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
						# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
						# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
						# print(f"{gpu_alloc_mem_diff=}MB")
						# print(f"{gpu_peak_mem_diff=}MB")
						# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
						# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

						self.assertGreater(
						    _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
						    F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
						    F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" ,					)

						self.assertGreater(
						    _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
						    F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
						    F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" ,					)

						self.assertEqual(
						    _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Tuple ,					__lowercase    :  int = 3e-3 ,					__lowercase    :  Optional[int] = "adafactor" ,					__lowercase    :  int = False ,					__lowercase    :  Union[str, Any] = None ,					__lowercase    :  Dict = 0 ,					__lowercase    :  Any = True ,					__lowercase    :  Any = True ,					__lowercase    :  Optional[Any] = True ,					__lowercase    :  Optional[int] = True ,					__lowercase    :  str = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
						UpperCAmelCase_								=     self.get_auto_remove_tmp_dir()
						UpperCAmelCase_								=     F"""\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_SCREAMING_SNAKE_CASE		)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_SCREAMING_SNAKE_CASE		)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

						UpperCAmelCase_								=     F"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_SCREAMING_SNAKE_CASE		)}\n        """.split()

						UpperCAmelCase_								=     """
            --do_predict
        """.split()

						UpperCAmelCase_								=     []
						if do_train:
							args += args_train

						if do_eval:
							args += args_eval

						if do_predict:
							args += args_predict

						if predict_with_generate:
							args += "--predict_with_generate".split()

						if do_train:
							if optim == "adafactor":
								args += "--adafactor".split()
							else:
								args += F"""--optim {optim}""".split()

						if extra_args_str is not None:
							args += extra_args_str.split()

						if distributed:
							if n_gpus_to_use is None:
								UpperCAmelCase_								=     get_gpu_count()
							UpperCAmelCase_								=     get_torch_dist_unique_port()
							UpperCAmelCase_								=     F"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
							UpperCAmelCase_								=     [sys.executable] + distributed_args + args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_SCREAMING_SNAKE_CASE ,					env=self.get_env()		)
						else:
							UpperCAmelCase_								=     ["""run_translation.py"""] + args
							with patch.object(_SCREAMING_SNAKE_CASE ,					"""argv""" ,					_SCREAMING_SNAKE_CASE		):
								main()

						return output_dir









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   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModel.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModel.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForPreTraining.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForPreTraining.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForCausalLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForCausalLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForMaskedLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForMaskedLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForMaskedLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForMaskedLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForSeqaSeqLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForSequenceClassification.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







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

							UpperCAmelCase_								=     TFAutoModelForQuestionAnswering.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForQuestionAnswering.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)

						UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)

						UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)


