

'''simple docstring'''







import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_lowerCamelCase    =  logging.getLogger()
_lowerCamelCase    =  torch.cuda.is_available()
_lowerCamelCase    =  {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 4_2,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_lowercase							=     """\n""".join(snake_case_	)
	Path(snake_case_	).open("""w"""	).writelines(snake_case_	)


_lowerCamelCase    =  [' Sam ate lunch today.', 'Sams lunch ingredients.']
_lowerCamelCase    =  ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_lowerCamelCase    =  'patrickvonplaten/t5-tiny-random'
_lowerCamelCase    =  'sshleifer/t5-tinier-random'
_lowerCamelCase    =  'sshleifer/bart-tiny-random'
_lowerCamelCase    =  'sshleifer/tiny-mbart'
_lowerCamelCase    =  'sshleifer/tiny-marian-en-de'
_lowerCamelCase    =  'stas/tiny-wmt19-en-de'


_lowerCamelCase    =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(snake_case_							,       F"""{split}.source"""	)							,       snake_case_	)
		_dump_articles(os.path.join(snake_case_							,       F"""{split}.target"""	)							,       snake_case_	)
	return tmp_dir
class 					__a   (		_snake_case  ):

						@classmethod
						def 	_UpperCAmelCase							(		cls	:						int)						->str:

							"""simple docstring"""



							logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
							return cls

						@slow
						@require_torch_gpu
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Dict:

							"""simple docstring"""



							_lowercase							=     list_models()
							_lowercase							=     """sshleifer"""
							_lowercase							=     [x.modelId for x in model_list if x.modelId.startswith(lowercase__)]
							_lowercase							=     ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
							_lowercase							=     []
							for m in model_ids:
								if m in allowed_to_be_broken:
									continue
								try:
									AutoConfig.from_pretrained(lowercase__)
								except Exception:
									failures.append(lowercase__)
							assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""

						def 	_UpperCAmelCase							(		self	:						List[Any])						->List[Any]:

							"""simple docstring"""



							_lowercase							=     {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
							self._test_distiller_cli(lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[Any])						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     {
							    """student_encoder_layers""": 2,
							    """student_decoder_layers""": 1,
							    """max_epochs""": 4,
							    """val_check_interval""": 0.25,
							    """alpha_hid""": 2.0,
							    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
							}
							_lowercase							=     self._test_distiller_cli(lowercase__    ,			check_contents=lowercase__)

							_lowercase							=     list(Path(model.output_dir).glob("""*.ckpt"""))
							self.assertEqual(1    ,			len(lowercase__))
							_lowercase							=     list(Path(model.output_dir).glob("""**/*.bin"""))
							self.assertEqual(len(lowercase__)    ,			2)
							_lowercase							=     lmap(str.strip    ,			Path(model.hparams.data_dir).joinpath("""test.source""").open().readlines())
							_lowercase							=     tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(lowercase__    ,			lowercase__    ,			str(model.output_dir / """best_tfmr"""))
							self.assertTrue(Path(lowercase__).exists())

							_lowercase							=     self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]    ,			transformer_ckpts[0].parent    ,			lowercase__)
							assert os.path.exists(os.path.join(lowercase__    ,			"""pytorch_model.bin"""))

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     AutoModelForSeqaSeqLM.from_pretrained(lowercase__)
							_lowercase		,		_lowercase							=     model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
							_lowercase							=     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,			dtype=torch.long    ,			device=model.device)
							_lowercase							=     target_ids[:, :-1].contiguous()  # Why this line?
							_lowercase							=     target_ids[:, 1:].clone()  # why clone?
							_lowercase							=     model(
							    lowercase__    ,			attention_mask=lowercase__    ,			decoder_input_ids=lowercase__    ,			labels=lowercase__    ,			use_cache=lowercase__).loss

							_lowercase							=     model(lowercase__    ,			attention_mask=lowercase__    ,			decoder_input_ids=lowercase__    ,			use_cache=lowercase__).logits

							_lowercase							=     nn.functional.log_softmax(lowercase__    ,			dim=-1)
							_lowercase		,		_lowercase							=     label_smoothed_nll_loss(
							    lowercase__    ,			lowercase__    ,			0.1    ,			ignore_index=model.config.pad_token_id)
							with self.assertRaises(lowercase__):
								# TODO: understand why this breaks
								self.assertEqual(lowercase__    ,			lowercase__)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							_lowercase							=     {
							    """student_encoder_layers""": 2,
							    """student_decoder_layers""": 1,
							    """num_train_epochs""": 4,
							    """val_check_interval""": 0.25,
							    """alpha_hid""": 2.0,
							    """task""": """translation""",
							    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
							    """tokenizer_name""": MBART_TINY,
							    """teacher""": MBART_TINY,
							    """src_lang""": """en_XX""",
							    """tgt_lang""": """ro_RO""",
							}
							_lowercase							=     self._test_distiller_cli(lowercase__    ,			check_contents=lowercase__)
							assert model.model.config.model_type == "mbart"

							_lowercase							=     list(Path(model.output_dir).glob("""*.ckpt"""))
							self.assertEqual(1    ,			len(lowercase__))
							_lowercase							=     list(Path(model.output_dir).glob("""**/*.bin"""))
							_lowercase							=     list(Path(model.output_dir).glob("""best_tfmr/*"""))
							assert len(lowercase__) > 2
							self.assertEqual(len(lowercase__)    ,			2)

						def 	_UpperCAmelCase							(		self	:						List[str])						->int:

							"""simple docstring"""



							_lowercase							=     {
							    """student_encoder_layers""": 1,
							    """student_decoder_layers""": 1,
							    """alpha_hid""": 2.0,
							    """teacher""": T5_TINY,
							    """model_name_or_path""": T5_TINY,
							    """tokenizer_name""": T5_TINY,
							}
							self._test_distiller_cli(lowercase__)

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->str:

							"""simple docstring"""



							_lowercase							=     {
							    """teacher""": T5_TINY,
							    """student""": T5_TINIER,
							    """model_name_or_path""": T5_TINIER,
							    """tokenizer_name""": T5_TINIER,
							}
							self._test_distiller_cli(lowercase__)


						def 	_UpperCAmelCase							(		self	:						int    ,			lowercase__	:						Any    ,			lowercase__	:						Optional[Any]=True)						->Dict:

							"""simple docstring"""



							_lowercase							=     {
							    """label_smoothing""": 0.0,
							    """early_stopping_patience""": -1,
							    """train_batch_size""": 1,
							    """eval_batch_size""": 2,
							    """max_epochs""": 2,
							    """alpha_mlm""": 0.2,
							    """alpha_ce""": 0.8,
							    """do_predict""": True,
							    """model_name_or_path""": """sshleifer/tinier_bart""",
							    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
							    """val_check_interval""": 0.5,
							}
							default_updates.update(lowercase__)
							_lowercase							=     CHEAP_ARGS.copy()
							_lowercase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_lowercase							=     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=lowercase__    ,			output_dir=lowercase__    ,			**lowercase__)
							_lowercase							=     distill_main(argparse.Namespace(**lowercase__))
							if not check_contents:
								return model
							_lowercase							=     os.listdir(lowercase__)
							_lowercase							=     {os.path.basename(lowercase__) for p in contents}
							_lowercase							=     [p for p in contents if p.endswith("""ckpt""")]
							assert len(lowercase__) > 0

							self.assertIn("""test_generations.txt"""    ,			lowercase__)
							self.assertIn("""test_results.txt"""    ,			lowercase__)

							_lowercase							=     load_json(model.metrics_save_path)
							_lowercase							=     metrics["""val"""][-1]
							self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]    ,			0.01)
							self.assertGreaterEqual(1.0    ,			last_step_stats["""val_avg_gen_time"""])
							self.assertIsInstance(last_step_stats[f"""val_avg_{model.val_metric}"""]    ,			lowercase__)
							_lowercase							=     int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1)
							self.assertEqual(len(metrics["""val"""])    ,			lowercase__)
							self.assertEqual(len(metrics["""test"""])    ,			1)
							return model






class 					__a   (		_snake_case  ):

						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,			)
						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						Tuple)						->Dict:

							"""simple docstring"""



							_lowercase							=     CHEAP_ARGS.copy()
							_lowercase							=     """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
							_lowercase							=     0.1 if task == """translation""" else 0

							_lowercase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_lowercase							=     self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=lowercase__    ,			model_name_or_path=lowercase__    ,			tokenizer_name=lowercase__    ,			train_batch_size=2    ,			eval_batch_size=2    ,			output_dir=lowercase__    ,			do_predict=lowercase__    ,			task=lowercase__    ,			src_lang="""en_XX"""    ,			tgt_lang="""ro_RO"""    ,			freeze_encoder=lowercase__    ,			freeze_embeds=lowercase__    ,			)
							assert "n_train" in args_d
							_lowercase							=     argparse.Namespace(**lowercase__)
							_lowercase							=     main(lowercase__)

							_lowercase							=     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
								_lowercase							=     module.model.lm_head
								assert not lm_head.weight.requires_grad
								assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
								_lowercase							=     module.model.model
								_lowercase							=     fsmt.decoder.embed_positions
								assert not embed_pos.weight.requires_grad
								assert not fsmt.decoder.embed_tokens.weight.requires_grad
								# check that embeds are not the same
								assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
								_lowercase							=     module.model.model
								_lowercase							=     bart.decoder.embed_positions
								assert not embed_pos.weight.requires_grad
								assert not bart.shared.weight.requires_grad
								# check that embeds are the same
								assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
								assert bart.decoder.embed_tokens == bart.shared

							_lowercase							=     load_json(module.output_dir / """text_batch.json""")
							assert isinstance(lowercase__    ,			lowercase__)
							assert len(lowercase__) >= 4

						def 	_UpperCAmelCase							(		self	:						List[Any])						->Dict:

							"""simple docstring"""



							_lowercase							=     CHEAP_ARGS.copy()

							_lowercase							=     """summarization"""
							_lowercase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

							args_d.update(
							    data_dir=lowercase__    ,			tokenizer_name=lowercase__    ,			train_batch_size=2    ,			eval_batch_size=2    ,			do_predict=lowercase__    ,			task=lowercase__    ,			src_lang="""en_XX"""    ,			tgt_lang="""ro_RO"""    ,			freeze_encoder=lowercase__    ,			freeze_embeds=lowercase__    ,			)

							# test models whose config includes the extra_model_args
							_lowercase							=     BART_TINY
							_lowercase							=     self.get_auto_remove_tmp_dir()
							_lowercase							=     args_d.copy()
							args_da.update(
							    model_name_or_path=lowercase__    ,			output_dir=lowercase__    ,			)
							_lowercase							=     ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
							for p in extra_model_params:
								_lowercase							=     0.5
							_lowercase							=     argparse.Namespace(**lowercase__)
							_lowercase							=     main(lowercase__)
							for p in extra_model_params:
								assert getattr(model.config    ,			lowercase__) == 0.5, f"""failed to override the model config for param {p}"""

							# test models whose config doesn't include the extra_model_args
							_lowercase							=     T5_TINY
							_lowercase							=     self.get_auto_remove_tmp_dir()
							_lowercase							=     args_d.copy()
							args_da.update(
							    model_name_or_path=lowercase__    ,			output_dir=lowercase__    ,			)
							_lowercase							=     """encoder_layerdrop"""
							_lowercase							=     0.5
							_lowercase							=     argparse.Namespace(**lowercase__)
							with pytest.raises(lowercase__) as excinfo:
								_lowercase							=     main(lowercase__)
							assert str(excinfo.value) == f"""model config doesn't have a `{unsupported_param}` attribute"""


						def 	_UpperCAmelCase							(		self	:						Tuple)						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     CHEAP_ARGS.copy()

							_lowercase							=     """summarization"""
							_lowercase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

							_lowercase							=     BART_TINY
							_lowercase							=     self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=lowercase__    ,			model_name_or_path=lowercase__    ,			output_dir=lowercase__    ,			tokenizer_name=lowercase__    ,			train_batch_size=2    ,			eval_batch_size=2    ,			do_predict=lowercase__    ,			task=lowercase__    ,			src_lang="""en_XX"""    ,			tgt_lang="""ro_RO"""    ,			freeze_encoder=lowercase__    ,			freeze_embeds=lowercase__    ,			)

							# emulate finetune.py
							_lowercase							=     argparse.ArgumentParser()
							_lowercase							=     pl.Trainer.add_argparse_args(lowercase__)
							_lowercase							=     SummarizationModule.add_model_specific_args(lowercase__    ,			os.getcwd())
							_lowercase							=     {"""--help""": True}

							# --help test
							with pytest.raises(lowercase__) as excinfo:
								with CaptureStdout() as cs:
									_lowercase							=     parser.parse_args(lowercase__)
								assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							_lowercase							=     lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							_lowercase							=     """non_existing_scheduler"""
							_lowercase							=     {f"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(lowercase__) as excinfo:
								with CaptureStderr() as cs:
									_lowercase							=     parser.parse_args(lowercase__)
								assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							_lowercase							=     f"""invalid choice: '{unsupported_param}'"""
							assert expected in cs.err, f"""should have bailed on invalid choice of scheduler {unsupported_param}"""

							# --lr_scheduler=existing_scheduler test
							_lowercase							=     """cosine"""
							_lowercase							=     args_d.copy()
							_lowercase							=     supported_param
							_lowercase							=     argparse.Namespace(**lowercase__)
							_lowercase							=     main(lowercase__)
							assert (
							    getattr(model.hparams    ,			"""lr_scheduler""") == supported_param
							), f"""lr_scheduler={supported_param} shouldn't fail"""







'''simple docstring'''







import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


_lowerCamelCase    =  {}
for task, _ in pipeline_test_mapping.items():
							_lowerCamelCase    =  {'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
_lowerCamelCase    =  {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):

	if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "pt"
	elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "tf"
	elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "flax"
	else:
		return None






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	# Use the cached results
	if PIPELINE_TEST_MAPPING[task].get(snake_case_							,       snake_case_	) is not None:
		return PIPELINE_TEST_MAPPING[task][framework]

	_lowercase							=     pipeline_test_mapping[task]["""test"""]
	_lowercase							=     None

	if framework == "pt":
		_lowercase							=     getattr(snake_case_							,       """model_mapping"""							,       snake_case_	)
	elif framework == "tf":
		_lowercase							=     getattr(snake_case_							,       """tf_model_mapping"""							,       snake_case_	)

	if mapping is not None:
		_lowercase							=     dict(mapping.items()	)

	# cache the results
	_lowercase							=     mapping
	return mapping






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework is None:
		return None
	_lowercase							=     get_mapping_for_task(snake_case_							,       snake_case_	)
	if mapping is None:
		return None

	_lowercase							=     list({model_class.config_class for model_class in test_class.all_model_classes}	)
	if len(snake_case_	) != 1:
		raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""	)

	# This could be a list/tuple of model classes, but it's rare.
	_lowercase							=     mapping.get(config_classes[0]							,       snake_case_	)
	if isinstance(snake_case_							,       (tuple, list)	):
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: x.__name__	)

	return model_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [(task, get_model_for_pipeline_test(snake_case_							,       snake_case_	)) for task in pipeline_test_mapping]
	_lowercase							=     sorted([(task, model) for task, model in mapping if model is not None]							,       key=lambda snake_case_	: x[0]	)

	return dict(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework == "pt":
		_lowercase							=     """torch"""
	_lowercase							=     """{}"""

	_lowercase							=     get_pipeline_model_mapping(snake_case_	)
	if len(snake_case_	) == 0:
		return ""

	_lowercase							=     []
	for task, model_classes in mapping.items():
		if isinstance(snake_case_							,       (tuple, list)	):
			# A list/tuple of model classes
			_lowercase							=     """(""" + """, """.join([x.__name__ for x in model_classes]	) + """)"""
		else:
			# A single model class
			_lowercase							=     model_classes.__name__
		texts.append(F"""\"{task}\": {value}"""	)
	_lowercase							=     """{""" + """, """.join(snake_case_	) + """}"""
	_lowercase							=     F"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

	return text






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
	if not issubclass(snake_case_							,       unittest.TestCase	):
		return False
	return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]	)	) > 0






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [x for x in get_test_classes(snake_case_	) if is_valid_test_class(snake_case_	)]

	_lowercase							=     None
	for test_class in test_classes:
		# If a test class has defined `pipeline_model_mapping`, let's take it
		if getattr(snake_case_							,       """pipeline_model_mapping"""							,       snake_case_	) is not None:
			_lowercase							=     test_class
			break
    # Take the test class with the shortest name (just a heuristic)
	if target_test_class is None and len(snake_case_	) > 0:
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: (len(x.__name__	), x.__name__)	)[0]

	return target_test_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     start_idx
	for idx, line in enumerate(lines[start_idx:]	):
		_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
		if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
			_lowercase							=     start_idx + idx
		elif idx > 0 and indent <= indent_level:
			# Outside the definition block of `pipeline_model_mapping`
			break

	return end_idx






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	if getattr(snake_case_							,       """pipeline_model_mapping"""							,       snake_case_	) is not None:
		if not overwrite:
			return "", -1

	_lowercase							=     get_pipeline_model_mapping_string(snake_case_	)
	if len(snake_case_	) == 0:
		return "", -1
	_lowercase							=     line_to_add + """\n"""

	# The code defined the class `test_class`
	_lowercase		,		_lowercase							=     inspect.getsourcelines(snake_case_	)
	# `inspect` gives the code for an object, including decorator(s) if any.
	# We (only) need the exact line of the class definition.
	for idx, line in enumerate(snake_case_	):
		if line.lstrip().startswith("""class """	):
			_lowercase							=     class_lines[idx:]
			class_start_line_no += idx
			break
	_lowercase							=     class_start_line_no + len(snake_case_	) - 1

	# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
	# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
	# block that appears in a `test_class`.
	_lowercase							=     None
	# The indent level of the line at `class_lines[start_idx]` (if defined)
	_lowercase							=     0
	# To record if `pipeline_model_mapping` is found in `test_class`.
	_lowercase							=     None
	for idx, line in enumerate(snake_case_	):
		if line.strip().startswith("""all_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""all_generative_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""pipeline_model_mapping = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
			_lowercase							=     line
			break

	if start_idx is None:
		return "", -1
	# Find the ending index (inclusive) of the above found block.
	_lowercase							=     find_block_ending(snake_case_							,       snake_case_							,       snake_case_	)

	# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
	# `is_timm_available()` instead of `is_torch_available()`.
	# Keep leading and trailing whitespaces
	_lowercase							=     re.compile(r"""\s(is_\S+?_available\(\))\s"""	)
	for line in class_lines[start_idx : end_idx + 1]:
		_lowercase							=     r.search(snake_case_	)
		if backend_condition is not None:
			# replace the leading and trailing whitespaces to the space character " ".
			_lowercase							=     """ """ + backend_condition[0][1:-1] + """ """
			_lowercase							=     r.sub(snake_case_							,       snake_case_	)
			break

	if def_line is None:
		# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
		# `all_model_classes` or `all_generative_model_classes`.
		_lowercase							=     end_idx
	else:
		# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
		_lowercase							=     start_idx - 1
		# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
		for idx in range(snake_case_							,       end_idx + 1	):
			# These lines are going to be removed before writing to the test file.
			_lowercase							=     None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
	_lowercase							=     [x.__name__ for x in test_class.__bases__]
	if "PipelineTesterMixin" not in parent_classes:
		# Put `PipelineTesterMixin` just before `unittest.TestCase`
		_lowercase							=     [x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
		if "TestCase" in parent_classes:
			# Here we **assume** the original string is always with `unittest.TestCase`.
			_parent_classes.append("""unittest.TestCase"""	)
		_lowercase							=     """, """.join(_parent_classes	)
		for idx, line in enumerate(snake_case_	):
			# Find the ending of the declaration of `test_class`
			if line.strip().endswith("""):"""	):
				# mark the lines of the declaration of `test_class` to be removed
				for _idx in range(idx + 1	):
					_lowercase							=     None  # noqa
				break
        # Add the new, one-line, class declaration for `test_class`
		_lowercase							=     F"""class {test_class.__name__}({parent_classes}):\n"""

	# Add indentation
	_lowercase							=     """ """ * indent_level + line_to_add
	# Insert `pipeline_model_mapping` to `class_lines`.
	# (The line at `target_idx` should be kept by definition!)
	_lowercase							=     class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
	# Remove the lines that are marked to be removed
	_lowercase							=     [x for x in class_lines if x is not None]

	# Move from test class to module (in order to write to the test file)
	_lowercase							=     inspect.getsourcelines(inspect.getmodule(snake_case_	)	)[0]
	# Be careful with the 1-off between line numbers and array indices
	_lowercase							=     module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
	_lowercase							=     """""".join(snake_case_	)

	_lowercase							=     inspect.getsourcefile(snake_case_	)
	with open(snake_case_							,       """w"""							,       encoding="""UTF-8"""							,       newline="""\n"""	) as fp:
		fp.write(snake_case_	)

	return line_to_add






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	_lowercase							=     find_test_class(snake_case_	)
	if test_class:
		add_pipeline_model_mapping(snake_case_							,       overwrite=snake_case_	)


if __name__ == "__main__":
							_lowerCamelCase    =  argparse.ArgumentParser()
							parser.add_argument(
							    '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
							)
							parser.add_argument(
							    '--all',
							    action='store_true',
							    help='If to check and modify all test files.',
							)
							parser.add_argument(
							    '--overwrite',
							    action='store_true',
							    help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
							)
							_lowerCamelCase    =  parser.parse_args()

							if not args.all and not args.test_file:
														raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
							elif args.all and args.test_file:
														raise ValueError('Only one of `--test_file` and `--all` could be specified.')

							_lowerCamelCase    =  []
							if args.test_file:
														_lowerCamelCase    =  [args.test_file]
							else:
														_lowerCamelCase    =  os.path.join('tests', 'models', '**', 'test_modeling_*.py')
														for test_file in glob.glob(pattern):
																					# `Flax` is not concerned at this moment
																					if not test_file.startswith('test_modeling_flax_'):
																												test_files.append(test_file)

							for test_file in test_files:
														if test_file in TEST_FILE_TO_IGNORE:
																					print(F"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
																					continue
														add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)




