

'''simple docstring'''







import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
							from comet_ml import OfflineExperiment

if is_tensorboard_available():
							import struct

							import tensorboard.compat.proto.event_pba as event_pba

_lowerCamelCase    =  logging.getLogger(__name__)
@require_tensorboard
class 					__a   (		unittest.TestCase  ):

						def 	_UpperCAmelCase							(		self	:						Tuple)						->str:

							"""simple docstring"""



							_lowercase							=     """test_project_with_config"""
							with tempfile.TemporaryDirectory() as dirpath:
								_lowercase							=     Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase__)
								_lowercase							=     {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
								accelerator.init_trackers(lowercase__    ,			lowercase__)
								accelerator.end_training()
								for child in Path(f"""{dirpath}/{project_name}""").glob("""*/**"""):
									_lowercase							=     list(filter(lambda lowercase__: x.is_file()    ,			child.iterdir()))[0]
								self.assertNotEqual(str(lowercase__)    ,			"""""")

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

							"""simple docstring"""



							_lowercase							=     """test_project_with_log"""
							with tempfile.TemporaryDirectory() as dirpath:
								_lowercase							=     Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase__)
								accelerator.init_trackers(lowercase__)
								_lowercase							=     {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
								accelerator.log(lowercase__    ,			step=0)
								accelerator.end_training()
								# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
								# Names are randomly generated each time
								_lowercase							=     list(filter(lambda lowercase__: x.is_file()    ,			Path(f"""{dirpath}/{project_name}""").iterdir()))[0]
								self.assertNotEqual(str(lowercase__)    ,			"""""")

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

							"""simple docstring"""



							_lowercase							=     """test_project_with_log"""
							with tempfile.TemporaryDirectory() as dirpath:
								_lowercase							=     Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase__)
								accelerator.init_trackers(lowercase__)
								_lowercase							=     {"""tensor""": torch.tensor(1)}
								accelerator.log(lowercase__    ,			step=0)
								accelerator.end_training()
								# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
								# Names are randomly generated each time
								_lowercase							=     list(filter(lambda lowercase__: x.is_file()    ,			Path(f"""{dirpath}/{project_name}""").iterdir()))[0]
								# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
								with open(lowercase__    ,			"""rb""") as f:
									_lowercase							=     f.read()
								_lowercase							=     False
								while data:
									_lowercase							=     struct.unpack("""Q"""    ,			data[:8])

									_lowercase							=     data[12 : 12 + int(header[0])]  # 8+4
									_lowercase							=     data[12 + int(header[0]) + 4 :]
									_lowercase							=     event_pba.Event()

									event.ParseFromString(lowercase__)
									if event.HasField("""summary"""):
										for value in event.summary.value:
											if value.simple_value == 1.0 and value.tag == "tensor":
												_lowercase							=     True
								self.assertTrue(lowercase__    ,			"""Converted tensor was not found in the log file!""")

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

							"""simple docstring"""



							with self.assertRaisesRegex(lowercase__    ,			"""Logging with `tensorboard` requires a `logging_dir`"""):
								_lowercase							=     Accelerator(log_with="""tensorboard""")
							with tempfile.TemporaryDirectory() as dirpath:
								_lowercase							=     Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase__)


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

							"""simple docstring"""



							_lowercase							=     ProjectConfiguration(total_limit=30)
							with tempfile.TemporaryDirectory() as dirpath:
								_lowercase							=     Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase__    ,			project_config=lowercase__)






@require_wandb
@mock.patch.dict(os.environ							,{'WANDB_MODE': 'offline'}  )
class 					__a   (		_snake_case							,_snake_case  ):

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

							"""simple docstring"""



							super().setUp()
							# wandb let's us override where logs are stored to via the WANDB_DIR env var
							self.add_mocks(mock.patch.dict(os.environ    ,			{"""WANDB_DIR""": self.tmpdir}))

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						str    ,			lowercase__	:						str    ,			lowercase__	:						bool = True)						->Tuple:

							"""simple docstring"""



							_lowercase							=     rf"""{section} ([\S\s]*?)\n\n"""
							if record:
								_lowercase							=     rf"""Record: {pattern}"""
							_lowercase							=     re.findall(lowercase__    ,			lowercase__)[0]
							# A config
							if section == "config" or section == "history":
								_lowercase							=     re.findall(r"""\"([a-zA-Z0-9_.,]+)"""    ,			lowercase__)
								return {key: val for key, val in zip(cleaned_record[0::2]    ,			cleaned_record[1::2])}
							# Everything else
							else:
								return dict(re.findall(r"""(\w+): \"([^\s]+)\""""    ,			lowercase__))


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

							"""simple docstring"""



							_lowercase							=     """test_project_with_config"""
							_lowercase							=     Accelerator(log_with="""wandb""")
							_lowercase							=     {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
							_lowercase							=     {"""wandb""": {"""tags""": ["""my_tag"""]}}
							accelerator.init_trackers(lowercase__    ,			lowercase__    ,			lowercase__)
							_lowercase							=     {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
							accelerator.log(lowercase__    ,			step=0)
							accelerator.end_training()
							# The latest offline log is stored at wandb/latest-run/*.wandb
							for child in Path(f"""{self.tmpdir}/wandb/latest-run""").glob("""*"""):
								if child.is_file() and child.suffix == ".wandb":
									_lowercase							=     subprocess.check_output(
									    ["""wandb""", """sync""", """--view""", """--verbose""", str(lowercase__)]    ,			env=os.environ.copy()).decode("""utf8"""    ,			"""ignore""")
									break

        # Check HPS through careful parsing and cleaning
							_lowercase							=     self.parse_log(lowercase__    ,			"""config""")
							self.assertEqual(logged_items["""num_iterations"""]    ,			"""12""")
							self.assertEqual(logged_items["""learning_rate"""]    ,			"""0.01""")
							self.assertEqual(logged_items["""some_boolean"""]    ,			"""false""")
							self.assertEqual(logged_items["""some_string"""]    ,			"""some_value""")
							self.assertEqual(logged_items["""some_string"""]    ,			"""some_value""")

							# Run tags
							_lowercase							=     self.parse_log(lowercase__    ,			"""run"""    ,			lowercase__)
							self.assertEqual(logged_items["""tags"""]    ,			"""my_tag""")

							# Actual logging
							_lowercase							=     self.parse_log(lowercase__    ,			"""history""")
							self.assertEqual(logged_items["""total_loss"""]    ,			"""0.1""")
							self.assertEqual(logged_items["""iteration"""]    ,			"""1""")
							self.assertEqual(logged_items["""my_text"""]    ,			"""some_value""")
							self.assertEqual(logged_items["""_step"""]    ,			"""0""")






def 					_SCREAMING_SNAKE_CASE		(						self							,       snake_case_							,       snake_case_	):
	_lowercase							=     run_name
	_lowercase							=     OfflineExperiment(project_name=snake_case_							,       offline_directory=snake_case_	)
	logger.info(F"""Initialized offline CometML project {self.run_name}"""	)
	logger.info("""Make sure to log any initial configurations with `self.store_init_configuration` before training!"""	)
@require_comet_ml
@mock.patch.object(_snake_case							,'__init__'							,_snake_case  )
class 					__a   (		unittest.TestCase  ):

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						List[Any]    ,			lowercase__	:						str    ,			lowercase__	:						bool = False)						->List[str]:

							"""simple docstring"""



							for log in log_list:
								_lowercase							=     json.loads(lowercase__)["""payload"""]
								if is_param and "param" in j.keys():
									if j["param"]["paramName"] == key:
										return j["param"]["paramValue"]
								if "log_other" in j.keys():
									if j["log_other"]["key"] == key:
										return j["log_other"]["val"]
								if "metric" in j.keys():
									if j["metric"]["metricName"] == key:
										return j["metric"]["metricValue"]

						def 	_UpperCAmelCase							(		self	:						int)						->Any:

							"""simple docstring"""



							with tempfile.TemporaryDirectory() as d:
								_lowercase							=     CometMLTracker("""test_project_with_config"""    ,			lowercase__)
								_lowercase							=     Accelerator(log_with=lowercase__)
								_lowercase							=     {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
								accelerator.init_trackers(lowercase__    ,			lowercase__)
								accelerator.end_training()
								_lowercase							=     os.listdir(lowercase__)[0]  # Comet is nice, it's just a zip file here
								# We parse the raw logs
								_lowercase							=     os.path.join(lowercase__    ,			lowercase__)
								_lowercase							=     zipfile.ZipFile(lowercase__    ,			"""r""")
								_lowercase							=     archive.open("""messages.json""").read().decode("""utf-8""")
							_lowercase							=     log.split("""\n""")[:-1]
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""num_iterations"""    ,			lowercase__)    ,			12)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""learning_rate"""    ,			lowercase__)    ,			0.01)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""some_boolean"""    ,			lowercase__)    ,			lowercase__)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""some_string"""    ,			lowercase__)    ,			"""some_value""")


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

							"""simple docstring"""



							with tempfile.TemporaryDirectory() as d:
								_lowercase							=     CometMLTracker("""test_project_with_config"""    ,			lowercase__)
								_lowercase							=     Accelerator(log_with=lowercase__)
								accelerator.init_trackers(lowercase__)
								_lowercase							=     {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
								accelerator.log(lowercase__    ,			step=0)
								accelerator.end_training()
								_lowercase							=     os.listdir(lowercase__)[0]  # Comet is nice, it's just a zip file here
								# We parse the raw logs
								_lowercase							=     os.path.join(lowercase__    ,			lowercase__)
								_lowercase							=     zipfile.ZipFile(lowercase__    ,			"""r""")
								_lowercase							=     archive.open("""messages.json""").read().decode("""utf-8""")
							_lowercase							=     log.split("""\n""")[:-1]
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""curr_step"""    ,			lowercase__)    ,			0)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""total_loss""")    ,			0.1)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""iteration""")    ,			1)
							self.assertEqual(self.get_value_from_key(lowercase__    ,			"""my_text""")    ,			"""some_value""")






class 					__a   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					Dict  =      [
						    'total_loss',
						    'iteration',
						    'my_text',
						    'learning_rate',
						    'num_iterations',
						    'some_boolean',
						    'some_string',
						]

						__SCREAMING_SNAKE_CASE     :					List[Any]  =      'my_custom_tracker'
						__SCREAMING_SNAKE_CASE     :					List[Any]  =      False

						def __init__(		self	:						Any    ,			lowercase__	:						str)						->int:

							"""simple docstring"""



							_lowercase							=     open(f"""{dir}/log.csv"""    ,			"""w+""")
							_lowercase							=     csv.DictWriter(self.f    ,			fieldnames=self._col_names)
							self.writer.writeheader()

						@property
						def 	_UpperCAmelCase							(		self	:						List[Any])						->Union[str, Any]:

							"""simple docstring"""



							return self.writer

						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						dict)						->List[str]:

							"""simple docstring"""



							logger.info("""Call init""")
							self.writer.writerow(lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[Any]    ,			lowercase__	:						dict    ,			lowercase__	:						Optional[int])						->Union[str, Any]:

							"""simple docstring"""



							logger.info("""Call log""")
							self.writer.writerow(lowercase__)


						def 	_UpperCAmelCase							(		self	:						Dict)						->Union[str, Any]:

							"""simple docstring"""



							self.f.close()






class 					__a   (		unittest.TestCase  ):

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

							"""simple docstring"""



							with tempfile.TemporaryDirectory() as d:
								_lowercase							=     MyCustomTracker(lowercase__)
								_lowercase							=     Accelerator(log_with=lowercase__)
								_lowercase							=     {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
								accelerator.init_trackers("""Some name"""    ,			lowercase__)
								accelerator.end_training()
								with open(f"""{d}/log.csv"""    ,			"""r""") as f:
									_lowercase							=     csv.DictReader(lowercase__)
									_lowercase							=     next(lowercase__)
									_lowercase							=     {
									    """total_loss""": """""",
									    """iteration""": """""",
									    """my_text""": """""",
									    """learning_rate""": """0.01""",
									    """num_iterations""": """12""",
									    """some_boolean""": """False""",
									    """some_string""": """some_value""",
									}
									self.assertDictEqual(lowercase__    ,			lowercase__)


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

							"""simple docstring"""



							with tempfile.TemporaryDirectory() as d:
								_lowercase							=     MyCustomTracker(lowercase__)
								_lowercase							=     Accelerator(log_with=lowercase__)
								accelerator.init_trackers("""Some name""")
								_lowercase							=     {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
								accelerator.log(lowercase__    ,			step=0)
								accelerator.end_training()
								with open(f"""{d}/log.csv"""    ,			"""r""") as f:
									_lowercase							=     csv.DictReader(lowercase__)
									_lowercase							=     next(lowercase__)
									_lowercase							=     {
									    """total_loss""": """0.1""",
									    """iteration""": """1""",
									    """my_text""": """some_value""",
									    """learning_rate""": """""",
									    """num_iterations""": """""",
									    """some_boolean""": """""",
									    """some_string""": """""",
									}
									self.assertDictEqual(lowercase__    ,			lowercase__)







'''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)




