





'''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


snake_case_   :  Union[str, Any]								=      {}
for task, _ in pipeline_test_mapping.items():
			snake_case_   :  int								=      {'''pt''': None, '''tf''': None}


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



def  lowercase__(		_UpperCamelCase  : Union[str, Any]				)->   str:






					"""simple docstring"""

					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  lowercase__(		_UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : List[str]				)->   Optional[int]:






					"""simple docstring"""

					if PIPELINE_TEST_MAPPING[task].get(_UpperCamelCase ,   _UpperCamelCase				) is not None:
										return PIPELINE_TEST_MAPPING[task][framework]

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

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

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

					# cache the results
					_UpperCamelCase											=   mapping
					return mapping



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Tuple				)->   Optional[int]:






					"""simple docstring"""

					_UpperCamelCase											=   get_framework(_UpperCamelCase				)
					if framework is None:
										return None
					_UpperCamelCase											=   get_mapping_for_task(_UpperCamelCase ,   _UpperCamelCase				)
					if mapping is None:
										return None

					_UpperCamelCase											=   list({model_class.config_class for model_class in test_class.all_model_classes}				)
					if len(_UpperCamelCase				) != 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.
					_UpperCamelCase											=   mapping.get(config_classes[0] ,   _UpperCamelCase				)
					if isinstance(_UpperCamelCase ,   (tuple, list)				):
										_UpperCamelCase											=   sorted(_UpperCamelCase ,   key=lambda _UpperCamelCase				: x.__name__				)

					return model_class



def  lowercase__(		_UpperCamelCase  : List[Any]				)->   Optional[int]:






					"""simple docstring"""

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

					return dict(_UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : Optional[Any]				)->   Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   get_framework(_UpperCamelCase				)
					if framework == "pt":
										_UpperCamelCase											=   "torch"
					_UpperCamelCase											=   "{}"

					_UpperCamelCase											=   get_pipeline_model_mapping(_UpperCamelCase				)
					if len(_UpperCamelCase				) == 0:
										return ""

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

					return text



def  lowercase__(		_UpperCamelCase  : str				)->   Optional[int]:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : int				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   [x for x in get_test_classes(_UpperCamelCase				) if is_valid_test_class(_UpperCamelCase				)]

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

					return target_test_class



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : Any ,   _UpperCamelCase  : Tuple				)->   Optional[Any]:






					"""simple docstring"""

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

					return end_idx



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : int=False				)->   Any:






					"""simple docstring"""

					if getattr(_UpperCamelCase ,   "pipeline_model_mapping" ,   _UpperCamelCase				) is not None:
										if not overwrite:
															return "", -1

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

					# The code defined the class `test_class`
					_UpperCamelCase				,	_UpperCamelCase											=   inspect.getsourcelines(_UpperCamelCase				)
					# `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(_UpperCamelCase				):
										if line.lstrip().startswith("class "				):
															_UpperCamelCase											=   class_lines[idx:]
															class_start_line_no += idx
															break
					_UpperCamelCase											=   class_start_line_no + len(_UpperCamelCase				) - 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`.
					_UpperCamelCase											=   None
					# The indent level of the line at `class_lines[start_idx]` (if defined)
					_UpperCamelCase											=   0
					# To record if `pipeline_model_mapping` is found in `test_class`.
					_UpperCamelCase											=   None
					for idx, line in enumerate(_UpperCamelCase				):
										if line.strip().startswith("all_model_classes = "				):
															_UpperCamelCase											=   len(_UpperCamelCase				) - len(line.lstrip()				)
															_UpperCamelCase											=   idx
										elif line.strip().startswith("all_generative_model_classes = "				):
															_UpperCamelCase											=   len(_UpperCamelCase				) - len(line.lstrip()				)
															_UpperCamelCase											=   idx
										elif line.strip().startswith("pipeline_model_mapping = "				):
															_UpperCamelCase											=   len(_UpperCamelCase				) - len(line.lstrip()				)
															_UpperCamelCase											=   idx
															_UpperCamelCase											=   line
															break

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

					# 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
					_UpperCamelCase											=   re.compile(R"\s(is_\S+?_available\(\))\s"				)
					for line in class_lines[start_idx : end_idx + 1]:
										_UpperCamelCase											=   r.search(_UpperCamelCase				)
										if backend_condition is not None:
															# replace the leading and trailing whitespaces to the space character " ".
															_UpperCamelCase											=   " " + backend_condition[0][1:-1] + " "
															_UpperCamelCase											=   r.sub(_UpperCamelCase ,   _UpperCamelCase				)
															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`.
										_UpperCamelCase											=   end_idx
					else:
										# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
										_UpperCamelCase											=   start_idx - 1
										# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
										for idx in range(_UpperCamelCase ,   end_idx + 1				):
															# These lines are going to be removed before writing to the test file.
															_UpperCamelCase											=   None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
					_UpperCamelCase											=   [x.__name__ for x in test_class.__bases__]
					if "PipelineTesterMixin" not in parent_classes:
										# Put `PipelineTesterMixin` just before `unittest.TestCase`
										_UpperCamelCase											=   [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"				)
										_UpperCamelCase											=   ", ".join(_parent_classes				)
										for idx, line in enumerate(_UpperCamelCase				):
															# 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				):
																									_UpperCamelCase											=   None  # noqa
																				break
        # Add the new, one-line, class declaration for `test_class`
										_UpperCamelCase											=   f"class {test_class.__name__}({parent_classes}):\n"

					# Add indentation
					_UpperCamelCase											=   " " * indent_level + line_to_add
					# Insert `pipeline_model_mapping` to `class_lines`.
					# (The line at `target_idx` should be kept by definition!)
					_UpperCamelCase											=   class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
					# Remove the lines that are marked to be removed
					_UpperCamelCase											=   [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)
					_UpperCamelCase											=   inspect.getsourcelines(inspect.getmodule(_UpperCamelCase				)				)[0]
					# Be careful with the 1-off between line numbers and array indices
					_UpperCamelCase											=   module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
					_UpperCamelCase											=   "".join(_UpperCamelCase				)

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

					return line_to_add



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : List[Any]=False				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   find_test_class(_UpperCamelCase				)
					if test_class:
										add_pipeline_model_mapping(_UpperCamelCase ,   overwrite=_UpperCamelCase				)


if __name__ == "__main__":
			snake_case_   :  List[Any]								=      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`.''',
			)
			snake_case_   :  Optional[int]								=      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.''')

			snake_case_   :  List[Any]								=      []
			if args.test_file:
						snake_case_   :  Optional[Any]								=      [args.test_file]
			else:
						snake_case_   :  Union[str, Any]								=      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)










'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



