


'''simple docstring'''




import torch
from torch import autograd



class 			lowerCAmelCase					(					autograd.Function				):



	@staticmethod
	def 					lowercase  (       snake_case__			,       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     str   				=			inputs.numel()
		lowerCAmelCase       :     str   				=			int(0.0_0_5 * nb_elems     ) + 1
		if sigmoid:
			lowerCAmelCase       :     int   				=			(torch.sigmoid(snake_case__     ) > threshold).type(inputs.type()     )
		else:
			lowerCAmelCase       :     Optional[Any]   				=			(inputs > threshold).type(inputs.type()     )
		if mask.sum() < nb_min:
			# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
			lowerCAmelCase       :     Tuple   				=			inputs.flatten().kthvalue(max(nb_elems - nb_min			,       1     )     ).values
			lowerCAmelCase       :     str   				=			(inputs > k_threshold).type(inputs.type()     )
		return mask



	@staticmethod
	def 					lowercase  (       snake_case__			,       snake_case__     ):
		return gradOutput, None, None



class 			lowerCAmelCase					(					autograd.Function				):



	@staticmethod
	def 					lowercase  (       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Optional[int]   				=			inputs.clone()
		lowerCAmelCase				, lowerCAmelCase       :     List[str]   				=			inputs.flatten().sort(descending=snake_case__     )
		lowerCAmelCase       :     int   				=			int(threshold * inputs.numel()     )

		# flat_out and mask access the same memory.
		lowerCAmelCase       :     str   				=			mask.flatten()
		lowerCAmelCase       :     List[Any]   				=			0
		lowerCAmelCase       :     Union[str, Any]   				=			1
		return mask



	@staticmethod
	def 					lowercase  (       snake_case__			,       snake_case__     ):
		return gradOutput, None



class 			lowerCAmelCase					(					a				):



	@staticmethod
	def 					lowercase  (       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Optional[int]   				=			inputs.clone()
		lowerCAmelCase				, lowerCAmelCase       :     List[str]   				=			inputs.abs().flatten().sort(descending=snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			int(threshold * inputs.numel()     )

		# flat_out and mask access the same memory.
		lowerCAmelCase       :     Dict   				=			mask.flatten()
		lowerCAmelCase       :     Optional[int]   				=			0
		lowerCAmelCase       :     str   				=			1
		return mask




'''simple docstring'''




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_lowerCAmelCase	:		Optional[int]		  =						importlib.metadata.version('numpy')
_lowerCAmelCase	:		List[Any]		  =						'.'.join([str(x) for x in sys.version_info[:3]])



class 			lowerCAmelCase					(					a				):



	def 					lowercase  (       self     ):
		# lt + different version strings
		require_version_core('numpy<1000.4.5'     )
		require_version_core('numpy<1000.4'     )
		require_version_core('numpy<1000'     )

		# le
		require_version_core('numpy<=1000.4.5'     )
		require_version_core(f"numpy<={numpy_ver}"     )

		# eq
		require_version_core(f"numpy=={numpy_ver}"     )

		# ne
		require_version_core('numpy!=1000.4.5'     )

		# ge
		require_version_core('numpy>=1.0'     )
		require_version_core('numpy>=1.0.0'     )
		require_version_core(f"numpy>={numpy_ver}"     )

		# gt
		require_version_core('numpy>1.0.0'     )

		# mix
		require_version_core('numpy>1.0.0,<1000'     )

		# requirement w/o version
		require_version_core('numpy'     )

		# unmet requirements due to version conflict
		for req in ["numpy==1.0.0", "numpy>=1000.0.0", f"numpy<{numpy_ver}"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn('but found'			,       str(snake_case__     )     )

        # unmet requirements due to missing module
		for req in ["numpipypie>1", "numpipypie2"]:
			try:
				require_version_core(snake_case__     )
			except importlib.metadata.PackageNotFoundError as e:
				self.assertIn(f"The '{req}' distribution was not found and is required by this application"			,       str(snake_case__     )     )
				self.assertIn('Try: pip install transformers -U'			,       str(snake_case__     )     )

        # bogus requirements formats:
        # 1. whole thing
		for req in ["numpy??1.0.0", "numpy1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('requirement needs to be in the pip package format'			,       str(snake_case__     )     )
        # 2. only operators
		for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('need one of '			,       str(snake_case__     )     )



	def 					lowercase  (       self     ):
		# matching requirement
		require_version('python>=3.6.0'     )

		# not matching requirements
		for req in ["python>9.9.9", "python<3.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn(f"but found python=={python_ver}"			,       str(snake_case__     )     )
