
"""simple docstring"""





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_a														=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_a														=		"""    \"\"\"
    Output class for the scheduler's step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
"""






class     _UpperCAmelCase(    unittest.TestCase	):



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






							'''simple docstring'''
							_UpperCamelCase							 =  tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir				,		'''schedulers/'''))
							_UpperCamelCase							 =  self.diffusers_dir
							shutil.copy(
							    os.path.join(__a				,		'''src/diffusers/schedulers/scheduling_ddpm.py''')				,		os.path.join(self.diffusers_dir				,		'''schedulers/scheduling_ddpm.py''')				,		)



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  '''src/diffusers'''
							shutil.rmtree(self.diffusers_dir)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a=None)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
							if overwrite_result is not None:
												_UpperCamelCase							 =  comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
							_UpperCamelCase							 =  black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19)
							_UpperCamelCase							 =  black.format_str(__a				,		mode=__a)
							_UpperCamelCase							 =  os.path.join(self.diffusers_dir				,		'''new_code.py''')
							with open(__a				,		'''w'''				,		newline='''\n''') as f:
												f.write(__a)
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(__a)) == 0)
							else:
												check_copies.is_copy_consistent(f.name				,		overwrite=__a)
												with open(__a				,		'''r''') as f:
																	self.assertTrue(f.read()				,		__a)



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''')
							self.assertEqual(__a				,		__a)




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






							'''simple docstring'''
							# Base copy consistency
							self.check_copy_consistency(
							    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,		'''DDPMSchedulerOutput'''				,		REFERENCE_CODE + '''\n'''				,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,		'''DDPMSchedulerOutput'''				,		__a				,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,		'''TestSchedulerOutput'''				,		re.sub('''DDPM'''				,		'''Test'''				,		__a)				,		)

							# Copy consistency with a really long name
							_UpperCamelCase							 =  '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
							self.check_copy_consistency(
							    F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''				,		F'''{long_class_name}SchedulerOutput'''				,		re.sub('''Bert'''				,		__a				,		__a)				,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,		'''TestSchedulerOutput'''				,		__a				,		overwrite_result=re.sub('''DDPM'''				,		'''Test'''				,		__a)				,		)








"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a														=		parser.parse_args()

				if args.model_type == "bert":
								_a														=		BertForMaskedLM.from_pretrained(args.model_name)
								_a														=		"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a														=		model.state_dict()
				_a														=		{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a														=		state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a														=		state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a														=		0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a														=		state_dict["""cls.predictions.decoder.weight"""]
				_a														=		state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a														=		state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a														=		state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






