
"""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  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir							,						'''schedulers/'''   )   )
							lowerCamelCase_					=  self.diffusers_dir
							shutil.copy(
							    os.path.join(UpperCAmelCase							,						'''src/diffusers/schedulers/scheduling_ddpm.py'''   )							,						os.path.join(self.diffusers_dir							,						'''schedulers/scheduling_ddpm.py'''   )							,						)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''src/diffusers'''
							shutil.rmtree(self.diffusers_dir   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							lowerCamelCase_					=  comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
										lowerCamelCase_					=  comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							lowerCamelCase_					=  black.Mode(target_versions={black.TargetVersion.PYaa}							,						line_length=119   )
							lowerCamelCase_					=  black.format_str(UpperCAmelCase							,						mode=UpperCAmelCase   )
							lowerCamelCase_					=  os.path.join(self.diffusers_dir							,						'''new_code.py'''   )
							with open(UpperCAmelCase							,						'''w'''							,						newline='''\n'''   ) as f:
										f.write(UpperCAmelCase   )
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase   )   ) == 0   )
							else:
										check_copies.is_copy_consistent(f.name							,						overwrite=UpperCAmelCase   )
										with open(UpperCAmelCase							,						'''r'''   ) as f:
													self.assertTrue(f.read()							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							# 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'''							,						UpperCAmelCase							,						)

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

							# Copy consistency with a really long name
							lowerCamelCase_					=  '''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'''							,						UpperCAmelCase							,						UpperCAmelCase   )							,						)

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





"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



