import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 	A_ (							snake_case							:   Dict )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			[
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''encoder.embed_positions._float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(snake_case ,      snake_case )




def 	A_ (							snake_case							:   Tuple )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			list(s_dict.keys() )
						for key in keys:
												if "transformer_layers" in key:
																		__UpperCamelCase       =			s_dict.pop(snake_case )
												elif "subsample" in key:
																		__UpperCamelCase       =			s_dict.pop(snake_case )




def 	A_ (							snake_case							:   List[Any] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase  ,							__UpperCamelCase       =			emb.weight.shape
						__UpperCamelCase       =			nn.Linear(snake_case ,      snake_case ,      bias=snake_case )
						__UpperCamelCase       =			emb.weight.data
						return lin_layer




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   List[Any] )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )
						__UpperCamelCase       =			mam_aaa['''args''']
						__UpperCamelCase       =			mam_aaa['''model''']
						__UpperCamelCase       =			state_dict['''decoder.output_projection.weight''']

						remove_ignore_keys_(snake_case )
						rename_keys(snake_case )

						__UpperCamelCase       =			state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__UpperCamelCase       =			args.share_decoder_input_output_embed

						__UpperCamelCase       =			[int(snake_case ) for i in args.conv_kernel_sizes.split(''',''' )]
						__UpperCamelCase       =			SpeechaTextConfig(
						    vocab_size=snake_case ,      max_source_positions=args.max_source_positions ,      max_target_positions=args.max_target_positions ,      encoder_layers=args.encoder_layers ,      decoder_layers=args.decoder_layers ,      encoder_attention_heads=args.encoder_attention_heads ,      decoder_attention_heads=args.decoder_attention_heads ,      encoder_ffn_dim=args.encoder_ffn_embed_dim ,      decoder_ffn_dim=args.decoder_ffn_embed_dim ,      d_model=args.encoder_embed_dim ,      dropout=args.dropout ,      attention_dropout=args.attention_dropout ,      activation_dropout=args.activation_dropout ,      activation_function='''relu''' ,      num_conv_layers=len(snake_case ) ,      conv_channels=args.conv_channels ,      conv_kernel_sizes=snake_case ,      input_feat_per_channel=args.input_feat_per_channel ,      input_channels=args.input_channels ,      tie_word_embeddings=snake_case ,      num_beams=5 ,      max_length=200 ,      use_cache=snake_case ,      decoder_start_token_id=2 ,      early_stopping=snake_case ,      )

						__UpperCamelCase       =			SpeechaTextForConditionalGeneration(snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			model.model.load_state_dict(snake_case ,      strict=snake_case )
						if len(snake_case ) > 0 and not set(snake_case ) <= {
						    "encoder.embed_positions.weights",
						    "decoder.embed_positions.weights",
						}:
												raise ValueError(
												    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
												    f" but all the following weights are missing {missing}" )

						if tie_embeds:
												__UpperCamelCase       =			make_linear_from_emb(model.model.decoder.embed_tokens )
						else:
												__UpperCamelCase       =			lm_head_weights

						model.save_pretrained(snake_case )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				lowercase__							:							str									=			parser.parse_args()
				convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






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

import black


lowercase__							:							Optional[Any]									=			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.
lowercase__							:							Dict									=			"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"






class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()
													os.makedirs(os.path.join(self.diffusers_dir     ,	'''schedulers/'''			)			)
													__UpperCamelCase       =			self.diffusers_dir
													shutil.copy(
													    os.path.join(SCREAMING_SNAKE_CASE_     ,	'''src/diffusers/schedulers/scheduling_ddpm.py'''			)     ,	os.path.join(self.diffusers_dir     ,	'''schedulers/scheduling_ddpm.py'''			)     ,	)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			'''src/diffusers'''
													shutil.rmtree(self.diffusers_dir			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None			)->   Optional[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=119			)
													__UpperCamelCase       =			black.format_str(SCREAMING_SNAKE_CASE_     ,	mode=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			os.path.join(self.diffusers_dir     ,	'''new_code.py'''			)
													with open(SCREAMING_SNAKE_CASE_     ,	'''w'''     ,	newline='''\n'''			) as f:
																			f.write(SCREAMING_SNAKE_CASE_			)
													if overwrite_result is None:
																			self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE_			)			) == 0			)
													else:
																			check_copies.is_copy_consistent(f.name     ,	overwrite=SCREAMING_SNAKE_CASE_			)
																			with open(SCREAMING_SNAKE_CASE_     ,	'''r'''			) as f:
																									self.assertTrue(f.read()     ,	SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													__UpperCamelCase       =			check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''			)
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													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'''     ,	SCREAMING_SNAKE_CASE_     ,	)

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

													# 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'''     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	)

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




