
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def 		lowercase_    (     A__	)							->							Dict:
    """simple docstring"""

    snake_case				  =      [
        "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(A__				,	A__	)



def 		lowercase_    (     A__	)							->							Tuple:
    """simple docstring"""

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



def 		lowercase_    (     A__	)							->							Tuple:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__	)							->							str:
    """simple docstring"""

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

    remove_ignore_keys_(A__	)
    rename_keys(A__	)

    snake_case				  =      state_dict["decoder.embed_tokens.weight"].shape[0]

    snake_case				  =      args.share_decoder_input_output_embed

    snake_case				  =      [int(A__	) for i in args.conv_kernel_sizes.split(","	)]
    snake_case				  =      SpeechaTextConfig(
        vocab_size=A__				,	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(A__	)				,	conv_channels=args.conv_channels				,	conv_kernel_sizes=A__				,	input_feat_per_channel=args.input_feat_per_channel				,	input_channels=args.input_channels				,	tie_word_embeddings=A__				,	num_beams=5				,	max_length=200				,	use_cache=A__				,	decoder_start_token_id=2				,	early_stopping=A__				,	)

    snake_case				  =      SpeechaTextForConditionalGeneration(A__	)
    snake_case  ,							snake_case				  =      model.model.load_state_dict(A__				,	strict=A__	)
    if len(A__	) > 0 and not set(A__	) <= {
        "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:
        snake_case				  =      make_linear_from_emb(model.model.decoder.embed_tokens	)
    else:
        snake_case				  =      lm_head_weights

    model.save_pretrained(A__	)


if __name__ == "__main__":
   _A						      =				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.")
   _A						      =				parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Dict     )		->		Dict:
         snake_case				  =      inspect.getfile(accelerate.test_utils     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
         snake_case				  =      os.path.sep.join(
             mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
         print(f'Command: {cmd}'     )
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,					cuda_visible_devices="0,1"     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )


if __name__ == "__main__":
   _A						      =				Accelerator()
   _A						      =				(accelerator.state.process_index + 2, 10)
   _A						      =				torch.randint(0, 10, shape).to(accelerator.device)

   _A						      =				""

   _A						      =				accelerator.pad_across_processes(tensor)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
      error_msg += "Padding was not done with the right value (0)."

   _A						      =				accelerator.pad_across_processes(tensor, pad_first=True)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   _A						      =				accelerator.state.num_processes - accelerator.state.process_index - 1
   if not torch.equal(tensora[index:], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[:index] == 0):
      error_msg += "Padding was not done with the right value (0)."

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)

