




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				3_84
     if "tiny" in model_name:
          a     =				[3, 3, 9, 3]
          a     =				[96, 1_92, 3_84, 7_68]
     if "small" in model_name:
          a     =				[3, 3, 27, 3]
          a     =				[96, 1_92, 3_84, 7_68]
     if "base" in model_name:
          a     =				[3, 3, 27, 3]
          a     =				[1_28, 2_56, 5_12, 10_24]
          a     =				5_12
     if "large" in model_name:
          a     =				[3, 3, 27, 3]
          a     =				[1_92, 3_84, 7_68, 15_36]
          a     =				7_68
     if "xlarge" in model_name:
          a     =				[3, 3, 27, 3]
          a     =				[2_56, 5_12, 10_24, 20_48]
          a     =				10_24

     # set label information
     a     =				1_50
     a     =				"""huggingface/label-files"""
     a     =				"""ade20k-id2label.json"""
     a     =				json.load(open(hf_hub_download(lowerCAmelCase__		, lowerCAmelCase__		, repo_type="dataset")		, "r"))
     a     =				{int(lowerCAmelCase__): v for k, v in idalabel.items()}
     a     =				{v: k for k, v in idalabel.items()}

     a     =				ConvNextConfig(
         depths=lowerCAmelCase__		, hidden_sizes=lowerCAmelCase__		, out_features=["stage1", "stage2", "stage3", "stage4"])
     a     =				UperNetConfig(
         backbone_config=lowerCAmelCase__		, auxiliary_in_channels=lowerCAmelCase__		, num_labels=lowerCAmelCase__		, idalabel=lowerCAmelCase__		, labelaid=lowerCAmelCase__		, )

     return config

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Any:
     a     =				[]

     # fmt: off
     # stem
     rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight"))
     rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias"))
     rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight"))
     rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias"))
     # stages
     for i in range(len(config.backbone_config.depths)):
          for j in range(config.backbone_config.depths[i]):
               rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight'''))
               rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias'''))
          if i > 0:
               rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight'''))
               rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias'''))
               rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight'''))
               rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias'''))

          rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight'''))
          rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias'''))

     # decode head
     rename_keys.extend(
         [
             ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
             ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
             ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
             ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
         ])
     # fmt: on

     return rename_keys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				dct.pop(lowerCAmelCase__)
     a     =				val

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Any:
     a     =				{
         """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
         """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
         """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
         """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
         """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
     }
     a     =				model_name_to_url[model_name]
     a     =				torch.hub.load_state_dict_from_url(lowerCAmelCase__		, map_location="cpu")["""state_dict"""]

     a     =				get_upernet_config(lowerCAmelCase__)
     a     =				UperNetForSemanticSegmentation(lowerCAmelCase__)
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          a     =				state_dict.pop(lowerCAmelCase__)
          if "bn" in key:
               a     =				key.replace("bn"		, "batch_norm")
          a     =				val

     # rename keys
     a     =				create_rename_keys(lowerCAmelCase__)
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase__		, lowerCAmelCase__		, lowerCAmelCase__)

     model.load_state_dict(lowerCAmelCase__)

     # verify on image
     a     =				"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
     a     =				Image.open(requests.get(lowerCAmelCase__		, stream=lowerCAmelCase__).raw).convert("RGB")

     a     =				SegformerImageProcessor()
     a     =				processor(lowerCAmelCase__		, return_tensors="pt").pixel_values

     with torch.no_grad():
          a     =				model(lowerCAmelCase__)

     if model_name == "upernet-convnext-tiny":
          a     =				torch.tensor(
              [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]])
     elif model_name == "upernet-convnext-small":
          a     =				torch.tensor(
              [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]])
     elif model_name == "upernet-convnext-base":
          a     =				torch.tensor(
              [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]])
     elif model_name == "upernet-convnext-large":
          a     =				torch.tensor(
              [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]])
     elif model_name == "upernet-convnext-xlarge":
          a     =				torch.tensor(
              [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]])
     print("Logits:"		, outputs.logits[0, 0, :3, :3])
     assert torch.allclose(outputs.logits[0, 0, :3, :3]		, lowerCAmelCase__		, atol=1e-4)
     print("Looks ok!")

     if pytorch_dump_folder_path is not None:
          print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''')
          model.save_pretrained(lowerCAmelCase__)
          print(f'''Saving processor to {pytorch_dump_folder_path}''')
          processor.save_pretrained(lowerCAmelCase__)

     if push_to_hub:
          print(f'''Pushing model and processor for {model_name} to hub''')
          model.push_to_hub(f'''openmmlab/{model_name}''')
          processor.push_to_hub(f'''openmmlab/{model_name}''')


if __name__ == "__main__":
   lowercase__				:      Any	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="upernet-convnext-tiny",
       type=str,
       choices=[F'upernet-convnext-{size}' for size in ["tiny", "small", "base", "large", "xlarge"]],
       help="Name of the ConvNext UperNet model you\'d like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   lowercase__				:      str	   = parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowercase__				:      Optional[int]	   = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
   from run_translation import main  # noqa


set_seed(42)
lowercase__				:      List[str]	   = "sshleifer/student_marian_en_ro_6_1"
lowercase__				:      List[Any]	   = "sshleifer/tiny-mbart"




@require_torch
class       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     self  ,		A=False  ,		A=None  ,		A=True  ,		A=True  ,		A=True  ,		A=True  ,		)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.run_trainer(
              eval_steps=1  ,		max_len=12  ,		model_name=A  ,		num_train_epochs=1  ,		distributed=A  ,		extra_args_str=A  ,		predict_with_generate=A  ,		do_train=A  ,		do_eval=A  ,		do_predict=A  ,		)
          a     =				TrainerState.load_from_json(os.path.join(A  ,		"trainer_state.json"			)			).log_history

          if not do_eval:
               return

          a     =				[log for log in logs if "eval_loss" in log.keys()]

          a     =				eval_metrics[0]
          if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               a     =				eval_metrics[-1]
               assert isinstance(last_step_stats["eval_bleu"]  ,		A			)
               assert not math.isnan(float(last_step_stats["eval_loss"]			)			), "eval_loss must not be `nan`"







     @require_torch_non_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          self.run_seqaseq_quick()







     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A			)







     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp simple"			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp simple --fp16"			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp zero_dp_2"  ,		predict_with_generate=A			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          self.run_seqaseq_quick(
              distributed=A  ,		extra_args_str="--sharded_ddp zero_dp_2 --fp16"  ,		predict_with_generate=A			)







     @require_apex
     @require_torch_gpu
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--fp16 --fp16_backend=apex"			)
          # test 2nd time - was getting eval_loss': nan'
          # to reproduce the problem set distributed=False
          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--fp16 --fp16_backend=apex"			)







     @parameterized.expand(["base", "low", "high", "mixed"]			)
     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				{
              # test with the default log_level - should be info and thus log info once
              "base": {"extra_args_str": "", "n_matches": 1},
              # test with low log_level and log_level_replica - should be noisy on all processes
              # now the info string should appear twice on 2 processes
              "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
              # test with high log_level and low log_level_replica
              # now the info string should appear once only on the replica
              "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
              # test with high log_level and log_level_replica - should be quiet on all processes
              "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
          }

          a     =				experiments[experiment_id]
          a     =				{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
          a     =				"Running training"
          with CaptureStderr() as cl:
               self.run_seqaseq_quick(**A  ,		extra_args_str=data["extra_args_str"]			)
          a     =				len(re.findall(A  ,		cl.err			)			)
          self.assertEqual(A  ,		data["n_matches"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.run_trainer(
              eval_steps=2  ,		max_len=128  ,		model_name=A  ,		learning_rate=3e-4  ,		num_train_epochs=10  ,		distributed=A  ,		)

          # Check metrics
          a     =				TrainerState.load_from_json(os.path.join(A  ,		"trainer_state.json"			)			).log_history
          a     =				[log for log in logs if "eval_loss" in log.keys()]
          a     =				eval_metrics[0]
          a     =				eval_metrics[-1]

          assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
          assert isinstance(last_step_stats["eval_bleu"]  ,		A			)

          # test if do_predict saves generations and metrics
          a     =				os.listdir(A			)
          a     =				{os.path.basename(A			) for p in contents}
          assert "generated_predictions.txt" in contents
          assert "predict_results.json" in contents







     @slow
     @require_bitsandbytes
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          from transformers.training_args import OptimizerNames

          def train_and_return_metrics(A			) -> Tuple[int, float]:
               a     =				"--skip_memory_metrics 0"

               a     =				self.run_trainer(
                   max_len=128  ,		model_name=A  ,		learning_rate=3e-4  ,		num_train_epochs=1  ,		optim=A  ,		distributed=A  ,		extra_args_str=A  ,		do_eval=A  ,		do_predict=A  ,		n_gpus_to_use=1  ,		)

               # Check metrics
               a     =				TrainerState.load_from_json(Path(A  ,		"trainer_state.json"			)			).log_history
               a     =				int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20			)
               a     =				int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20			)

               a     =				logs[0]["train_loss"]
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

          a  ,				a  ,				a     =				train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
          a  ,				a  ,				a     =				train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

          a     =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

          a     =				gpu_peak_mem_orig + gpu_alloc_mem_orig
          a     =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
          a     =				gpu_total_mem_orig - gpu_total_mem_bnb

          # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
          # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
          # in 2 bytes and the diff in optim memory usage is derived as so:
          #
          # - normal 25*8=~200MB (8 bytes per param)
          # - bnb    25*2= ~50MB (2 bytes per param)
          #
          # Thus we should expect ~150MB total memory saved.
          #
          # Peak memory should be the same - the total should be different by about that same margin
          #
          # After leaving a small margin to accommodate for differences between gpus let's check
          # that we have at least 120MB in savings
          a     =				120

          # uncomment the following if this test starts failing - requires py38 for a new print feature
          # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
          # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
          # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
          # print(f"{gpu_alloc_mem_diff=}MB")
          # print(f"{gpu_peak_mem_diff=}MB")
          # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
          # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

          self.assertGreater(
              A  ,		A  ,		"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
              F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
              F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''  ,		)

          self.assertGreater(
              A  ,		A  ,		"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
              F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
              F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''  ,		)

          self.assertEqual(
              A  ,		A  ,		F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A = 3e-3  ,		A = "adafactor"  ,		A = False  ,		A = None  ,		A = 0  ,		A = True  ,		A = True  ,		A = True  ,		A = True  ,		A = None  ,		)  ->   Tuple:



          '''simple docstring'''



          a     =				self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
          a     =				self.get_auto_remove_tmp_dir()
          a     =				F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(A			)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(A			)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

          a     =				F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(A			)}
        '''.split()

          a     =				"\n            --do_predict\n        ".split()

          a     =				[]
          if do_train:
               args += args_train

          if do_eval:
               args += args_eval

          if do_predict:
               args += args_predict

          if predict_with_generate:
               args += "--predict_with_generate".split()

          if do_train:
               if optim == "adafactor":
                    args += "--adafactor".split()
               else:
                    args += F'''--optim {optim}'''.split()

          if extra_args_str is not None:
               args += extra_args_str.split()

          if distributed:
               if n_gpus_to_use is None:
                    a     =				get_gpu_count()
               a     =				get_torch_dist_unique_port()
               a     =				F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
               a     =				[sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(A  ,		env=self.get_env()			)
          else:
               a     =				["run_translation.py"] + args
               with patch.object(A  ,		"argv"  ,		A			):
                    main()

          return output_dir

