
'''simple docstring'''





import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__						:      int   				=				logging.get_logger(__name__)

a__						:      Optional[Any]   				=				{
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

a__						:      List[Any]   				=				{
    "b0": {
        "hidden_dim": 1_2_8_0,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 2_2_4,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1_2_8_0,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 2_4_0,
        "dropout_rate": 0.2,
        "dw_padding": [1_6],
    },
    "b2": {
        "hidden_dim": 1_4_0_8,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 2_6_0,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 1_6],
    },
    "b3": {
        "hidden_dim": 1_5_3_6,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 3_0_0,
        "dropout_rate": 0.3,
        "dw_padding": [5, 1_8],
    },
    "b4": {
        "hidden_dim": 1_7_9_2,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 3_8_0,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2_0_4_8,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 4_5_6,
        "dropout_rate": 0.4,
        "dw_padding": [1_3, 2_7],
    },
    "b6": {
        "hidden_dim": 2_3_0_4,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 5_2_8,
        "dropout_rate": 0.5,
        "dw_padding": [3_1],
    },
    "b7": {
        "hidden_dim": 2_5_6_0,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 6_0_0,
        "dropout_rate": 0.5,
        "dw_padding": [1_8],
    },
}





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







      """simple docstring"""
      __A						=	EfficientNetConfig()
      __A						=	CONFIG_MAP[model_name]['hidden_dim']
      __A						=	CONFIG_MAP[model_name]['width_coef']
      __A						=	CONFIG_MAP[model_name]['depth_coef']
      __A						=	CONFIG_MAP[model_name]['image_size']
      __A						=	CONFIG_MAP[model_name]['dropout_rate']
      __A						=	CONFIG_MAP[model_name]['dw_padding']

      __A						=	'huggingface/label-files'
      __A						=	'imagenet-1k-id2label.json'
      __A						=	1_0_0_0
      __A						=	json.load(open(hf_hub_download(snake_case__					,     snake_case__					,     repo_type='dataset'		)					,     'r'		)		)
      __A						=	{int(snake_case__		): v for k, v in idalabel.items()}

      __A						=	idalabel
      __A						=	{v: k for k, v in idalabel.items()}
      return config





def 				snake_case							(						)->	str:







      """simple docstring"""
      __A						=	'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A						=	Image.open(requests.get(snake_case__					,     stream=snake_case__		).raw		)
      return im





def 				snake_case							(						UpperCAmelCase		)->	int:







      """simple docstring"""
      __A						=	CONFIG_MAP[model_name]['image_size']
      __A						=	EfficientNetImageProcessor(
          size={'height': size, 'width': size}					,     image_mean=[0.485, 0.456, 0.406]					,     image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]					,     do_center_crop=snake_case__					,     )
      return preprocessor





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







      """simple docstring"""
      __A						=	[v.split('_'		)[0].split('block'		)[1] for v in original_param_names if v.startswith('block'		)]
      __A						=	sorted(set(snake_case__		)		)
      __A						=	len(snake_case__		)
      __A						=	{b: str(snake_case__		) for b, i in zip(snake_case__					,     range(snake_case__		)		)}

      __A						=	[]
      rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')		)
      rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')		)
      rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')		)
      rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')		)
      rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')		)

      for b in block_names:
            __A						=	block_name_mapping[b]
            rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight')		)
            rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight')		)
            rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias')		)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')		)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')		)
            rename_keys.append(
                (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')		)
            rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')		)
            rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')		)
            rename_keys.append(
                (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')		)
            rename_keys.append(
                (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')		)

            rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')		)
            rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')		)
            rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')		)
            rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')		)
            rename_keys.append(
                (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight')		)
            rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight')		)
            rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias')		)
            rename_keys.append(
                (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean')		)
            rename_keys.append(
                (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var')		)

      rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')		)
      rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')		)
      rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')		)
      rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')		)
      rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')		)

      __A						=	{}
      for item in rename_keys:
            if item[0] in original_param_names:
                  __A						=	'efficientnet.' + item[1]

      __A						=	'classifier.weight'
      __A						=	'classifier.bias'
      return key_mapping





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase		)->	Any:







      """simple docstring"""
      for key, value in tf_params.items():
            if "normalization" in key:
                  continue

            __A						=	key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  __A						=	torch.from_numpy(snake_case__		).permute(3					,     2					,     0					,     1		)
            elif "depthwise_kernel" in key:
                  __A						=	torch.from_numpy(snake_case__		).permute(2					,     3					,     0					,     1		)
            elif "kernel" in key:
                  __A						=	torch.from_numpy(np.transpose(snake_case__		)		)
            else:
                  __A						=	torch.from_numpy(snake_case__		)

            # Replace HF parameters with original TF model parameters
            assert hf_params[hf_key].shape == new_hf_value.shape
            hf_params[hf_key].copy_(snake_case__		)





@torch.no_grad()
def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase		)->	Any:







      """simple docstring"""
      __A						=	model_classes[model_name](
          include_top=snake_case__					,     weights='imagenet'					,     input_tensor=snake_case__					,     input_shape=snake_case__					,     pooling=snake_case__					,     classes=1_0_0_0					,     classifier_activation='softmax'					,     )

      __A						=	original_model.trainable_variables
      __A						=	original_model.non_trainable_variables
      __A						=	{param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            __A						=	param.numpy()
      __A						=	list(tf_params.keys()		)

      # Load HuggingFace model
      __A						=	get_efficientnet_config(snake_case__		)
      __A						=	EfficientNetForImageClassification(snake_case__		).eval()
      __A						=	hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'		)
      __A						=	rename_keys(snake_case__		)
      replace_params(snake_case__					,     snake_case__					,     snake_case__		)

      # Initialize preprocessor and preprocess input image
      __A						=	convert_image_processor(snake_case__		)
      __A						=	preprocessor(images=prepare_img()					,     return_tensors='pt'		)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            __A						=	hf_model(**snake_case__		)
      __A						=	outputs.logits.detach().numpy()

      # Original model inference
      __A						=	False
      __A						=	CONFIG_MAP[model_name]['image_size']
      __A						=	prepare_img().resize((image_size, image_size)					,     resample=PIL.Image.NEAREST		)
      __A						=	image.img_to_array(snake_case__		)
      __A						=	np.expand_dims(snake_case__					,     axis=0		)
      __A						=	original_model.predict(snake_case__		)

      # Check whether original and HF model outputs match  -> np.allclose
      assert np.allclose(snake_case__					,     snake_case__					,     atol=1e-3		), "The predicted logits are not the same."
      print('Model outputs match!'		)

      if save_model:
            # Create folder to save model
            if not os.path.isdir(snake_case__		):
                  os.mkdir(snake_case__		)
            # Save converted model and image processor
            hf_model.save_pretrained(snake_case__		)
            preprocessor.save_pretrained(snake_case__		)

      if push_to_hub:
            # Push model and image processor to hub
            print(f'Pushing converted {model_name} to the hub...'		)
            __A						=	f'efficientnet-{model_name}'
            preprocessor.push_to_hub(snake_case__		)
            hf_model.push_to_hub(snake_case__		)


if __name__ == "__main__":
 a__						:      int   				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="b0",
     type=str,
     help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="hf_model",
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--save_model", action="store_true", help="Save model to local")
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

 a__						:      Dict   				=				parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, 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


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


set_seed(42)
_SCREAMING_SNAKE_CASE										=						'sshleifer/student_marian_en_ro_6_1'
_SCREAMING_SNAKE_CASE										=						'sshleifer/tiny-mbart'



@require_torch
class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				)  ->					int:
        _A        =  self.run_trainer(
            eval_steps=1		,				max_len=12		,				model_name=lowerCAmelCase_		,				num_train_epochs=1		,				distributed=lowerCAmelCase_		,				extra_args_str=lowerCAmelCase_		,				predict_with_generate=lowerCAmelCase_		,				do_train=lowerCAmelCase_		,				do_eval=lowerCAmelCase_		,				do_predict=lowerCAmelCase_		,				)
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""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"""]		,				lowerCAmelCase_							)
               assert not math.isnan(float(last_step_stats["""eval_loss"""]							)							), "eval_loss must not be `nan`"



 @require_torch_non_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        self.run_seqaseq_quick()



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					str:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp simple"""							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				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      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp zero_dp_2"""		,				predict_with_generate=lowerCAmelCase_							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        self.run_seqaseq_quick(
            distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""		,				predict_with_generate=lowerCAmelCase_							)



 @require_apex
 @require_torch_gpu
 def      UpperCAmelCase							(					self							)  ->					int:
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				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=lowerCAmelCase_		,				extra_args_str="""--fp16 --fp16_backend=apex"""							)



 @parameterized.expand(["""base""", """low""", """high""", """mixed"""]							)
 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					int:
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        _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(**lowerCAmelCase_		,				extra_args_str=data["""extra_args_str"""]							)
        _A        =  len(re.findall(lowerCAmelCase_		,				cl.err							)							)
        self.assertEqual(lowerCAmelCase_		,				data["""n_matches"""]							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.run_trainer(
            eval_steps=2		,				max_len=1_28		,				model_name=lowerCAmelCase_		,				learning_rate=3E-4		,				num_train_epochs=10		,				distributed=lowerCAmelCase_		,				)

        # Check metrics
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""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"""]		,				lowerCAmelCase_							)

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



 @slow
 @require_bitsandbytes
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        from transformers.training_args import OptimizerNames

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

               _A        =  self.run_trainer(
                   max_len=1_28		,				model_name=lowerCAmelCase_		,				learning_rate=3E-4		,				num_train_epochs=1		,				optim=lowerCAmelCase_		,				distributed=lowerCAmelCase_		,				extra_args_str=lowerCAmelCase_		,				do_eval=lowerCAmelCase_		,				do_predict=lowerCAmelCase_		,				n_gpus_to_use=1		,				)

               # Check metrics
               _A        =  TrainerState.load_from_json(Path(lowerCAmelCase_		,				"""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        =  1_20

        # 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(
            lowerCAmelCase_		,				lowerCAmelCase_		,				"""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(
            lowerCAmelCase_		,				lowerCAmelCase_		,				"""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(
            lowerCAmelCase_		,				lowerCAmelCase_		,				F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_ = 3E-3		,				lowerCAmelCase_ = "adafactor"		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = 0		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = None		,				)  ->					str:
        _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(lowerCAmelCase_							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCAmelCase_							)}
            --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(lowerCAmelCase_							)}
        '''.split()

        _A        =  """
            --do_predict
        """.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(lowerCAmelCase_		,				env=self.get_env()							)
        else:
               _A        =  ["""run_translation.py"""] + args
               with patch.object(lowerCAmelCase_		,				"""argv"""		,				lowerCAmelCase_							):
                      main()

        return output_dir




