

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import OwlViTImageProcessor, OwlViTProcessor





@require_vision
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Tuple						=			tempfile.mkdtemp()

     # fmt: off
     UpperCAmelCase	:							str						=			['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
     # fmt: on
     UpperCAmelCase	:							int						=			dict(zip(__A,		range(len(__A						)						)						)						)
     UpperCAmelCase	:							Optional[int]						=			['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
     UpperCAmelCase	:							List[str]						=			{'''unk_token''': '''<unk>'''}

     UpperCAmelCase	:							Optional[int]						=			os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES['''vocab_file''']						)
     UpperCAmelCase	:							Union[str, Any]						=			os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES['''merges_file''']						)
     with open(self.vocab_file,		'''w''',		encoding='''utf-8'''						) as fp:
       fp.write(json.dumps(__A						) + '''\n'''						)
     with open(self.merges_file,		'''w''',		encoding='''utf-8'''						) as fp:
       fp.write('''\n'''.join(__A						)						)

     UpperCAmelCase	:							Optional[Any]						=			{
         '''do_resize''': True,
         '''size''': 2_0,
         '''do_center_crop''': True,
         '''crop_size''': 1_8,
         '''do_normalize''': True,
         '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
     }
     UpperCAmelCase	:							List[str]						=			os.path.join(self.tmpdirname,		__A						)
     with open(self.image_processor_file,		'''w''',		encoding='''utf-8'''						) as fp:
       json.dump(__A,		__A						)



   def 	__magic_name__   (							self							:					List[Any],		**__A							:					Dict						):
     return CLIPTokenizer.from_pretrained(self.tmpdirname,		pad_token='''!''',		**__A						)



   def 	__magic_name__   (							self							:					int,		**__A							:					List[str]						):
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname,		pad_token='''!''',		**__A						)



   def 	__magic_name__   (							self							:					Dict,		**__A							:					List[Any]						):
     return OwlViTImageProcessor.from_pretrained(self.tmpdirname,		**__A						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     shutil.rmtree(self.tmpdirname						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							List[str]						=			[np.random.randint(2_5_5,		size=(3, 3_0, 4_0_0),		dtype=np.uinta						)]

     UpperCAmelCase	:							Dict						=			[Image.fromarray(np.moveaxis(__A,		0,		-1						)						) for x in image_inputs]

     return image_inputs



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Any						=			self.get_tokenizer()
     UpperCAmelCase	:							Optional[int]						=			self.get_rust_tokenizer()
     UpperCAmelCase	:							str						=			self.get_image_processor()

     UpperCAmelCase	:							Optional[int]						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)
     processor_slow.save_pretrained(self.tmpdirname						)
     UpperCAmelCase	:							Optional[Any]						=			OwlViTProcessor.from_pretrained(self.tmpdirname,		use_fast=__A						)

     UpperCAmelCase	:							Dict						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)
     processor_fast.save_pretrained(self.tmpdirname						)
     UpperCAmelCase	:							Union[str, Any]						=			OwlViTProcessor.from_pretrained(self.tmpdirname						)

     self.assertEqual(processor_slow.tokenizer.get_vocab(),		tokenizer_slow.get_vocab()						)
     self.assertEqual(processor_fast.tokenizer.get_vocab(),		tokenizer_fast.get_vocab()						)
     self.assertEqual(tokenizer_slow.get_vocab(),		tokenizer_fast.get_vocab()						)
     self.assertIsInstance(processor_slow.tokenizer,		__A						)
     self.assertIsInstance(processor_fast.tokenizer,		__A						)

     self.assertEqual(processor_slow.image_processor.to_json_string(),		image_processor.to_json_string()						)
     self.assertEqual(processor_fast.image_processor.to_json_string(),		image_processor.to_json_string()						)
     self.assertIsInstance(processor_slow.image_processor,		__A						)
     self.assertIsInstance(processor_fast.image_processor,		__A						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Dict						=			OwlViTProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     UpperCAmelCase	:							List[str]						=			self.get_tokenizer(bos_token='''(BOS)''',		eos_token='''(EOS)'''						)
     UpperCAmelCase	:							Dict						=			self.get_image_processor(do_normalize=__A						)

     UpperCAmelCase	:							str						=			OwlViTProcessor.from_pretrained(
         self.tmpdirname,		bos_token='''(BOS)''',		eos_token='''(EOS)''',		do_normalize=__A						)

     self.assertEqual(processor.tokenizer.get_vocab(),		tokenizer_add_kwargs.get_vocab()						)
     self.assertIsInstance(processor.tokenizer,		__A						)

     self.assertEqual(processor.image_processor.to_json_string(),		image_processor_add_kwargs.to_json_string()						)
     self.assertIsInstance(processor.image_processor,		__A						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							int						=			self.get_image_processor()
     UpperCAmelCase	:							Optional[Any]						=			self.get_tokenizer()

     UpperCAmelCase	:							Union[str, Any]						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Any						=			self.prepare_image_inputs()

     UpperCAmelCase	:							Optional[int]						=			image_processor(__A,		return_tensors='''np'''						)
     UpperCAmelCase	:							List[Any]						=			processor(images=__A,		return_tensors='''np'''						)

     for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum(),		input_processor[key].sum(),		delta=1E-2						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Tuple						=			self.get_image_processor()
     UpperCAmelCase	:							Any						=			self.get_tokenizer()

     UpperCAmelCase	:							Dict						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							str						=			'''lower newer'''

     UpperCAmelCase	:							Optional[int]						=			processor(text=__A,		return_tensors='''np'''						)

     UpperCAmelCase	:							List[Any]						=			tokenizer(__A,		return_tensors='''np'''						)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key][0].tolist(),		encoded_processor[key][0].tolist()						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							Dict						=			self.get_image_processor()
     UpperCAmelCase	:							int						=			self.get_tokenizer()

     UpperCAmelCase	:							Union[str, Any]						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Tuple						=			'''lower newer'''
     UpperCAmelCase	:							Optional[int]						=			self.prepare_image_inputs()

     UpperCAmelCase	:							Optional[Any]						=			processor(text=__A,		images=__A						)

     self.assertListEqual(list(inputs.keys()						),		['''input_ids''', '''attention_mask''', '''pixel_values''']						)

     # test if it raises when no input is passed
     with pytest.raises(__A						):
       processor()



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Dict						=			'''google/owlvit-base-patch32'''
     UpperCAmelCase	:							int						=			OwlViTProcessor.from_pretrained(__A						)

     UpperCAmelCase	:							int						=			['''cat''', '''nasa badge''']
     UpperCAmelCase	:							Any						=			processor(text=__A						)

     UpperCAmelCase	:							Optional[Any]						=			1_6
     self.assertListEqual(list(inputs.keys()						),		['''input_ids''', '''attention_mask''']						)
     self.assertEqual(inputs['''input_ids'''].shape,		(2, seq_length)						)

     # test if it raises when no input is passed
     with pytest.raises(__A						):
       processor()



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]						=			'''google/owlvit-base-patch32'''
     UpperCAmelCase	:							Union[str, Any]						=			OwlViTProcessor.from_pretrained(__A						)

     UpperCAmelCase	:							Tuple						=			[['''cat''', '''nasa badge'''], ['''person''']]
     UpperCAmelCase	:							Optional[int]						=			processor(text=__A						)

     UpperCAmelCase	:							str						=			1_6
     UpperCAmelCase	:							Any						=			len(__A						)
     UpperCAmelCase	:							str						=			max([len(__A						) for texts in input_texts]						)

     self.assertListEqual(list(inputs.keys()						),		['''input_ids''', '''attention_mask''']						)
     self.assertEqual(inputs['''input_ids'''].shape,		(batch_size * num_max_text_queries, seq_length)						)

     # test if it raises when no input is passed
     with pytest.raises(__A						):
       processor()



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[Any]						=			'''google/owlvit-base-patch32'''
     UpperCAmelCase	:							Union[str, Any]						=			OwlViTProcessor.from_pretrained(__A						)

     UpperCAmelCase	:							Optional[int]						=			['''cat''', '''nasa badge''']
     UpperCAmelCase	:							Union[str, Any]						=			processor(text=__A						)

     UpperCAmelCase	:							List[Any]						=			1_6
     UpperCAmelCase	:							Tuple						=			inputs['''input_ids''']
     UpperCAmelCase	:							List[Any]						=			[
         [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
     ]

     self.assertListEqual(list(inputs.keys()						),		['''input_ids''', '''attention_mask''']						)
     self.assertEqual(inputs['''input_ids'''].shape,		(2, seq_length)						)
     self.assertListEqual(list(input_ids[0]						),		predicted_ids[0]						)
     self.assertListEqual(list(input_ids[1]						),		predicted_ids[1]						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Optional[Any]						=			self.get_image_processor()
     UpperCAmelCase	:							List[Any]						=			self.get_tokenizer()

     UpperCAmelCase	:							List[str]						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							str						=			self.prepare_image_inputs()
     UpperCAmelCase	:							str						=			self.prepare_image_inputs()

     UpperCAmelCase	:							int						=			processor(images=__A,		query_images=__A						)

     self.assertListEqual(list(inputs.keys()						),		['''query_pixel_values''', '''pixel_values''']						)

     # test if it raises when no input is passed
     with pytest.raises(__A						):
       processor()





   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							int						=			self.get_image_processor()
     UpperCAmelCase	:							Any						=			self.get_tokenizer()

     UpperCAmelCase	:							Any						=			OwlViTProcessor(tokenizer=__A,		image_processor=__A						)

     UpperCAmelCase	:							Optional[Any]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase	:							Tuple						=			processor.batch_decode(__A						)
     UpperCAmelCase	:							Optional[Any]						=			tokenizer.batch_decode(__A						)

     self.assertListEqual(__A,		__A						)








import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(4_2)

_lowerCamelCase    :       List[str]        =							"bert-base-cased"
_lowerCamelCase    :       str        =							"fp16"
_lowerCamelCase    :       Optional[int]        =							"bf16"
_lowerCamelCase    :       List[str]        =							[FPaa, BFaa]





@require_fsdp
@require_cuda
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					Tuple						):
     super().setUp()

     UpperCAmelCase	:							Optional[Any]						=			dict(
         ACCELERATE_USE_FSDP='''true''',		MASTER_ADDR='''localhost''',		MASTER_PORT='''10999''',		RANK='''0''',		LOCAL_RANK='''0''',		WORLD_SIZE='''1''',		)



   def 	__magic_name__   (							self							:					int						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

     for i, strategy in enumerate(__A						):
       UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
       UpperCAmelCase	:							Union[str, Any]						=			F'''{i + 1}'''
       UpperCAmelCase	:							Union[str, Any]						=			strategy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Union[str, Any]						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.sharding_strategy,		ShardingStrategy(i + 1						)						)



   def 	__magic_name__   (							self							:					Dict						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

     for i, prefetch_policy in enumerate(__A						):
       UpperCAmelCase	:							int						=			self.dist_env.copy()
       UpperCAmelCase	:							Dict						=			prefetch_policy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
         if prefetch_policy == "NO_PREFETCH":
           self.assertIsNone(fsdp_plugin.backward_prefetch						)
         else:
           self.assertEqual(fsdp_plugin.backward_prefetch,		BackwardPrefetch(i + 1						)						)



   def 	__magic_name__   (							self							:					Any						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

     for i, state_dict_type in enumerate(__A						):
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			state_dict_type
       with mockenv_context(**__A						):
         UpperCAmelCase	:							str						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.state_dict_type,		StateDictType(i + 1						)						)
         if state_dict_type == "FULL_STATE_DICT":
           self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu						)
           self.assertTrue(fsdp_plugin.state_dict_config.ranka_only						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			AutoModel.from_pretrained(__A						)
     for policy in FSDP_AUTO_WRAP_POLICY:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							List[Any]						=			policy
       if policy == "TRANSFORMER_BASED_WRAP":
         UpperCAmelCase	:							Tuple						=			'''BertLayer'''
       elif policy == "SIZE_BASED_WRAP":
         UpperCAmelCase	:							List[str]						=			'''2000'''
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[Any]						=			FullyShardedDataParallelPlugin()
         fsdp_plugin.set_auto_wrap_policy(__A						)
         if policy == "NO_WRAP":
           self.assertIsNone(fsdp_plugin.auto_wrap_policy						)
         else:
           self.assertIsNotNone(fsdp_plugin.auto_wrap_policy						)

     UpperCAmelCase	:							List[str]						=			self.dist_env.copy()
     UpperCAmelCase	:							Tuple						=			'''TRANSFORMER_BASED_WRAP'''
     UpperCAmelCase	:							Optional[Any]						=			'''T5Layer'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							int						=			FullyShardedDataParallelPlugin()
       with self.assertRaises(__A						) as cm:
         fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception						)						)

     UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
     UpperCAmelCase	:							str						=			'''SIZE_BASED_WRAP'''
     UpperCAmelCase	:							str						=			'''0'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
       fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertIsNone(fsdp_plugin.auto_wrap_policy						)



   def 	__magic_name__   (							self							:					int						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
     from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

     for mp_dtype in dtypes:
       UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			mp_dtype
       with mockenv_context(**__A						):
         UpperCAmelCase	:							int						=			Accelerator()
         if mp_dtype == "fp16":
           UpperCAmelCase	:							Any						=			torch.floataa
         elif mp_dtype == "bf16":
           UpperCAmelCase	:							Any						=			torch.bfloataa
         UpperCAmelCase	:							Optional[Any]						=			MixedPrecision(param_dtype=__A,		reduce_dtype=__A,		buffer_dtype=__A						)
         self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy,		__A						)
         if mp_dtype == FPaa:
           self.assertTrue(isinstance(accelerator.scaler,		__A						)						)
         elif mp_dtype == BFaa:
           self.assertIsNone(accelerator.scaler						)
         AcceleratorState._reset_state(__A						)





   def 	__magic_name__   (							self							:					Optional[int]						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

     for flag in [True, False]:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			str(__A						).lower()
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Union[str, Any]						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.cpu_offload,		CPUOffload(offload_params=__A						)						)






@require_fsdp
@require_multi_gpu
@slow
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					List[Any]						):
     super().setUp()
     UpperCAmelCase	:							int						=			0.8_2
     UpperCAmelCase	:							List[str]						=			[
         '''fsdp_shard_grad_op_transformer_based_wrap''',
         '''fsdp_full_shard_transformer_based_wrap''',
     ]
     UpperCAmelCase	:							int						=			{
         '''multi_gpu_fp16''': 3_2_0_0,
         '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
         '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
         # Disabling below test as it overwhelms the RAM memory usage
         # on CI self-hosted runner leading to tests getting killed.
         # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
     }
     UpperCAmelCase	:							List[str]						=			1_6_0
     UpperCAmelCase	:							Optional[int]						=			1_6_0

     UpperCAmelCase	:							Union[str, Any]						=			inspect.getfile(accelerate.test_utils						)
     UpperCAmelCase	:							Dict						=			os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''external_deps''']						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							str						=			os.path.join(self.test_scripts_folder,		'''test_performance.py'''						)
     UpperCAmelCase	:							Any						=			['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
     for config in self.performance_configs:
       UpperCAmelCase	:							Union[str, Any]						=			cmd.copy()
       for i, strategy in enumerate(__A						):
         if strategy.lower() in config:
           cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''						)
           break

       if "fp32" in config:
         cmd_config.append('''--mixed_precision=no'''						)
       else:
         cmd_config.append('''--mixed_precision=fp16'''						)

       if "cpu_offload" in config:
         cmd_config.append('''--fsdp_offload_params=True'''						)

       for policy in FSDP_AUTO_WRAP_POLICY:
         if policy.lower() in config:
           cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''						)
           break

       if policy == "TRANSFORMER_BASED_WRAP":
         cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''						)
       elif policy == "SIZE_BASED_WRAP":
         cmd_config.append('''--fsdp_min_num_params=2000'''						)

       cmd_config.extend(
           [
               self.test_file_path,
               F'''--output_dir={self.tmpdir}''',
               F'''--performance_lower_bound={self.performance_lower_bound}''',
           ]						)
       with patch_environment(omp_num_threads=1						):
         execute_subprocess_async(__A,		env=os.environ.copy()						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Dict						=			os.path.join(self.test_scripts_folder,		'''test_checkpointing.py'''						)
     UpperCAmelCase	:							Optional[int]						=			[
         '''accelerate''',
         '''launch''',
         '''--num_processes=2''',
         '''--num_machines=1''',
         '''--machine_rank=0''',
         '''--use_fsdp''',
         '''--mixed_precision=fp16''',
         '''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
     ]

     for i, strategy in enumerate(__A						):
       UpperCAmelCase	:							Optional[Any]						=			cmd.copy()
       cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''						)
       if strategy != "FULL_SHARD":
         continue
       UpperCAmelCase	:							Any						=			len(__A						)
       for state_dict_type in FSDP_STATE_DICT_TYPE:
         UpperCAmelCase	:							List[Any]						=			cmd_config[:state_dict_config_index]
         cmd_config.append(F'''--fsdp_state_dict_type={state_dict_type}'''						)
         cmd_config.extend(
             [
                 self.test_file_path,
                 F'''--output_dir={self.tmpdir}''',
                 '''--partial_train_epoch=1''',
             ]						)
         with patch_environment(omp_num_threads=1						):
           execute_subprocess_async(__A,		env=os.environ.copy()						)

         UpperCAmelCase	:							Tuple						=			cmd_config[:-1]
         UpperCAmelCase	:							List[str]						=			os.path.join(self.tmpdir,		'''epoch_0'''						)
         cmd_config.extend(
             [
                 F'''--resume_from_checkpoint={resume_from_checkpoint}''',
             ]						)
         with patch_environment(omp_num_threads=1						):
           execute_subprocess_async(__A,		env=os.environ.copy()						)





   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Optional[Any]						=			os.path.join(self.test_scripts_folder,		'''test_peak_memory_usage.py'''						)
     UpperCAmelCase	:							str						=			[
         '''accelerate''',
         '''launch''',
         '''--num_processes=2''',
         '''--num_machines=1''',
         '''--machine_rank=0''',
     ]
     for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
       UpperCAmelCase	:							List[Any]						=			cmd.copy()
       if "fp16" in spec:
         cmd_config.extend(['''--mixed_precision=fp16''']						)
       else:
         cmd_config.extend(['''--mixed_precision=no''']						)

       if "multi_gpu" in spec:
         continue
       else:
         cmd_config.extend(['''--use_fsdp''']						)
         for i, strategy in enumerate(__A						):
           if strategy.lower() in spec:
             cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''						)
             break

         if "cpu_offload" in spec:
           cmd_config.append('''--fsdp_offload_params=True'''						)

         for policy in FSDP_AUTO_WRAP_POLICY:
           if policy.lower() in spec:
             cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''						)
             break

         if policy == "TRANSFORMER_BASED_WRAP":
           cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''						)
         elif policy == "SIZE_BASED_WRAP":
           cmd_config.append('''--fsdp_min_num_params=2000'''						)

       cmd_config.extend(
           [
               self.test_file_path,
               F'''--output_dir={self.tmpdir}''',
               F'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
               F'''--n_train={self.n_train}''',
               F'''--n_val={self.n_val}''',
           ]						)
       with patch_environment(omp_num_threads=1						):
         execute_subprocess_async(__A,		env=os.environ.copy()						)





