




"""simple docstring"""




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


a_        =       logging.get_logger(__name__)

a_        =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_        =       {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_        =       {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_        =       {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

a_        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
a_        =       {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
a_        =       {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


a_        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a_        =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a_        =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


a_        =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

a_        =       collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a_        =       r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(snake_case		)
class   UpperCAmelCase_    :
		def __call__(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = False				,						UpperCamelCase_ = False				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       BatchEncoding:
					if titles is None and texts is None:
								return super().__call__(
								    UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_				,						return_attention_mask=UpperCamelCase_				,						**UpperCamelCase_				,						)
					elif titles is None or texts is None:
								__lowercase			:  int								=       titles if texts is None else texts
								return super().__call__(
								    UpperCamelCase_				,						UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_				,						return_attention_mask=UpperCamelCase_				,						**UpperCamelCase_				,						)
					__lowercase			:  Optional[int]								=       titles if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [titles]
					__lowercase			:  Optional[int]								=       texts if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [texts]
					__lowercase			:  str								=       len(UpperCamelCase_       )
					__lowercase			:  List[Any]								=       questions if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [questions] * n_passages
					if len(UpperCamelCase_       ) != len(UpperCamelCase_       ):
								raise ValueError(
								    F"""There should be as many titles than texts but got {len(UpperCamelCase_       )} titles and {len(UpperCamelCase_       )} texts."""       )
					__lowercase			:  int								=       super().__call__(UpperCamelCase_				,						UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_       )['''input_ids''']
					__lowercase			:  List[Any]								=       super().__call__(UpperCamelCase_				,						add_special_tokens=UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_       )['''input_ids''']
					__lowercase			:  Optional[Any]								=       {
					    '''input_ids''': [
					        (encoded_question_and_title + encoded_text)[:max_length]
					        if max_length is not None and truncation
					        else encoded_question_and_title + encoded_text
					        for encoded_question_and_title, encoded_text in zip(UpperCamelCase_				,						UpperCamelCase_       )
					    ]
					}
					if return_attention_mask is not False:
								__lowercase			:  str								=       []
								for input_ids in encoded_inputs["input_ids"]:
											attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
								__lowercase			:  List[str]								=       attention_mask
					return self.pad(UpperCamelCase_				,						padding=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = 16				,						UpperCamelCase_ = 64				,						UpperCamelCase_ = 4				,						)						->       List[DPRSpanPrediction]:
					__lowercase			:  List[Any]								=       reader_input['''input_ids''']
					__lowercase			,__lowercase			,__lowercase			:  List[str]								=       reader_output[:3]
					__lowercase			:  Optional[int]								=       len(UpperCamelCase_       )
					__lowercase			:  Any								=       sorted(range(UpperCamelCase_       )				,						reverse=UpperCamelCase_				,						key=relevance_logits.__getitem__       )
					__lowercase			:  List[DPRReaderOutput]								=       []
					for doc_id in sorted_docs:
								__lowercase			:  Any								=       list(input_ids[doc_id]       )
								# assuming question & title information is at the beginning of the sequence
								__lowercase			:  Tuple								=       sequence_ids.index(self.sep_token_id				,						2       ) + 1  # second sep id
								if sequence_ids[-1] == self.pad_token_id:
											__lowercase			:  Optional[Any]								=       sequence_ids.index(self.pad_token_id       )
								else:
											__lowercase			:  List[Any]								=       len(UpperCamelCase_       )

								__lowercase			:  List[str]								=       self._get_best_spans(
								    start_logits=start_logits[doc_id][passage_offset:sequence_len]				,						end_logits=end_logits[doc_id][passage_offset:sequence_len]				,						max_answer_length=UpperCamelCase_				,						top_spans=UpperCamelCase_				,						)
								for start_index, end_index in best_spans:
											start_index += passage_offset
											end_index += passage_offset
											nbest_spans_predictions.append(
											    DPRSpanPrediction(
											        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]				,						relevance_score=relevance_logits[doc_id]				,						doc_id=UpperCamelCase_				,						start_index=UpperCamelCase_				,						end_index=UpperCamelCase_				,						text=self.decode(sequence_ids[start_index : end_index + 1]       )				,						)       )
								if len(UpperCamelCase_       ) >= num_spans:
											break
					return nbest_spans_predictions[:num_spans]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       List[DPRSpanPrediction]:
					__lowercase			:  Tuple								=       []
					for start_index, start_score in enumerate(UpperCamelCase_       ):
								for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
											scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
					__lowercase			:  int								=       sorted(UpperCamelCase_				,						key=lambda UpperCamelCase_       : x[1]				,						reverse=UpperCamelCase_       )
					__lowercase			:  Optional[Any]								=       []
					for (start_index, end_index), score in scores:
								if start_index > end_index:
											raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""       )
								__lowercase			:  Any								=       end_index - start_index + 1
								if length > max_answer_length:
											raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""       )
								if any(
								    start_index <= prev_start_index <= prev_end_index <= end_index
								    or prev_start_index <= start_index <= end_index <= prev_end_index
								    for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
											continue
								chosen_span_intervals.append((start_index, end_index)       )

								if len(UpperCamelCase_       ) == top_spans:
											break
					return chosen_span_intervals


@add_end_docstrings(snake_case		)
class   UpperCAmelCase_    (			snake_case   ,					snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=READER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=READER_PRETRAINED_INIT_CONFIGURATION
		UpperCamelCase							=["input_ids", "attention_mask"]










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(42)

_lowerCamelCase 					=      'bert-base-cased'
_lowerCamelCase 					=      'fp16'
_lowerCamelCase 					=      'bf16'
_lowerCamelCase 					=      [FPaa, BFaa]




@require_fsdp
@require_cuda
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									super().setUp()

									SCREAMING_SNAKE_CASE__								=   dict(
									    ACCELERATE_USE_FSDP="""true"""				,					MASTER_ADDR="""localhost"""				,					MASTER_PORT="""10999"""				,					RANK="""0"""				,					LOCAL_RANK="""0"""				,					WORLD_SIZE="""1"""				,					)


							def   _snake_case	(				self     :List[Any]		) -> Tuple:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

									for i, strategy in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   f'''{i + 1}'''
											SCREAMING_SNAKE_CASE__								=   strategy
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													self.assertEqual(fsdp_plugin.sharding_strategy				,					ShardingStrategy(i + 1		)		)


							def   _snake_case	(				self     :int		) -> List[str]:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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


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



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

									for i, state_dict_type in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   state_dict_type
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AutoModel.from_pretrained(__A		)
									for policy in FSDP_AUTO_WRAP_POLICY:
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   policy
											if policy == "TRANSFORMER_BASED_WRAP":
													SCREAMING_SNAKE_CASE__								=   """BertLayer"""
											elif policy == "SIZE_BASED_WRAP":
													SCREAMING_SNAKE_CASE__								=   """2000"""
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   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		)

									SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
									SCREAMING_SNAKE_CASE__								=   """TRANSFORMER_BASED_WRAP"""
									SCREAMING_SNAKE_CASE__								=   """T5Layer"""
									with mockenv_context(**__A		):
											SCREAMING_SNAKE_CASE__								=   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		)		)

									SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
									SCREAMING_SNAKE_CASE__								=   """SIZE_BASED_WRAP"""
									SCREAMING_SNAKE_CASE__								=   """0"""
									with mockenv_context(**__A		):
											SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
											fsdp_plugin.set_auto_wrap_policy(__A		)
											self.assertIsNone(fsdp_plugin.auto_wrap_policy		)


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[int]:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
									from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

									for mp_dtype in dtypes:
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   mp_dtype
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   Accelerator()
													if mp_dtype == "fp16":
															SCREAMING_SNAKE_CASE__								=   torch.floataa
													elif mp_dtype == "bf16":
															SCREAMING_SNAKE_CASE__								=   torch.bfloataa
													SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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




@require_fsdp
@require_multi_gpu
@slow
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def   _snake_case	(				self     :Any		) -> Any:



									"""simple docstring"""



									super().setUp()
									SCREAMING_SNAKE_CASE__								=   0.8_2
									SCREAMING_SNAKE_CASE__								=   [
									    """fsdp_shard_grad_op_transformer_based_wrap""",
									    """fsdp_full_shard_transformer_based_wrap""",
									]
									SCREAMING_SNAKE_CASE__								=   {
									    """multi_gpu_fp16""": 3200,
									    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2000,
									    """fsdp_full_shard_transformer_based_wrap_fp16""": 1900,
									    # 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
									}
									SCREAMING_SNAKE_CASE__								=   160
									SCREAMING_SNAKE_CASE__								=   160

									SCREAMING_SNAKE_CASE__								=   inspect.getfile(accelerate.test_utils		)
									SCREAMING_SNAKE_CASE__								=   os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """external_deps"""]		)


							def   _snake_case	(				self     :Union[str, Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_performance.py"""		)
									SCREAMING_SNAKE_CASE__								=   ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
									for config in self.performance_configs:
											SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_checkpointing.py"""		)
									SCREAMING_SNAKE_CASE__								=   [
									    """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		):
											SCREAMING_SNAKE_CASE__								=   cmd.copy()
											cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''		)
											if strategy != "FULL_SHARD":
													continue
											SCREAMING_SNAKE_CASE__								=   len(__A		)
											for state_dict_type in FSDP_STATE_DICT_TYPE:
													SCREAMING_SNAKE_CASE__								=   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()		)

													SCREAMING_SNAKE_CASE__								=   cmd_config[:-1]
													SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :Tuple		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_peak_memory_usage.py"""		)
									SCREAMING_SNAKE_CASE__								=   [
									    """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():
											SCREAMING_SNAKE_CASE__								=   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()		)