
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A  :			Optional[Any]     				=				logging.get_logger(__name__)

A  :			Any     				=				{}







class     __A(				UpperCamelCase__    ):
				snake_case_		     =					"""fsmt_decoder"""




				def __init__(  self	,  _snake_case=0	,  _snake_case=0       )					->   List[Any]:


								'''simple docstring'''




								super().__init__()
								__a    							=					vocab_size
								__a    							=					bos_token_id







class     __A(				UpperCamelCase__    ):
				snake_case_		     =					"""fsmt"""
				snake_case_		     =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	,  _snake_case=["en", "de"]	,  _snake_case=42_024	,  _snake_case=42_024	,  _snake_case="relu"	,  _snake_case=1_024	,  _snake_case=200	,  _snake_case=1_024	,  _snake_case=4_096	,  _snake_case=12	,  _snake_case=16	,  _snake_case=0.0	,  _snake_case=4_096	,  _snake_case=12	,  _snake_case=16	,  _snake_case=0.0	,  _snake_case=0.0	,  _snake_case=0.1	,  _snake_case=0.0	,  _snake_case=0.02	,  _snake_case=2	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=5	,  _snake_case=1.0	,  _snake_case=False	,  _snake_case=True	,  _snake_case=1	,  _snake_case=0	,  _snake_case=2	,  _snake_case=2	,  **_snake_case	,  )					->   int:


								'''simple docstring'''




								__a    							=					langs
								__a    							=					src_vocab_size
								__a    							=					tgt_vocab_size
								__a    							=					d_model  # encoder_embed_dim and decoder_embed_dim

								__a    							=					encoder_ffn_dim
								__a    							=					encoder_layers
								__a    							=					encoder_attention_heads
								__a    							=					encoder_layerdrop
								__a    							=					decoder_layerdrop
								__a    							=					decoder_ffn_dim
								__a    							=					decoder_layers
								__a    							=					decoder_attention_heads
								__a    							=					max_position_embeddings
								__a    							=					init_std  # Normal(0, this parameter)
								__a    							=					activation_function

								__a    							=					DecoderConfig(vocab_size=lowerCAmelCase__	,  bos_token_id=lowerCAmelCase__       )
								if "decoder" in common_kwargs:
												del common_kwargs["decoder"]

								__a    							=					scale_embedding  # scale factor will be sqrt(d_model) if True

								# 3 Types of Dropout
								__a    							=					attention_dropout
								__a    							=					activation_dropout
								__a    							=					dropout

								__a    							=					use_cache
								super().__init__(
								    pad_token_id=lowerCAmelCase__	,  bos_token_id=lowerCAmelCase__	,  eos_token_id=lowerCAmelCase__	,  decoder_start_token_id=lowerCAmelCase__	,  is_encoder_decoder=lowerCAmelCase__	,  tie_word_embeddings=lowerCAmelCase__	,  forced_eos_token_id=lowerCAmelCase__	,  max_length=lowerCAmelCase__	,  num_beams=lowerCAmelCase__	,  length_penalty=lowerCAmelCase__	,  early_stopping=lowerCAmelCase__	,  **lowerCAmelCase__	,  )




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


								'''simple docstring'''




								__a    							=					copy.deepcopy(self.__dict__       )
								__a    							=					self.decoder.to_dict()
								__a    							=					self.__class__.model_type
								return output

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
					import torch_xla.core.xla_model as xm  # noqa: F401

					A  :			List[str]     				=				True
except ImportError:
					A  :			List[Any]     				=				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
A  :			str     				=				torch.distributed.is_available()


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
				__a    							=					importlib.util.find_spec(a__						) is not None
				if package_exists:
								try:
												__a    							=					importlib.metadata.metadata(a__						)
												return True
								except importlib.metadata.PackageNotFoundError:
												return False


def 					__lowerCAmelCase    (    ) ->						bool:
				return _torch_distributed_available


def 					__lowerCAmelCase    (    ) ->						Tuple:
				try:
								pass
				except ImportError:
								print(
								    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
								    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
								    ''' Bindings for PyTorch*.'''						)
				return (
				    importlib.util.find_spec('''torch_ccl'''						) is not None
				    or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''						) is not None
				)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return importlib.metadata.version('''oneccl_bind_pt'''						)


def 					__lowerCAmelCase    (    ) ->						Dict:
				return _is_package_available('''transformer_engine'''						)


@lru_cache
def 					__lowerCAmelCase    (    a__=True						) ->						Union[str, Any]:
				# Due to bugs on the amp series GPUs, we disable torch-xla on them
				if torch.cuda.is_available():
								return False
				if _tpu_available and check_device:
								try:
												# Will raise a RuntimeError if no XLA configuration is found
												__a    							=					xm.xla_device()
												return True
								except RuntimeError:
												return False
				return _tpu_available


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''deepspeed'''						)


def 					__lowerCAmelCase    (    a__=False						) ->						Optional[Any]:
				if is_tpu_available():
								return not ignore_tpu
				if torch.cuda.is_available():
								return torch.cuda.is_bfaa_supported()
				if is_npu_available():
								return False
				return True


def 					__lowerCAmelCase    (    ) ->						List[str]:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.39.0'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.37.2'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''bitsandbytes'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''		,					'''False'''						)						) == 1:
								__a    							=					importlib.util.find_spec('''megatron'''						) is not None
								if package_exists:
												try:
																__a    							=					parse(importlib.metadata.version('''megatron-lm'''						)						)
																return compare_versions(a__		,					'''>='''		,					'''2.2.0'''						)
												except Exception as e:
																warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""						)
																return False


def 					__lowerCAmelCase    (    ) ->						List[str]:
				return _is_package_available('''safetensors'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return _is_package_available('''transformers'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''datasets'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''aim'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''wandb'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''comet_ml'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				return _is_package_available('''boto3'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				if _is_package_available('''rich'''						):
								if "ACCELERATE_DISABLE_RICH" in os.environ:
												warnings.warn(
												    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''						)
												return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''		,					a__						)
								return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''		,					a__						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''sagemaker'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''tqdm'''						)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return _is_package_available('''mlflow'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return is_torch_version('''>='''		,					'''1.12'''						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 					__lowerCAmelCase    (    ) ->						str:
				def get_major_and_minor_from_version(a__						):
								return str(version.parse(a__						).major						) + "." + str(version.parse(a__						).minor						)

				__a    							=					importlib.metadata.version('''torch'''						)
				if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
								return False
				__a    							=					'''N/A'''
				try:
								__a    							=					importlib.metadata.version('''intel_extension_for_pytorch'''						)
				except importlib.metadata.PackageNotFoundError:
								return False
				__a    							=					get_major_and_minor_from_version(_torch_version						)
				__a    							=					get_major_and_minor_from_version(_ipex_version						)
				if torch_major_and_minor != ipex_major_and_minor:
								warnings.warn(
								    F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
								    F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
								return False
				return True


@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						List[Any]:
				if importlib.util.find_spec('''torch'''						) is None or importlib.util.find_spec('''torch_npu'''						) is None:
								return False

				import torch
				import torch_npu  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no NPU is found
												__a    							=					torch.npu.device_count()
												return torch.npu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''npu'''						) and torch.npu.is_available()






@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						Union[str, Any]:
				if not parse_flag_from_env('''ACCELERATE_USE_XPU'''		,					default=a__						):
								return False
				if is_ipex_available():
								import torch

								if is_torch_version('''<='''		,					'''1.12'''						):
												return False
				else:
								return False

				import intel_extension_for_pytorch  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no XPU  is found
												__a    							=					torch.xpu.device_count()
												return torch.xpu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''xpu'''						) and torch.xpu.is_available()