
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
		from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    List[str]:
							if is_torch_version("""<"""			,				"""2.0.0"""       ) or not hasattr(UpperCAmelCase__			,				"""_dynamo"""       ):
														return False
							return isinstance(UpperCAmelCase__			,				torch._dynamo.eval_frame.OptimizedModule       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool = True       )  ->    str:
							lowerCamelCase_							  =					(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							lowerCamelCase_							  =					is_compiled_module(UpperCAmelCase__       )
							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					model.module

							if not keep_fpaa_wrapper:
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""forward"""       )
														lowerCamelCase_							  =					model.__dict__.pop("""_original_forward"""			,				UpperCAmelCase__       )
														if original_forward is not None:
																					while hasattr(UpperCAmelCase__			,				"""__wrapped__"""       ):
																												lowerCamelCase_							  =					forward.__wrapped__
																												if forward == original_forward:
																																			break
																					lowerCamelCase_							  =					forward
														if getattr(UpperCAmelCase__			,				"""_converted_to_transformer_engine"""			,				UpperCAmelCase__       ):
																					convert_model(UpperCAmelCase__			,				to_transformer_engine=UpperCAmelCase__       )

							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					compiled_model

							return model



def      __lowerCAmelCase						(							)  ->    Any:
							PartialState().wait_for_everyone()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Tuple       )  ->    Dict:
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(UpperCAmelCase__			,				UpperCAmelCase__       )
							elif PartialState().local_process_index == 0:
														torch.save(UpperCAmelCase__			,				UpperCAmelCase__       )



@contextmanager
def      __lowerCAmelCase						(							**UpperCAmelCase__				:      Any       )  ->    Dict:
							for key, value in kwargs.items():
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    str:
							if not hasattr(UpperCAmelCase__			,				"""__qualname__"""       ) and not hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""__class__"""			,				UpperCAmelCase__       )
							if hasattr(UpperCAmelCase__			,				"""__qualname__"""       ):
														return obj.__qualname__
							if hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														return obj.__name__
							return str(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:
							for key, value in source.items():
														if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
																					lowerCamelCase_							  =					destination.setdefault(UpperCAmelCase__			,				{}       )
																					merge_dicts(UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					lowerCamelCase_							  =					value

							return destination



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = None       )  ->    bool:
							if port is None:
														lowerCamelCase_							  =					2_9_5_0_0
							with socket.socket(socket.AF_INET			,				socket.SOCK_STREAM       ) as s:
														return s.connect_ex(("""localhost""", port)       ) == 0







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												if images is None and text is None:
																			raise ValueError("""You have to specify either images or text."""   )

												# Get only text
												if images is None:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
																			return text_encoding

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

												if text_encoding is not None:
																			encoding_image_processor.update(__UpperCamelCase   )

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





