





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""image_processor""", """tokenizer"""]
 UpperCAmelCase__						=						"""AutoImageProcessor"""
 UpperCAmelCase__						=						"""AutoTokenizer"""
 def __init__( self    : str     ,	UpperCAmelCase    : Tuple=None     ,	UpperCAmelCase    : Dict=None     ,	**UpperCAmelCase    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'     ,	UpperCAmelCase     ,	)
         lowerCamelCase__       : List[str]   					=						kwargs.pop('feature_extractor'      )

     lowerCamelCase__       : Optional[Any]   					=						image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'      )
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'      )

     super().__init__(UpperCAmelCase     ,	UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						self.image_processor
     lowerCamelCase__       : Union[str, Any]   					=						False
 def __call__( self    : Optional[int]     ,	*UpperCAmelCase    : Optional[Any]     ,	**UpperCAmelCase    : List[str]      )     ->      List[str]:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*UpperCAmelCase     ,	**UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						kwargs.pop('images'     ,	UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						kwargs.pop('text'     ,	UpperCAmelCase      )
     if len(UpperCAmelCase      ) > 0:
         lowerCamelCase__       : Optional[Any]   					=						args[0]
         lowerCamelCase__       : Tuple   					=						args[1:]

     if images is None and text is None:
         raise ValueError('You need to specify either an `images` or `text` input to process.'      )

     if images is not None:
         lowerCamelCase__       : List[Any]   					=						self.image_processor(UpperCAmelCase     ,	*UpperCAmelCase     ,	**UpperCAmelCase      )
     if text is not None:
         lowerCamelCase__       : Optional[Any]   					=						self.tokenizer(UpperCAmelCase     ,	**UpperCAmelCase      )

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         lowerCamelCase__       : Optional[Any]   					=						encodings['input_ids']
         return inputs
 def 	A_      ( self    : Optional[int]     ,	*UpperCAmelCase    : Tuple     ,	**UpperCAmelCase    : Any      )     ->      Dict:
     return self.tokenizer.batch_decode(*UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Any     ,	*UpperCAmelCase    : Dict     ,	**UpperCAmelCase    : Tuple      )     ->      List[str]:
     return self.tokenizer.decode(*UpperCAmelCase     ,	**UpperCAmelCase      )
 @contextmanager
 def 	A_      ( self    : int      )     ->      int:
     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your images inputs, or in a separate call.'      )
     lowerCamelCase__       : Any   					=						True
     lowerCamelCase__       : Union[str, Any]   					=						self.tokenizer
     yield
     lowerCamelCase__       : Dict   					=						self.image_processor
     lowerCamelCase__       : Optional[Any]   					=						False
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[int]=False     ,	UpperCAmelCase    : Optional[Any]=None      )     ->      List[Any]:
     if added_vocab is None:
         lowerCamelCase__       : Union[str, Any]   					=						self.tokenizer.get_added_vocab()

     lowerCamelCase__       : List[str]   					=						{}

     while tokens:
         lowerCamelCase__       : List[Any]   					=						re.search(R'<s_(.*?)>'     ,	UpperCAmelCase     ,	re.IGNORECASE      )
         if start_token is None:
             break
         lowerCamelCase__       : str   					=						start_token.group(1      )
         lowerCamelCase__       : Optional[Any]   					=						re.search(RF"""</s_{key}>"""     ,	UpperCAmelCase     ,	re.IGNORECASE      )
         lowerCamelCase__       : str   					=						start_token.group()
         if end_token is None:
             lowerCamelCase__       : Tuple   					=						tokens.replace(UpperCAmelCase     ,	''      )
         else:
             lowerCamelCase__       : List[Any]   					=						end_token.group()
             lowerCamelCase__       : int   					=						re.escape(UpperCAmelCase      )
             lowerCamelCase__       : Dict   					=						re.escape(UpperCAmelCase      )
             lowerCamelCase__       : Dict   					=						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""     ,	UpperCAmelCase     ,	re.IGNORECASE      )
             if content is not None:
                 lowerCamelCase__       : str   					=						content.group(1      ).strip()
                 if r"<s_" in content and r"</s_" in content:  # non-leaf node
                     lowerCamelCase__       : Union[str, Any]   					=						self.tokenajson(UpperCAmelCase     ,	is_inner_value=UpperCAmelCase     ,	added_vocab=UpperCAmelCase      )
                     if value:
                         if len(UpperCAmelCase      ) == 1:
                             lowerCamelCase__       : Dict   					=						value[0]
                         lowerCamelCase__       : Optional[Any]   					=						value
                 else:  # leaf nodes
                     lowerCamelCase__       : Optional[int]   					=						[]
                     for leaf in content.split(R'<sep/>'      ):
                         lowerCamelCase__       : List[Any]   					=						leaf.strip()
                         if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                             lowerCamelCase__       : Dict   					=						leaf[1:-2]  # for categorical special tokens
                         output[key].append(UpperCAmelCase      )
                     if len(output[key]      ) == 1:
                         lowerCamelCase__       : Dict   					=						output[key][0]

             lowerCamelCase__       : Optional[int]   					=						tokens[tokens.find(UpperCAmelCase      ) + len(UpperCAmelCase      ) :].strip()
             if tokens[:6] == r"<sep/>":  # non-leaf nodes
                 return [output] + self.tokenajson(tokens[6:]     ,	is_inner_value=UpperCAmelCase     ,	added_vocab=UpperCAmelCase      )

     if len(UpperCAmelCase      ):
         return [output] if is_inner_value else output
     else:
         return [] if is_inner_value else {"text_sequence": tokens}
 @property
 def 	A_      ( self    : Tuple      )     ->      Any:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCAmelCase     ,	)
     return self.image_processor_class







 @property
 def 	A_      ( self    : Union[str, Any]      )     ->      int:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCAmelCase     ,	)
     return self.image_processor












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						max(self.process.memory_info().rss     ,	self.cpu_memory_peak      )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def 	A_      ( self    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    print(F"""{description}:"""					)
    print(F"""- Time: {measures["time"]:.2f}s"""					)
    for i in range(torch.cuda.device_count()					):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[str]   					=						measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""					)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""					)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""					)





