import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


UpperCAmelCase				 =   logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE:
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=21				,  lowerCAmelCase_=[]				,  lowerCAmelCase_=None     )						->Dict:
        a	:     List[str]						      =    model
        a	:     str						      =    trace_batch_nums
        a	:     Dict						      =    abort_after_batch_num

        # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
        a	:     int						      =    collections.deque([]				,  lowerCAmelCase_     )
        a	:     Tuple						      =    []
        a	:     Optional[int]						      =    0
        a	:     Tuple						      =    0
        a	:     Union[str, Any]						      =    False
        a	:     Any						      =    '''                 '''

        self.analyse_model()

        self.register_forward_hook()
   def _lowercase(     self				,  lowerCAmelCase_=None     )						->Optional[int]:
        if frame is not None:
             self.expand_frame(lowerCAmelCase_     )
        self.frames.append('''\n'''.join(self.frame     )     )
        a	:     Dict						      =    []  # start a new frame
   def _lowercase(     self				,  lowerCAmelCase_     )						->List[str]:
        self.frame.append(lowerCAmelCase_     )
   def _lowercase(     self     )						->Optional[Any]:
        print('''\n'''.join(self.frames     )     )
        a	:     str						      =    []
   def _lowercase(     self     )						->Optional[int]:
        a	:     Optional[Any]						      =    []
   def _lowercase(     self     )						->List[Any]:
        print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""     )
        print(F"""Last {len(self.frames     )} forward frames:"""     )
        print(F"""{'abs min':8} {'abs max':8} metadata"""     )
        print('''\n'''.join(self.frames     )     )
        print('''\n\n'''     )
        a	:     str						      =    []
   def _lowercase(     self     )						->Union[str, Any]:
        # extract the fully qualified module names, to be able to report at run time. e.g.:
        # encoder.block.2.layer.0.SelfAttention.o
        #
        # for shared weights only the first shared module name will be registered
        a	:     Union[str, Any]						      =    {m: name for name, m in self.model.named_modules()}
        # self.longest_module_name = max(len(v) for v in self.module_names.values())
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        if torch.is_tensor(lowerCAmelCase_     ):
             self.expand_frame(get_abs_min_max(lowerCAmelCase_				,  lowerCAmelCase_     )     )
             if detect_overflow(lowerCAmelCase_				,  lowerCAmelCase_     ):
                  a	:     Optional[Any]						      =    True
        elif var is None:
             self.expand_frame(F"""{'None':>17} {ctx}"""     )
        else:
             self.expand_frame(F"""{'not a tensor':>17} {ctx}"""     )
   def _lowercase(     self     )						->Dict:
        self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""     )
        self.expand_frame(F"""{'abs min':8} {'abs max':8} metadata"""     )
   def _lowercase(     self     )						->Dict:
        self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""     )

        # params
        for name, p in module.named_parameters(recurse=lowerCAmelCase_     ):
             self.analyse_variable(lowerCAmelCase_				,  lowerCAmelCase_     )

        # inputs
        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             for i, x in enumerate(lowerCAmelCase_     ):
                  self.analyse_variable(lowerCAmelCase_				,  F"""input[{i}]"""     )
        else:
             self.analyse_variable(lowerCAmelCase_				,  '''input'''     )

        # outputs
        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             for i, x in enumerate(lowerCAmelCase_     ):
                  # possibly a tuple of tuples
                  if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
                       for j, y in enumerate(lowerCAmelCase_     ):
                            self.analyse_variable(lowerCAmelCase_				,  F"""output[{i}][{j}]"""     )
                  else:
                       self.analyse_variable(lowerCAmelCase_				,  F"""output[{i}]"""     )
        else:
             self.analyse_variable(lowerCAmelCase_				,  '''output'''     )

        self.save_frame()
   def _lowercase(     self     )						->Union[str, Any]:
        self.model.apply(self._register_forward_hook     )
   def _lowercase(     self				,  lowerCAmelCase_     )						->Any:
        module.register_forward_hook(self.forward_hook     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        # - input is a tuple of packed inputs (could be non-Tensors)
        # - output could be a Tensor or a tuple of Tensors and non-Tensors

        a	:     Dict						      =    False

        a	:     Optional[int]						      =    True if self.batch_number in self.trace_batch_nums else False
        if trace_mode:
             self.reset_saved_frames()

        if self.total_calls == 0:
             self.batch_start_frame()
        self.total_calls += 1

        # count batch numbers - the very first forward hook of the batch will be called when the
        # batch completes - i.e. it gets called very last - we know this batch has finished
        if module == self.model:
             self.batch_number += 1
             a	:     List[Any]						      =    True

        self.create_frame(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )

        # if last_frame_of_batch:
        #     self.batch_end_frame()

        if trace_mode:
             self.trace_frames()

        if last_frame_of_batch:
             self.batch_start_frame()

        if self.detected_overflow and not trace_mode:
             self.dump_saved_frames()

             # now we can abort, as it's pointless to continue running
             raise ValueError(
                 '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
                 '''Please scroll up above this traceback to see the activation values prior to this event.'''     )

        # abort after certain batch if requested to do so
        if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
             raise ValueError(
                 F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
                 F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""     )







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Optional[int]						      =    var.abs()
     return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Tuple:
     a	:     int						      =    False
     if torch.isnan(lowerCAmelCase_							).any().item():
          a	:     List[str]						      =    True
          print(f"""{ctx} has nans"""							)
     if torch.isinf(lowerCAmelCase_							).any().item():
          a	:     Union[str, Any]						      =    True
          print(f"""{ctx} has infs"""							)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          a	:     List[Any]						      =    var[torch.ge(var.abs()    ,   1_0_0							)]
          if naaa.numel() > 0:
               print(f"""{ctx}:  n100={naaa.numel()}"""							)
          a	:     str						      =    var[torch.ge(var.abs()    ,   1_0_0_0							)]
          if naaaa.numel() > 0:
               print(f"""{ctx}: n1000={naaaa.numel()}"""							)
          a	:     Dict						      =    var[torch.ge(var.abs()    ,   1_0_0_0_0							)]
          if naaaaa.numel() > 0:
               print(f"""{ctx}: n10000={naaaaa.numel()}"""							)

     if 0:
          print(f"""min={var.min():9.2e} max={var.max():9.2e}"""							)

     if 0:
          print(f"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""							)

     return detected






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Optional[Any]			      = 'underflow_overflow'
   UpperCamelCase_					:    str			      = 'tpu_metrics_debug'
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase				 =   datasets.utils.logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilderConfig						):
   UpperCamelCase_					:    bool			      = None
   UpperCamelCase_					:    bool			      = None






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilder						):
   UpperCamelCase_					:    List[str]			      = datasets.Image()
   UpperCamelCase_					:    Any			      = 'image'
   UpperCamelCase_					:    str			      = ImageFolderConfig
   UpperCamelCase_					:    List[str]  # definition at the bottom of the script
   UpperCamelCase_					:    Union[str, Any]			      = ImageClassification(image_column='image'							,   label_column='label'						)

UpperCAmelCase				 =   [
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
UpperCAmelCase				 =   IMAGE_EXTENSIONS