




import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=None   )->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       None
		# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
		if str(weight.dtype   ) == "torch.bfloat16":
				# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
				SCREAMING_SNAKE_CASE_							=       weight.view(torch.intaa   )
				SCREAMING_SNAKE_CASE_							=       "bfloat16"
		SCREAMING_SNAKE_CASE_							=       weight.cpu().numpy()
		SCREAMING_SNAKE_CASE_							=       os.path.join(lowerCAmelCase						,	F'''{weight_name}.dat'''   )
		if index is not None:
				if dtype is None:
						SCREAMING_SNAKE_CASE_							=       str(array.dtype   )
				SCREAMING_SNAKE_CASE_							=       {"dtype": dtype, "shape": list(array.shape   )}
		if array.ndim == 0:
				SCREAMING_SNAKE_CASE_							=       array[None]
		SCREAMING_SNAKE_CASE_							=       np.memmap(lowerCAmelCase						,	dtype=array.dtype						,	mode='w+'						,	shape=array.shape   )
		SCREAMING_SNAKE_CASE_							=       array[:]
		file_array.flush()
		return index


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       tuple(weight_info['shape']   )
		if shape == ():
				# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
				SCREAMING_SNAKE_CASE_							=       (1,)

		SCREAMING_SNAKE_CASE_							=       weight_info["dtype"]
		if dtype == "bfloat16":
				# NumPy does not support bfloat16 so this was saved as a int16
				SCREAMING_SNAKE_CASE_							=       "int16"

		SCREAMING_SNAKE_CASE_							=       np.memmap(lowerCAmelCase						,	dtype=lowerCAmelCase						,	shape=lowerCAmelCase						,	mode='r'   )

		if len(weight_info['shape']   ) == 0:
				SCREAMING_SNAKE_CASE_							=       weight[0]
		SCREAMING_SNAKE_CASE_							=       torch.tensor(lowerCAmelCase   )
		if weight_info["dtype"] == "bfloat16":
				SCREAMING_SNAKE_CASE_							=       weight.view(torch.bfloataa   )

		return weight


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Optional[Any]:



		if index is None or len(lowerCAmelCase   ) == 0:
				# Nothing to save
				return

		SCREAMING_SNAKE_CASE_							=       os.path.join(lowerCAmelCase						,	'index.json'   )
		if os.path.isfile(lowerCAmelCase   ):
				with open(lowerCAmelCase						,	'r'						,	encoding='utf-8'   ) as f:
						SCREAMING_SNAKE_CASE_							=       json.load(lowerCAmelCase   )
		else:
				SCREAMING_SNAKE_CASE_							=       {}
		current_index.update(lowerCAmelCase   )

		with open(lowerCAmelCase						,	'w'						,	encoding='utf-8'   ) as f:
				json.dump(lowerCAmelCase						,	lowerCAmelCase						,	indent=2   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		os.makedirs(lowerCAmelCase						,	exist_ok=lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       {}
		for name, parameter in state_dict.items():
				SCREAMING_SNAKE_CASE_							=       offload_weight(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	index=lowerCAmelCase   )

		# Update index
		save_offload_index(lowerCAmelCase						,	lowerCAmelCase   )



class       lowercase_		(UpperCamelCase__   ):



						def __init__( self  :      Dict				,       snake_case__  :      Tuple				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       dataset
								SCREAMING_SNAKE_CASE_							=       prefix



						def __getitem__( self  :      Tuple				,       snake_case__  :      List[str]     ):

								"""simple docstring"""

								return self.dataset[f'''{self.prefix}{key}''']



						def __iter__( self  :      str     ):

								"""simple docstring"""

								return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )






						def __len__( self  :      Optional[Any]     ):

								"""simple docstring"""

								return len(self.dataset     )



class       lowercase_		(UpperCamelCase__   ):



						def __init__( self  :      Any				,       snake_case__  :      List[str] = None				,       snake_case__  :      List[str] = None				,       snake_case__  :      Dict = None				,       snake_case__  :      int=None				,       ):

								"""simple docstring"""

								if state_dict is None and save_folder is None:
										raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'     )

								SCREAMING_SNAKE_CASE_							=       {} if state_dict is None else state_dict
								SCREAMING_SNAKE_CASE_							=       save_folder
								if index is None and save_folder is not None:
										with open(os.path.join(lowerCAmelCase__				,       'index.json'     )     ) as f:
												SCREAMING_SNAKE_CASE_							=       json.load(lowerCAmelCase__     )
								SCREAMING_SNAKE_CASE_							=       {} if index is None else index
								SCREAMING_SNAKE_CASE_							=       list(self.state_dict.keys()     )
								self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
								SCREAMING_SNAKE_CASE_							=       device



						def __getitem__( self  :      Tuple				,       snake_case__  :      List[str]     ):

								"""simple docstring"""

								if key in self.state_dict:
										return self.state_dict[key]
								SCREAMING_SNAKE_CASE_							=       self.index[key]
								if weight_info.get('safetensors_file'     ) is not None:
										if not is_safetensors_available():
												raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'     )

										from safetensors import safe_open

										SCREAMING_SNAKE_CASE_							=       "cpu" if self.device is None else self.device
										with safe_open(weight_info['safetensors_file']				,       framework='pt'				,       device=lowerCAmelCase__     ) as f:
												SCREAMING_SNAKE_CASE_							=       f.get_tensor(weight_info.get('weight_name'				,       lowerCAmelCase__     )     )

										if "dtype" in weight_info:
												return tensor.to(getattr(lowerCAmelCase__				,       weight_info['dtype']     )     )
										else:
												return tensor

								SCREAMING_SNAKE_CASE_							=       os.path.join(self.save_folder				,       f'''{key}.dat'''     )
								return load_offloaded_weight(lowerCAmelCase__				,       lowerCAmelCase__     )



						def __iter__( self  :      Optional[Any]     ):

								"""simple docstring"""

								return iter(self.all_keys     )






						def __len__( self  :      Optional[int]     ):

								"""simple docstring"""

								return len(self.all_keys     )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       {}
		for module_name in submodule_names:
				# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
				# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
				result.update(
				    {
				        key: param
				        for key, param in state_dict.items()
				        if key == module_name or key.startswith(module_name + '.'   )
				    }   )
		return result





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)



class       lowercase_		:



						def __init__( self  :      Union[str, Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Dict=21				,       snake_case__  :      List[str]=[]				,       snake_case__  :      List[str]=None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       model
								SCREAMING_SNAKE_CASE_							=       trace_batch_nums
								SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       collections.deque([]				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       []
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       False
								SCREAMING_SNAKE_CASE_							=       '                 '

								self.analyse_model()

								self.register_forward_hook()



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Optional[int]=None     ):

								"""simple docstring"""

								if frame is not None:
										self.expand_frame(snake_case__     )
								self.frames.append('\n'.join(self.frame     )     )
								SCREAMING_SNAKE_CASE_							=       []  # start a new frame



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Any     ):

								"""simple docstring"""

								self.frame.append(snake_case__     )



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								print('\n'.join(self.frames     )     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      int     ):

								"""simple docstring"""

								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'     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       {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   __a				( self  :      List[str]				,       snake_case__  :      List[str]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								if torch.is_tensor(snake_case__     ):
										self.expand_frame(get_abs_min_max(snake_case__				,       snake_case__     )     )
										if detect_overflow(snake_case__				,       snake_case__     ):
												SCREAMING_SNAKE_CASE_							=       True
								elif var is None:
										self.expand_frame(f'''{"None":>17} {ctx}'''     )
								else:
										self.expand_frame(f'''{"not a tensor":>17} {ctx}'''     )



						def   __a				( self  :      str     ):

								"""simple docstring"""

								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   __a				( self  :      List[str]     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''     )



						def   __a				( self  :      Optional[int]				,       snake_case__  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''     )

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

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

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

								self.save_frame()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self.model.apply(self._register_forward_hook     )



						def   __a				( self  :      int				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								module.register_forward_hook(self.forward_hook     )






						def   __a				( self  :      Optional[Any]				,       snake_case__  :      str				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       False

								SCREAMING_SNAKE_CASE_							=       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
										SCREAMING_SNAKE_CASE_							=       True

								self.create_frame(snake_case__				,       snake_case__				,       snake_case__     )

								# 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 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       var.abs()
		return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_							=       False
		if torch.isnan(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has nans'''   )
		if torch.isinf(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has infs'''   )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	100   )]
				if naaa.numel() > 0:
						print(F'''{ctx}:  n100={naaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	1000   )]
				if naaaa.numel() > 0:
						print(F'''{ctx}: n1000={naaaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	10000   )]
				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       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="underflow_overflow"
						lowerCAmelCase__ 			="tpu_metrics_debug"