




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = True   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    {}
    for name in model.attn_processors.keys():
        __UpperCamelCase			=    name.endswith('''attn2.processor'''   ) and not (
            name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
        )
        __UpperCamelCase			=    model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith('''mid_block'''   ):
            __UpperCamelCase			=    model.config.block_out_channels[-1]
        elif name.startswith('''up_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''up_blocks.'''   )]   )
            __UpperCamelCase			=    list(reversed(model.config.block_out_channels   )   )[block_id]
        elif name.startswith('''down_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''down_blocks.'''   )]   )
            __UpperCamelCase			=    model.config.block_out_channels[block_id]
        elif name.startswith('''transformer_in'''   ):
            # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
            __UpperCamelCase			=    8 * model.config.attention_head_dim

        __UpperCamelCase			=    LoRAAttnProcessor(hidden_size=lowercase_       ,  cross_attention_dim=lowercase_   )
        __UpperCamelCase			=    lora_attn_procs[name].to(model.device   )

        if mock_weights:
            # add 1 to weights to mock trained weights
            with torch.no_grad():
                lora_attn_procs[name].to_q_lora.up.weight += 1
                lora_attn_procs[name].to_k_lora.up.weight += 1
                lora_attn_procs[name].to_v_lora.up.weight += 1
                lora_attn_procs[name].to_out_lora.up.weight += 1

    return lora_attn_procs


@skip_mps
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			Dict     =       UNetaDConditionModel
      lowerCAmelCase__      :			Any     =       "sample"







      @property
      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    (32, 32)

          __UpperCamelCase			=    floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(snake_case					)
          __UpperCamelCase			=    torch.tensor([10]					).to(snake_case					)
          __UpperCamelCase			=    floats_tensor((batch_size, 4, 32)					).to(snake_case					)

          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







      @property
      def    snake_case						(  self       :			Tuple					):
          return (4, 4, 32, 32)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return (4, 4, 32, 32)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    {
              '''block_out_channels''': (32, 64),
              '''down_block_types''': (
                  '''CrossAttnDownBlock3D''',
                  '''DownBlock3D''',
              ),
              '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
              '''cross_attention_dim''': 32,
              '''attention_head_dim''': 8,
              '''out_channels''': 4,
              '''in_channels''': 4,
              '''layers_per_block''': 1,
              '''sample_size''': 32,
          }
          __UpperCamelCase			=    self.dummy_input
          return init_dict, inputs_dict







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()			,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,							)
      def    snake_case						(  self       :			Any					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    self.model_class(**snake_case					)

          model.enable_xformers_memory_efficient_attention()

          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)

              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    output.sample

          self.assertIsNotNone(snake_case					)
          __UpperCamelCase			=    inputs_dict['''sample'''].shape
          self.assertEqual(output.shape			,							snake_case			,							'''Input and output shapes do not match'''					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              # Warmup pass when using mps (see #372)
              if torch_device == "mps" and isinstance(snake_case			,							snake_case					):
                  model(**self.dummy_input					)

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    first.sample

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    second.sample

          __UpperCamelCase			=    first.cpu().numpy()
          __UpperCamelCase			=    second.cpu().numpy()
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    np.amax(np.abs(out_a - out_a					)					)
          self.assertLessEqual(snake_case			,							1E-5					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          model.set_attention_slice('''auto'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice('''max'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice(2					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)

          # make sure we can set a list of attention processors
          model.set_attn_processor(snake_case					)
          model.to(snake_case					)

          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (samplea - samplea).abs().max() < 3E-3
          assert (samplea - samplea).abs().max() < 3E-3

          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3E-3







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (sample - new_sample).abs().max() < 1E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case			,							safe_serialization=snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.safetensors'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (sample - new_sample).abs().max() < 3E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def    snake_case						(  self       :			Optional[int]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          __UpperCamelCase			=    create_lora_layers(snake_case			,							mock_weights=snake_case					)
          model.set_attn_processor(snake_case					)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case			,							weight_name='''pytorch_lora_weights.bin'''					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          __UpperCamelCase			=    create_lora_layers(snake_case			,							mock_weights=snake_case					)
          model.set_attn_processor(snake_case					)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              with self.assertRaises(snake_case					) as e:
                  new_model.load_attn_procs(snake_case			,							use_safetensors=snake_case					)
              self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''			,							str(e.exception					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample

          model.set_attn_processor(AttnProcessor()					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          assert (sample - new_sample).abs().max() < 1E-4
          assert (sample - old_sample).abs().max() < 3E-3







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()			,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,							)
      def    snake_case						(  self       :			Union[str, Any]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    4

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          # default
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

              model.enable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

              model.disable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

          assert (sample - on_sample).abs().max() < 1E-4
          assert (sample - off_sample).abs().max() < 1E-4







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)[0]

          self.assertEqual(output.shape			,							output_a.shape			,							'''Shape doesn\'t match'''					)
          assert np.abs(output.cpu() - output_a.cpu()					).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.











'''simple docstring'''






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCamelCase__	  =		logging.get_logger(__name__)

class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       Optional[int]      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       Union[str, Any]=21      ,		UpperCamelCase_							:       Dict=[]      ,		UpperCamelCase_							:       Any=None		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   model
									_lowercase							:		Tuple				=   trace_batch_nums
									_lowercase							:		Optional[Any]				=   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
									_lowercase							:		int				=   collections.deque([]      ,		UpperCamelCase_		)
									_lowercase							:		List[Any]				=   []
									_lowercase							:		List[str]				=   0
									_lowercase							:		Optional[int]				=   0
									_lowercase							:		List[Any]				=   False
									_lowercase							:		List[Any]				=   '''                 '''

									self.analyse_model()

									self.register_forward_hook()
		def 						__lowercase      (						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[Any]=None		)      ->			Optional[Any]:







									'''simple docstring'''





									if frame is not None:
																self.expand_frame(UpperCamelCase_		)
									self.frames.append('''\n'''.join(self.frame		)		)
									_lowercase							:		Dict				=   []  # start a new frame
		def 						__lowercase      (						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[Any]		)      ->			Union[str, Any]:







									'''simple docstring'''





									self.frame.append(UpperCamelCase_		)
		def 						__lowercase      (						self							:       str		)      ->			List[str]:







									'''simple docstring'''





									print('''\n'''.join(self.frames		)		)
									_lowercase							:		Union[str, Any]				=   []
		def 						__lowercase      (						self							:       Optional[int]		)      ->			List[str]:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   []
		def 						__lowercase      (						self							:       List[Any]		)      ->			Optional[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'''		)
									_lowercase							:		List[Any]				=   []
		def 						__lowercase      (						self							:       List[str]		)      ->			Tuple:







									'''simple docstring'''





									_lowercase							:		int				=   {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							:       Dict      ,		UpperCamelCase_							:       str      ,		UpperCamelCase_							:       Optional[int]		)      ->			int:







									'''simple docstring'''





									if torch.is_tensor(UpperCamelCase_		):
																self.expand_frame(get_abs_min_max(UpperCamelCase_      ,		UpperCamelCase_		)		)
																if detect_overflow(UpperCamelCase_      ,		UpperCamelCase_		):
																							_lowercase							:		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							:       Union[str, Any]		)      ->			int:







									'''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 						__lowercase      (						self							:       str		)      ->			Any:







									'''simple docstring'''





									self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
		def 						__lowercase      (						self							:       Tuple      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[int]		)      ->			Dict:







									'''simple docstring'''





									self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)

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

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

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

									self.save_frame()
		def 						__lowercase      (						self							:       Optional[int]		)      ->			Dict:







									'''simple docstring'''





									self.model.apply(self._register_forward_hook		)
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       int		)      ->			List[str]:







									'''simple docstring'''





									module.register_forward_hook(self.forward_hook		)





		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Dict      ,		UpperCamelCase_							:       List[Any]		)      ->			List[Any]:







									'''simple docstring'''





									_lowercase							:		str				=   False

									_lowercase							:		Dict				=   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
																_lowercase							:		Union[str, Any]				=   True

									self.create_frame(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)

									# 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 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any ,       snake_case_    :     Dict  )		->Any:






							'''simple docstring'''
							_lowercase							:		Union[str, Any]				=   var.abs()
							return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     Any  )		->int:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   False
							if torch.isnan(snake_case_  ).any().item():
														_lowercase							:		Any				=   True
														print(F"{ctx} has nans"  )
							if torch.isinf(snake_case_  ).any().item():
														_lowercase							:		Optional[Any]				=   True
														print(F"{ctx} has infs"  )

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														_lowercase							:		List[Any]				=   var[torch.ge(var.abs() ,       1_00  )]
														if naaa.numel() > 0:
																					print(F"{ctx}:  n100={naaa.numel()}"  )
														_lowercase							:		List[str]				=   var[torch.ge(var.abs() ,       10_00  )]
														if naaaa.numel() > 0:
																					print(F"{ctx}: n1000={naaaa.numel()}"  )
														_lowercase							:		Tuple				=   var[torch.ge(var.abs() ,       1_00_00  )]
														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 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		snake_case_							= 'underflow_overflow'
		snake_case_							= 'tpu_metrics_debug'



