






from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							tf.config.list_physical_devices("""GPU""")
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        SCREAMING_SNAKE_CASE__		:       int   							=							tf.config.list_logical_devices("""GPU""")
                        print("""Logical GPUs""", logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)







@require_tf
class 							UpperCAmelCase_     :
     __lowerCamelCase										=     None
     __lowerCamelCase										=     ()
     __lowerCamelCase										=     ()
     __lowerCamelCase										=     True
     __lowerCamelCase										=     True
     __lowerCamelCase										=     True
     __lowerCamelCase										=     False




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=False		):
          UpperCAmelCase__			:     List[Any]										= copy.deepcopy(_lowerCAmelCase		)

          if model_class in get_values(_lowerCAmelCase		):
               UpperCAmelCase__			:     Any										= {
                   k: tf.tile(tf.expand_dims(_lowerCAmelCase  ,	1		)  ,	(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
                   if isinstance(_lowerCAmelCase  ,	tf.Tensor		) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

          if return_labels:
               if model_class in get_values(_lowerCAmelCase		):
                    UpperCAmelCase__			:     Dict										= tf.ones(self.model_tester.batch_size  ,	dtype=tf.intaa		)
               elif model_class in get_values(_lowerCAmelCase		):
                    UpperCAmelCase__			:     Union[str, Any]										= tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa		)
                    UpperCAmelCase__			:     Tuple										= tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa		)
               elif model_class in [
                   *get_values(_lowerCAmelCase		),
                   *get_values(_lowerCAmelCase		),
               ]:
                    UpperCAmelCase__			:     List[str]										= tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa		)
               elif model_class in get_values(_lowerCAmelCase		):
                    UpperCAmelCase__			:     Union[str, Any]										= tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa		)
               elif model_class in [
                   *get_values(_lowerCAmelCase		),
                   *get_values(_lowerCAmelCase		),
                   *get_values(_lowerCAmelCase		),
                   *get_values(_lowerCAmelCase		),
                   *get_values(_lowerCAmelCase		),
               ]:
                    UpperCAmelCase__			:     List[str]										= tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)  ,	dtype=tf.intaa		)
          return inputs_dict




     @slow
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__			:     Optional[Any]										= self._prepare_for_class(_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Optional[int]										= model_class(_lowerCAmelCase		)

               @tf.function
               def run_in_graph_mode():
                    return model(_lowerCAmelCase		)

               UpperCAmelCase__			:     Dict										= run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase		)




     @slow
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[Any]										= self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__			:     Optional[int]										= self._prepare_for_class(_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Tuple										= model_class(_lowerCAmelCase		)

               @tf.function(experimental_compile=_lowerCAmelCase		)
               def run_in_graph_mode():
                    return model(_lowerCAmelCase		)

               UpperCAmelCase__			:     int										= run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase		)




     @slow
     def      __UpperCAmelCase     (							self		):
          # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
          UpperCAmelCase__			,		UpperCAmelCase__			:     Dict										= self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__			:     Optional[Any]										= model_class(_lowerCAmelCase		)
               if getattr(_lowerCAmelCase  ,	"""hf_compute_loss"""  ,	_lowerCAmelCase		):
                    # Test that model correctly compute the loss with kwargs
                    UpperCAmelCase__			:     Any										= self._prepare_for_class(inputs_dict.copy()  ,	_lowerCAmelCase  ,	return_labels=_lowerCAmelCase		)
                    # Is there a better way to remove these decoder inputs?
                    UpperCAmelCase__			:     Optional[Any]										= {
                        key: val
                        for key, val in prepared_for_class.items()
                        if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
                    }

                    UpperCAmelCase__			:     Optional[int]										= {
                        """labels""",
                        """label""",
                        """label_ids""",
                        """start_positions""",
                        """start_position""",
                        """end_positions""",
                        """end_position""",
                        """next_sentence_label""",
                    }
                    UpperCAmelCase__			:     Tuple										= possible_label_cols.intersection(set(_lowerCAmelCase		)		)
                    self.assertGreater(len(_lowerCAmelCase		)  ,	0  ,	msg="""No matching label names found!"""		)
                    UpperCAmelCase__			:     Any										= {key: val for key, val in prepared_for_class.items() if key in label_names}
                    UpperCAmelCase__			:     Dict										= {key: val for key, val in prepared_for_class.items() if key not in label_names}
                    self.assertGreater(len(_lowerCAmelCase		)  ,	0		)

                    # Make sure it works with XLA!
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)  ,	jit_compile=_lowerCAmelCase		)
                    # Make sure the model fits without crashing regardless of where we pass the labels
                    UpperCAmelCase__			:     Tuple										= model.fit(
                        _lowerCAmelCase  ,	validation_data=_lowerCAmelCase  ,	steps_per_epoch=1  ,	validation_steps=1  ,	shuffle=_lowerCAmelCase  ,	verbose=0  ,	)
                    UpperCAmelCase__			:     Any										= history.history["""loss"""][0]
                    self.assertTrue(not isnan(_lowerCAmelCase		)		)
                    UpperCAmelCase__			:     Tuple										= history.history["""val_loss"""][0]
                    self.assertTrue(not isnan(_lowerCAmelCase		)		)

                    # Now test it with separate labels, to make sure that path works in XLA too.
                    UpperCAmelCase__			:     Tuple										= model_class(_lowerCAmelCase		)
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)  ,	jit_compile=_lowerCAmelCase		)
                    UpperCAmelCase__			:     Optional[int]										= model.fit(
                        _lowerCAmelCase  ,	_lowerCAmelCase  ,	validation_data=(inputs_minus_labels, labels)  ,	steps_per_epoch=1  ,	validation_steps=1  ,	shuffle=_lowerCAmelCase  ,	verbose=0  ,	)

                    UpperCAmelCase__			:     List[Any]										= history.history["""loss"""][0]
                    self.assertTrue(not isnan(_lowerCAmelCase		)		)
                    UpperCAmelCase__			:     Any										= history.history["""val_loss"""][0]
                    self.assertTrue(not isnan(_lowerCAmelCase		)		)




     @slow
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:     Union[str, Any]										= True
          UpperCAmelCase__			:     Any										= True

          if hasattr(_lowerCAmelCase  ,	"""use_cache"""		):
               UpperCAmelCase__			:     Optional[Any]										= True

          UpperCAmelCase__			:     Optional[int]										= getattr(self.model_tester  ,	"""encoder_seq_length"""  ,	self.model_tester.seq_length		)
          UpperCAmelCase__			:     List[str]										= getattr(self.model_tester  ,	"""key_length"""  ,	_lowerCAmelCase		)

          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__			:     Union[str, Any]										= self._prepare_for_class(_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Union[str, Any]										= model_class(_lowerCAmelCase		)
               model.build()
               UpperCAmelCase__			:     Any										= len(model(_lowerCAmelCase		)		)

               for key in list(class_inputs_dict.keys()		):
                    # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                    if key not in model.input_signature:
                         del class_inputs_dict[key]
                    # Check it's a tensor, in case the inputs dict has some bools in it too
                    elif isinstance(class_inputs_dict[key]  ,	tf.Tensor		) and class_inputs_dict[key].dtype.is_integer:
                         UpperCAmelCase__			:     List[str]										= tf.cast(class_inputs_dict[key]  ,	tf.intaa		)

               if set(class_inputs_dict.keys()		) != set(model.input_signature.keys()		):
                    continue  # Some models have inputs that the preparation functions don't create, we skip those

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(_lowerCAmelCase  ,	saved_model=_lowerCAmelCase		)
                    UpperCAmelCase__			:     Optional[int]										= os.path.join(_lowerCAmelCase  ,	"""saved_model"""  ,	"""1"""		)
                    UpperCAmelCase__			:     List[str]										= tf.keras.models.load_model(_lowerCAmelCase		)
                    UpperCAmelCase__			:     List[str]										= model(_lowerCAmelCase		)

                    if self.is_encoder_decoder:
                         UpperCAmelCase__			:     Union[str, Any]										= outputs["""encoder_hidden_states"""]
                         UpperCAmelCase__			:     Union[str, Any]										= outputs["""encoder_attentions"""]
                    else:
                         UpperCAmelCase__			:     Union[str, Any]										= outputs["""hidden_states"""]
                         UpperCAmelCase__			:     Dict										= outputs["""attentions"""]

                    self.assertEqual(len(_lowerCAmelCase		)  ,	_lowerCAmelCase		)

                    UpperCAmelCase__			:     List[Any]										= getattr(
                        self.model_tester  ,	"""expected_num_hidden_layers"""  ,	self.model_tester.num_hidden_layers + 1		)

                    self.assertEqual(len(_lowerCAmelCase		)  ,	_lowerCAmelCase		)
                    self.assertListEqual(
                        list(output_hidden_states[0].shape[-2:]		)  ,	[self.model_tester.seq_length, self.model_tester.hidden_size]  ,	)

                    self.assertEqual(len(_lowerCAmelCase		)  ,	self.model_tester.num_hidden_layers		)
                    self.assertListEqual(
                        list(output_attentions[0].shape[-3:]		)  ,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,	)




     @slow
     def      __UpperCAmelCase     (							self		):
          tf.keras.mixed_precision.set_global_policy("""mixed_float16"""		)

          # try/finally block to ensure subsequent tests run in float32
          try:
               UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.model_tester.prepare_config_and_inputs_for_common()
               for model_class in self.all_model_classes[:2]:
                    UpperCAmelCase__			:     Optional[int]										= self._prepare_for_class(_lowerCAmelCase  ,	_lowerCAmelCase		)
                    UpperCAmelCase__			:     Dict										= model_class(_lowerCAmelCase		)
                    UpperCAmelCase__			:     Optional[int]										= model(_lowerCAmelCase		)

                    self.assertIsNotNone(_lowerCAmelCase		)
          finally:
               tf.keras.mixed_precision.set_global_policy("""float32"""		)




     @slow
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.model_tester.prepare_config_and_inputs_for_common()
          # head_mask and decoder_head_mask has different shapes than other input args
          if "head_mask" in inputs_dict:
               del inputs_dict["head_mask"]
          if "decoder_head_mask" in inputs_dict:
               del inputs_dict["decoder_head_mask"]
          if "cross_attn_head_mask" in inputs_dict:
               del inputs_dict["cross_attn_head_mask"]
          UpperCAmelCase__			:     str										= {
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__		),)
              for module_member_name in dir(_lowerCAmelCase		)
              if module_member_name.endswith("""MainLayer"""		)
              for module_member in (getattr(_lowerCAmelCase  ,	_lowerCAmelCase		),)
              if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(_lowerCAmelCase  ,	"""_keras_serializable"""  ,	_lowerCAmelCase		)
          }

          for main_layer_class in tf_main_layer_classes:
               # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
               if "T5" in main_layer_class.__name__:
                    # Take the same values than in TFT5ModelTester for this shared layer
                    UpperCAmelCase__			:     Tuple										= TFSharedEmbeddings(self.model_tester.vocab_size  ,	self.model_tester.hidden_size  ,	name="""shared"""		)
                    UpperCAmelCase__			:     List[str]										= False
                    UpperCAmelCase__			:     str										= main_layer_class(_lowerCAmelCase  ,	embed_tokens=_lowerCAmelCase		)
               else:
                    UpperCAmelCase__			:     Dict										= main_layer_class(_lowerCAmelCase		)

               UpperCAmelCase__			:     Tuple										= {
                   name: tf.keras.Input(tensor.shape[1:]  ,	dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
               }

               if hasattr(self.model_tester  ,	"""num_labels"""		):
                    UpperCAmelCase__			:     Optional[int]										= self.model_tester.num_labels
               else:
                    UpperCAmelCase__			:     Tuple										= 2

               UpperCAmelCase__			:     Optional[int]										= tf.data.Dataset.from_tensor_slices(
                   (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)		))		).batch(1		)

               UpperCAmelCase__			:     List[Any]										= main_layer(_lowerCAmelCase		)[0]
               UpperCAmelCase__			:     Optional[int]										= tf.keras.layers.Dense(_lowerCAmelCase  ,	activation="""softmax"""  ,	name="""outputs"""		)(_lowerCAmelCase		)
               UpperCAmelCase__			:     List[str]										= tf.keras.models.Model(inputs=_lowerCAmelCase  ,	outputs=[outputs]		)

               model.compile(loss="""binary_crossentropy"""  ,	optimizer="""adam"""  ,	metrics=["""binary_accuracy"""]		)
               model.fit(_lowerCAmelCase  ,	epochs=1		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    UpperCAmelCase__			:     str										= os.path.join(_lowerCAmelCase  ,	"""keras_model.h5"""		)
                    model.save(_lowerCAmelCase		)
                    if "T5" in main_layer_class.__name__:
                         UpperCAmelCase__			:     List[Any]										= tf.keras.models.load_model(
                             _lowerCAmelCase  ,	custom_objects={
                                 main_layer_class.__name__: main_layer_class,
                                 """TFSharedEmbeddings""": TFSharedEmbeddings,
                             }  ,	)
                    else:
                         UpperCAmelCase__			:     Any										= tf.keras.models.load_model(
                             _lowerCAmelCase  ,	custom_objects={main_layer_class.__name__: main_layer_class}		)
                    assert isinstance(_lowerCAmelCase  ,	tf.keras.Model		)
                    model(_lowerCAmelCase		)




     @slow
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[Any]										= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__			:     Tuple										= model_class(_lowerCAmelCase		)

               UpperCAmelCase__			:     List[str]										= copy.deepcopy(_lowerCAmelCase		)

               if not self.is_encoder_decoder:
                    UpperCAmelCase__			:     List[Any]										= inputs["""input_ids"""]
                    del inputs["input_ids"]
               else:
                    UpperCAmelCase__			:     str										= inputs["""input_ids"""]
                    UpperCAmelCase__			:     Optional[Any]										= inputs.get("""decoder_input_ids"""  ,	_lowerCAmelCase		)
                    del inputs["input_ids"]
                    inputs.pop("""decoder_input_ids"""  ,	_lowerCAmelCase		)

               if not self.is_encoder_decoder:
                    UpperCAmelCase__			:     Any										= model.get_input_embeddings()(_lowerCAmelCase		)
               else:
                    UpperCAmelCase__			:     int										= model.get_input_embeddings()(_lowerCAmelCase		)
                    UpperCAmelCase__			:     Dict										= model.get_input_embeddings()(_lowerCAmelCase		)

               UpperCAmelCase__			:     Dict										= self._prepare_for_class(_lowerCAmelCase  ,	_lowerCAmelCase		)

               @tf.function
               def run_in_graph_mode():
                    return model(_lowerCAmelCase		)

               UpperCAmelCase__			:     Union[str, Any]										= run_in_graph_mode()
               self.assertIsNotNone(_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          # special tokens cannot be bad tokens
          UpperCAmelCase__			:     str										= []
          if model.config.bos_token_id is not None:
               special_tokens.append(model.config.bos_token_id		)
          if model.config.pad_token_id is not None:
               special_tokens.append(model.config.pad_token_id		)
          if model.config.eos_token_id is not None:
               special_tokens.append(model.config.eos_token_id		)

          # create random bad tokens that are not special tokens
          UpperCAmelCase__			:     Tuple										= []
          while len(_lowerCAmelCase		) < num_bad_tokens:
               UpperCAmelCase__			:     List[Any]										= tf.squeeze(ids_tensor((1, 1)  ,	self.model_tester.vocab_size		)  ,	0		).numpy()[0]
               if token not in special_tokens:
                    bad_tokens.append(_lowerCAmelCase		)
          return bad_tokens




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(_lowerCAmelCase  ,	0		)
               self.assertLess(_lowerCAmelCase  ,	self.model_tester.vocab_size		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          # for all bad word tokens
          for bad_word_ids in bad_words_ids:
               # for all slices in batch
               for generated_ids_slice in generated_ids:
                    # for all word idx
                    for i in range(len(_lowerCAmelCase		)  ,	len(_lowerCAmelCase		)		):
                         # if tokens match
                         if generated_ids_slice[i - len(_lowerCAmelCase		) : i] == bad_word_ids:
                              return True
          return False











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 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
          UpperCAmelCase__			:     Union[str, Any]										= model
          UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
          UpperCAmelCase__			:     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
          UpperCAmelCase__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
          UpperCAmelCase__			:     str										= []
          UpperCAmelCase__			:     Optional[int]										= 0
          UpperCAmelCase__			:     int										= 0
          UpperCAmelCase__			:     int										= False
          UpperCAmelCase__			:     List[str]										= """                 """

          self.analyse_model()

          self.register_forward_hook()




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
          if frame is not None:
               self.expand_frame(_lowerCAmelCase		)
          self.frames.append("""\n""".join(self.frame		)		)
          UpperCAmelCase__			:     str										= []  # start a new frame




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          self.frame.append(_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self		):
          print("""\n""".join(self.frames		)		)
          UpperCAmelCase__			:     List[Any]										= []




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[int]										= []




     def      __UpperCAmelCase     (							self		):
          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"""		)
          UpperCAmelCase__			:     Any										= []




     def      __UpperCAmelCase     (							self		):
          # 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
          UpperCAmelCase__			:     str										= {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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          if torch.is_tensor(_lowerCAmelCase		):
               self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
               if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                    UpperCAmelCase__			:     Optional[int]										= True
          elif var is None:
               self.expand_frame(f"{'None':>17} {ctx}"		)
          else:
               self.expand_frame(f"{'not a tensor':>17} {ctx}"		)




     def      __UpperCAmelCase     (							self		):
          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      __UpperCAmelCase     (							self		):
          self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          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      __UpperCAmelCase     (							self		):
          self.model.apply(self._register_forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          module.register_forward_hook(self.forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          # - input is a tuple of packed inputs (could be non-Tensors)
          # - output could be a Tensor or a tuple of Tensors and non-Tensors

          UpperCAmelCase__			:     Union[str, Any]										= False

          UpperCAmelCase__			:     List[str]										= 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
               UpperCAmelCase__			:     Dict										= 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Dict										= var.abs()
     return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:

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

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
          if naaa.numel() > 0:
               print(F"{ctx}:  n100={naaa.numel()}"					)
          UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
          if naaaa.numel() > 0:
               print(F"{ctx}: n1000={naaaa.numel()}"					)
          UpperCAmelCase__			:     Optional[int]										= var[torch.ge(var.abs()   ,       1_0000					)]
          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 							UpperCAmelCase_     (		__lowerCamelCase		):
     __lowerCamelCase										=     'underflow_overflow'
     __lowerCamelCase										=     'tpu_metrics_debug'






