code: 
  
  
  
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
        import os
  
        # The slow tests are often failing with OOM error on GPU
        # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
        # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
        SCREAMING_SNAKE_CASE__		:       List[Any]   							=							"""platform"""
  
        import jax
        import jax.numpy as jnp
  
        from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None   ,       __lowerCamelCase=None					)   -> Tuple:
  
       '''simple docstring'''
       if attention_mask is None:
            UpperCAmelCase__			:     Optional[int]										= np.where(input_ids != config.pad_token_id   ,       1   ,       0					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }
  
  
  
  
  
  
  
  @require_flax
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=13  ,	_lowerCAmelCase=7  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=False  ,	_lowerCAmelCase=99  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=4  ,	_lowerCAmelCase=4  ,	_lowerCAmelCase="gelu"  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=20  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=1  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=0.0_2  ,	):
            UpperCAmelCase__			:     Optional[int]										= parent
            UpperCAmelCase__			:     Any										= batch_size
            UpperCAmelCase__			:     int										= seq_length
            UpperCAmelCase__			:     str										= is_training
            UpperCAmelCase__			:     Any										= use_labels
            UpperCAmelCase__			:     int										= vocab_size
            UpperCAmelCase__			:     Dict										= hidden_size
            UpperCAmelCase__			:     Union[str, Any]										= num_hidden_layers
            UpperCAmelCase__			:     List[str]										= num_attention_heads
            UpperCAmelCase__			:     List[str]										= intermediate_size
            UpperCAmelCase__			:     List[Any]										= hidden_act
            UpperCAmelCase__			:     List[Any]										= hidden_dropout_prob
            UpperCAmelCase__			:     Optional[Any]										= attention_probs_dropout_prob
            UpperCAmelCase__			:     Tuple										= max_position_embeddings
            UpperCAmelCase__			:     str										= eos_token_id
            UpperCAmelCase__			:     Any										= pad_token_id
            UpperCAmelCase__			:     int										= bos_token_id
            UpperCAmelCase__			:     str										= embed_dim
            UpperCAmelCase__			:     int										= word_embed_proj_dim
            UpperCAmelCase__			:     str										= initializer_range
            UpperCAmelCase__			:     Union[str, Any]										= False
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,	self.vocab_size		)  ,	3  ,	self.vocab_size		)
            UpperCAmelCase__			:     Dict										= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,	dtype=np.intaa		))  ,	-1		)
  
            UpperCAmelCase__			:     List[Any]										= OPTConfig(
                vocab_size=self.vocab_size  ,	hidden_size=self.hidden_size  ,	num_hidden_layers=self.num_hidden_layers  ,	num_attention_heads=self.num_attention_heads  ,	ffn_dim=self.intermediate_size  ,	dropout=self.hidden_dropout_prob  ,	attention_dropout=self.attention_probs_dropout_prob  ,	max_position_embeddings=self.max_position_embeddings  ,	eos_token_id=self.eos_token_id  ,	bos_token_id=self.bos_token_id  ,	pad_token_id=self.pad_token_id  ,	embed_dim=self.embed_dim  ,	is_encoder_decoder=_lowerCAmelCase  ,	word_embed_proj_dim=self.word_embed_proj_dim  ,	initializer_range=self.initializer_range  ,	use_cache=_lowerCAmelCase  ,	)
            UpperCAmelCase__			:     Optional[int]										= prepare_opt_inputs_dict(_lowerCAmelCase  ,	_lowerCAmelCase		)
            return config, inputs_dict
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			,		UpperCAmelCase__			:     str										= self.prepare_config_and_inputs()
            return config, inputs_dict
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Union[str, Any]										= 20
            UpperCAmelCase__			:     Optional[Any]										= model_class_name(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[Any]										= inputs_dict["""input_ids"""]
            UpperCAmelCase__			:     Dict										= inputs_dict["""attention_mask"""]
  
            UpperCAmelCase__			:     Tuple										= model.init_cache(input_ids.shape[0]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= jnp.ones((input_ids.shape[0], max_length)  ,	dtype="""i4"""		)
  
            UpperCAmelCase__			:     List[Any]										= jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,	(input_ids.shape[0], input_ids.shape[-1] - 1)  ,	)
            UpperCAmelCase__			:     Optional[int]										= model(
                input_ids[:, :-1]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Any										= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,	dtype="""i4"""		)
            UpperCAmelCase__			:     int										= model(
                input_ids[:, -1:]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=outputs_cache.past_key_values  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
            self.parent.assertTrue(diff < 1e-3  ,	msg=f"Max diff is {diff}"		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Dict										= 20
            UpperCAmelCase__			:     List[Any]										= model_class_name(_lowerCAmelCase		)
  
            UpperCAmelCase__			,		UpperCAmelCase__			:     str										= (
                inputs_dict["""input_ids"""],
                inputs_dict["""attention_mask"""],
            )
  
            UpperCAmelCase__			:     int										= jnp.concatenate(
                [
                    attention_mask,
                    jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])		),
                ]  ,	axis=-1  ,	)
  
            UpperCAmelCase__			:     Dict										= model.init_cache(input_ids.shape[0]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,	(input_ids.shape[0], input_ids.shape[-1] - 1)  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(
                input_ids[:, :-1]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
            UpperCAmelCase__			:     Tuple										= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,	dtype="""i4"""		)
            UpperCAmelCase__			:     Optional[int]										= model(
                input_ids[:, -1:]  ,	past_key_values=outputs_cache.past_key_values  ,	attention_mask=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(_lowerCAmelCase  ,	attention_mask=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     int										= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
            self.parent.assertTrue(diff < 1e-3  ,	msg=f"Max diff is {diff}"		)
  
  
  
  
  
  
  
  @require_flax
  class 							UpperCAmelCase_     (		__lowerCamelCase       ,			unittest.TestCase       ,			__lowerCamelCase		):
       __lowerCamelCase										=     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       __lowerCamelCase										=     () if is_flax_available() else ()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Any										= FlaxOPTModelTester(self		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			,		UpperCAmelCase__			:     int										= self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			,		UpperCAmelCase__			:     Tuple										= self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       @slow
       def      __UpperCAmelCase     (							self		):
            for model_class_name in self.all_model_classes:
                 UpperCAmelCase__			:     int										= model_class_name.from_pretrained("""facebook/opt-125m"""		)
                 UpperCAmelCase__			:     Any										= np.ones((1, 1)		) * model.config.eos_token_id
                 UpperCAmelCase__			:     List[Any]										= model(_lowerCAmelCase		)
                 self.assertIsNotNone(_lowerCAmelCase		)
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       @slow
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= FlaxOPTModel.from_pretrained("""facebook/opt-350m"""		)
            UpperCAmelCase__			:     List[Any]										= jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
            UpperCAmelCase__			:     Any										= model(input_ids=_lowerCAmelCase		).last_hidden_state
            UpperCAmelCase__			:     Union[str, Any]										= (1, 11, 512)
            self.assertEqual(output.shape  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= jnp.array(
                [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]		)
            self.assertTrue(jnp.allclose(output[:, :3, :3]  ,	_lowerCAmelCase  ,	atol=4e-2		)		)
  
  
  
  
  
  
  
  @require_flax
  @slow
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            super().setUp()
            UpperCAmelCase__			:     Optional[int]										= """facebook/opt-350m"""
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Dict										= FlaxOPTForCausalLM.from_pretrained(self.path_model		)
            UpperCAmelCase__			:     str										= GPTaTokenizer.from_pretrained(self.path_model		)
  
            UpperCAmelCase__			:     str										= [
                """Today is a beautiful day and I want to""",
                """In the city of""",
                """Paris is the capital of France and""",
                """Computers and mobile phones have taken""",
            ]
            # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
            UpperCAmelCase__			:     List[Any]										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase  ,	add_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= model(inputs.input_ids  ,	attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
            UpperCAmelCase__			:     Tuple										= jnp.array(
                [
                    [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                    [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                    [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                    [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
                ]		)
            self.assertTrue(jnp.allclose(_lowerCAmelCase  ,	_lowerCAmelCase  ,	atol=4e-2		)		)
  
            UpperCAmelCase__			:     str										= jax.jit(_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[int]										= model(inputs.input_ids  ,	attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
            self.assertTrue(jnp.allclose(_lowerCAmelCase  ,	_lowerCAmelCase  ,	atol=4e-2		)		)
  
  
  
  
  
  
  
  @require_flax
  @slow
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            return [
                "Today is a beautiful day and I want",
                "In the city of",
                "Paris is the capital of France and",
                "Computers and mobile phones have taken",
            ]
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= """facebook/opt-125m"""
  
            UpperCAmelCase__			:     List[Any]										= [
                """Today is a beautiful day and I want to""",
                """In the city of New York, the city""",
                """Paris is the capital of France and the capital""",
                """Computers and mobile phones have taken over the""",
            ]
  
            UpperCAmelCase__			:     Optional[Any]										= []
  
            UpperCAmelCase__			:     List[Any]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
  
            for prompt in self.prompts:
                 UpperCAmelCase__			:     List[Any]										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""		).input_ids
  
                 UpperCAmelCase__			:     List[str]										= model.generate(_lowerCAmelCase  ,	max_length=10		)
                 UpperCAmelCase__			:     List[str]										= generated_ids[0]
  
                 UpperCAmelCase__			:     str										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
                 predicted_outputs += generated_string
  
            self.assertListEqual(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[str]										= """facebook/opt-350m"""
  
            UpperCAmelCase__			:     Tuple										= [
                """Today is a beautiful day and I want to""",
                """In the city of San Francisco, the city""",
                """Paris is the capital of France and the capital""",
                """Computers and mobile phones have taken over the""",
            ]
  
            UpperCAmelCase__			:     str										= []
            UpperCAmelCase__			:     List[str]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
  
            for prompt in self.prompts:
                 UpperCAmelCase__			:     str										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""		).input_ids
  
                 UpperCAmelCase__			:     Optional[Any]										= model.generate(_lowerCAmelCase  ,	max_length=10		)
                 UpperCAmelCase__			:     Any										= generated_ids[0]
  
                 UpperCAmelCase__			:     Tuple										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
                 predicted_outputs += generated_string
  
            self.assertListEqual(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Union[str, Any]										= """facebook/opt-125m"""
            UpperCAmelCase__			:     Optional[int]										= [
                """Today is a beautiful day and I want to thank""",
                """In the city of Rome Canaver Canaver Canaver Canaver""",
            ]
            UpperCAmelCase__			:     Any										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= tokenizer(
                [
                    """Today is a beautiful day and I want to""",
                    """In the city of""",
                ]  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     int										= jax.jit(model.generate		)
  
            UpperCAmelCase__			:     int										= jit_generate(inputs["""input_ids"""]  ,	attention_mask=inputs["""attention_mask"""]		).sequences
  
            UpperCAmelCase__			:     str										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
  
            self.assertIsNotNone(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= """facebook/opt-350m"""
  
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     List[Any]										= """left"""
  
            # use different length sentences to test batching
            UpperCAmelCase__			:     Union[str, Any]										= [
                """Hello, my dog is a little""",
                """Today, I""",
            ]
  
            UpperCAmelCase__			:     str										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= inputs["""input_ids"""]
  
            UpperCAmelCase__			:     List[Any]										= model.generate(input_ids=_lowerCAmelCase  ,	attention_mask=inputs["""attention_mask"""]  ,	trace=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[int]										= tokenizer(sentences[0]  ,	return_tensors="""jax"""		).input_ids
            UpperCAmelCase__			:     List[Any]										= model.generate(input_ids=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[int]										= inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
            UpperCAmelCase__			:     List[str]										= tokenizer(sentences[1]  ,	return_tensors="""jax"""		).input_ids
            UpperCAmelCase__			:     Optional[int]										= model.generate(input_ids=_lowerCAmelCase  ,	max_length=model.config.max_length - num_paddings		)
  
            UpperCAmelCase__			:     Any										= tokenizer.batch_decode(outputs[0]  ,	skip_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= tokenizer.decode(output_non_padded[0][0]  ,	skip_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[str]										= tokenizer.decode(output_padded[0][0]  ,	skip_special_tokens=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     int										= [
                """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
                """Today, I was in the middle of a conversation with a friend about the""",
            ]
            self.assertListEqual(_lowerCAmelCase  ,	_lowerCAmelCase		)
            self.assertListEqual(_lowerCAmelCase  ,	[non_padded_sentence, padded_sentence]		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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'
  
  
  
  
  
  
  