


import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
				import tensorflow as tf
				from tensorflow.python.framework.errors_impl import ResourceExhaustedError

				from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
				import pyanvml.pyanvml as nvml

lowerCAmelCase							=						logging.get_logger(__name__)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						def run_func(lowercase_			):
												@wraps(lowercase_			)
												def run_in_eager_mode(*lowercase_			,					**lowercase_			):
																		return func(*lowercase_			,					**lowercase_			)

												@wraps(lowercase_			)
												@tf.function(experimental_compile=lowercase_			)
												def run_in_graph_mode(*lowercase_			,					**lowercase_			):
																		return func(*lowercase_			,					**lowercase_			)

												if do_eager_mode is True:
																		if use_xla is not False:
																								raise ValueError(
																								    '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''			)
																		return run_in_eager_mode
												else:
																		return run_in_graph_mode

						return run_func




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    ["tf.Tensor"]:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  random.Random()
						__UpperCAmelCase :       Any  						=  [rng.randint(0			,					vocab_size - 1			) for i in range(batch_size * sequence_length			)]
						return tf.constant(lowercase_			,					shape=(batch_size, sequence_length)			,					dtype=tf.intaa			)





class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		TensorFlowBenchmarkArguments
	_lowerCAmelCase    :		PretrainedConfig
	_lowerCAmelCase    :		str        =	"TensorFlow"


	@property
	def    A(						self):
							return tf.__version__


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							# initialize GPU on separate process
							__UpperCAmelCase :       Dict  						=  self.args.strategy
							if strategy is None:
													raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
							__UpperCAmelCase :       int  						=  self._prepare_inference_func(lowercase__							,    lowercase__							,    lowercase__)
							return self._measure_speed(_inference)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       int  						=  self.args.strategy
							if strategy is None:
													raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
							__UpperCAmelCase :       List[Any]  						=  self._prepare_train_func(lowercase__							,    lowercase__							,    lowercase__)
							return self._measure_speed(_train)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							# initialize GPU on separate process
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,    lowercase__)
							__UpperCAmelCase :       Tuple  						=  self.args.strategy
							if strategy is None:
													raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
							__UpperCAmelCase :       List[str]  						=  self._prepare_inference_func(lowercase__							,    lowercase__							,    lowercase__)
							return self._measure_memory(_inference)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,    lowercase__)
							__UpperCAmelCase :       str  						=  self.args.strategy
							if strategy is None:
													raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')

							__UpperCAmelCase :       Optional[Any]  						=  self._prepare_train_func(lowercase__							,    lowercase__							,    lowercase__)
							return self._measure_memory(_train)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Any  						=  self.config_dict[model_name]

							if self.args.fpaa:
													raise NotImplementedError('''Mixed precision is currently not supported.''')

							__UpperCAmelCase :       Tuple  						=  (
							    hasattr(lowercase__							,    '''architectures''')
							    and isinstance(config.architectures							,    lowercase__)
							    and len(config.architectures) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																			__UpperCAmelCase :       Optional[int]  						=  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																			__UpperCAmelCase :       List[str]  						=  __import__('''transformers'''							,    fromlist=[model_class])
																			__UpperCAmelCase :       Union[str, Any]  						=  getattr(lowercase__							,    lowercase__)
																			__UpperCAmelCase :       Any  						=  model_cls(lowercase__)
													except ImportError:
																			raise ImportError(
																			    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																			    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''')
							else:
													__UpperCAmelCase :       int  						=  TF_MODEL_MAPPING[config.__class__](lowercase__)

							# encoder-decoder has vocab size saved differently
							__UpperCAmelCase :       Dict  						=  config.vocab_size if hasattr(lowercase__							,    '''vocab_size''') else config.encoder.vocab_size
							__UpperCAmelCase :       Optional[int]  						=  random_input_ids(lowercase__							,    lowercase__							,    lowercase__)

							@run_with_tf_optimizations(self.args.eager_mode							,    self.args.use_xla)
							def encoder_decoder_forward():
													return model(lowercase__							,    decoder_input_ids=lowercase__							,    training=lowercase__)

							@run_with_tf_optimizations(self.args.eager_mode							,    self.args.use_xla)
							def encoder_forward():
													return model(lowercase__							,    training=lowercase__)

							__UpperCAmelCase :       int  						=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Optional[Any]  						=  self.config_dict[model_name]

							if self.args.eager_mode is not False:
													raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''')

							if self.args.fpaa:
													raise NotImplementedError('''Mixed precision is currently not supported.''')

							__UpperCAmelCase :       Tuple  						=  (
							    hasattr(lowercase__							,    '''architectures''')
							    and isinstance(config.architectures							,    lowercase__)
							    and len(config.architectures) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																			__UpperCAmelCase :       List[Any]  						=  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																			__UpperCAmelCase :       Any  						=  __import__('''transformers'''							,    fromlist=[model_class])
																			__UpperCAmelCase :       Dict  						=  getattr(lowercase__							,    lowercase__)
																			__UpperCAmelCase :       Optional[int]  						=  model_cls(lowercase__)
													except ImportError:
																			raise ImportError(
																			    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																			    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''')
							else:
													__UpperCAmelCase :       int  						=  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__)

							# encoder-decoder has vocab size saved differently
							__UpperCAmelCase :       Dict  						=  config.vocab_size if hasattr(lowercase__							,    '''vocab_size''') else config.encoder.vocab_size
							__UpperCAmelCase :       Any  						=  random_input_ids(lowercase__							,    lowercase__							,    lowercase__)

							@run_with_tf_optimizations(self.args.eager_mode							,    self.args.use_xla)
							def encoder_decoder_train():
													__UpperCAmelCase :       Any  						=  model(lowercase__							,    decoder_input_ids=lowercase__							,    labels=lowercase__							,    training=lowercase__)[0]
													__UpperCAmelCase :       str  						=  tf.gradients(lowercase__							,    model.trainable_variables)
													return gradients

							@run_with_tf_optimizations(self.args.eager_mode							,    self.args.use_xla)
							def encoder_train():
													__UpperCAmelCase :       str  						=  model(lowercase__							,    labels=lowercase__							,    training=lowercase__)[0]
													__UpperCAmelCase :       int  						=  tf.gradients(lowercase__							,    model.trainable_variables)
													return gradients

							__UpperCAmelCase :       Dict  						=  encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train


	def    A(						self							,    lowercase__):
							with self.args.strategy.scope():
													try:
																			if self.args.is_tpu or self.args.use_xla:
																									# run additional 10 times to stabilize compilation for tpu
																									logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''')
																									timeit.repeat(lowercase__							,    repeat=1							,    number=5)

																			# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																			__UpperCAmelCase :       Optional[int]  						=  timeit.repeat(
																			    lowercase__							,    repeat=self.args.repeat							,    number=1_0							,    )

																			return min(lowercase__) / 1_0.0
													except ResourceExhaustedError as e:
																			self.print_fn(F"Doesn't fit on GPU. {e}")


	def    A(						self							,    lowercase__):
							logger.info(
							    '''Note that TensorFlow allocates more memory than '''
							    '''it might need to speed up computation. '''
							    '''The memory reported here corresponds to the memory '''
							    '''reported by `nvidia-smi`, which can vary depending '''
							    '''on total available memory on the GPU that is used.''')
							with self.args.strategy.scope():
													try:
																			if self.args.trace_memory_line_by_line:
																									if not self.args.eager_mode:
																															raise ValueError(
																															    '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
																															    ''' consumption line by line.''')
																									__UpperCAmelCase :       Optional[Any]  						=  start_memory_tracing('''transformers''')

																			if self.args.is_tpu:
																									# tpu
																									raise NotImplementedError(
																									    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
																									    ''' with `args.memory=False`''')
																			elif self.args.is_gpu:
																									# gpu
																									if not is_pyanvml_available():
																															logger.warning(
																															    '''py3nvml not installed, we won\'t log GPU memory usage. '''
																															    '''Install py3nvml (pip install py3nvml) to log information about GPU.''')
																															__UpperCAmelCase :       Any  						=  '''N/A'''
																									else:
																															logger.info(
																															    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
																															    ''' running on the same GPU.''')
																															# init nvml
																															nvml.nvmlInit()
																															func()
																															__UpperCAmelCase :       Optional[int]  						=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
																															__UpperCAmelCase :       int  						=  nvml.nvmlDeviceGetMemoryInfo(lowercase__)
																															__UpperCAmelCase :       Dict  						=  meminfo.used
																															__UpperCAmelCase :       Optional[Any]  						=  Memory(lowercase__)
																															# shutdown nvml
																															nvml.nvmlShutdown()
																			else:
																									# cpu
																									if self.args.trace_memory_line_by_line:
																															logger.info(
																															    '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
																															    ''' TensorFlow.''')
																															__UpperCAmelCase :       Any  						=  None
																									else:
																															__UpperCAmelCase :       int  						=  measure_peak_memory_cpu(lowercase__)
																															__UpperCAmelCase :       List[str]  						=  Memory(lowercase__) if isinstance(lowercase__							,    lowercase__) else memory_bytes
																			if self.args.trace_memory_line_by_line:
																									__UpperCAmelCase :       Any  						=  stop_memory_tracing(lowercase__)
																									if memory is None:
																															__UpperCAmelCase :       Tuple  						=  summary.total
																			else:
																									__UpperCAmelCase :       List[str]  						=  None

																			return memory, summary
													except ResourceExhaustedError as e:
																			self.print_fn(F"Doesn't fit on GPU. {e}")
																			return "N/A", None





import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptTokenizer,
				)
				from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class        lowerCamelCase      :


	def __init__(						self							,    lowercase__							,    lowercase__=1_3							,    lowercase__=7							,    lowercase__=True							,    lowercase__=True							,    lowercase__=False							,    lowercase__=True							,    lowercase__=9_9							,    lowercase__=3_2							,    lowercase__=5							,    lowercase__=4							,    lowercase__=3_7							,    lowercase__="gelu"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=5_1_2							,    lowercase__=1_6							,    lowercase__=2							,    lowercase__=0.0_2							,    lowercase__=3							,    lowercase__=4							,    lowercase__=None							,    ):
							__UpperCAmelCase :       Tuple  						=  parent
							__UpperCAmelCase :       List[Any]  						=  batch_size
							__UpperCAmelCase :       Optional[Any]  						=  seq_length
							__UpperCAmelCase :       Tuple  						=  is_training
							__UpperCAmelCase :       List[Any]  						=  use_input_mask
							__UpperCAmelCase :       List[str]  						=  use_token_type_ids
							__UpperCAmelCase :       Union[str, Any]  						=  use_labels
							__UpperCAmelCase :       Union[str, Any]  						=  vocab_size
							__UpperCAmelCase :       Optional[int]  						=  hidden_size
							__UpperCAmelCase :       Any  						=  num_hidden_layers
							__UpperCAmelCase :       Optional[Any]  						=  num_attention_heads
							__UpperCAmelCase :       str  						=  intermediate_size
							__UpperCAmelCase :       Dict  						=  hidden_act
							__UpperCAmelCase :       str  						=  hidden_dropout_prob
							__UpperCAmelCase :       Optional[Any]  						=  attention_probs_dropout_prob
							__UpperCAmelCase :       List[str]  						=  max_position_embeddings
							__UpperCAmelCase :       Tuple  						=  type_vocab_size
							__UpperCAmelCase :       int  						=  type_sequence_label_size
							__UpperCAmelCase :       List[Any]  						=  initializer_range
							__UpperCAmelCase :       List[str]  						=  num_labels
							__UpperCAmelCase :       Dict  						=  num_choices
							__UpperCAmelCase :       Union[str, Any]  						=  scope


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size)

							__UpperCAmelCase :       Dict  						=  None
							if self.use_input_mask:
													__UpperCAmelCase :       List[Any]  						=  random_attention_mask([self.batch_size, self.seq_length])

							__UpperCAmelCase :       Union[str, Any]  						=  None
							if self.use_token_type_ids:
													__UpperCAmelCase :       List[str]  						=  ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size)

							__UpperCAmelCase :       Union[str, Any]  						=  None
							__UpperCAmelCase :       Tuple  						=  None
							__UpperCAmelCase :       Optional[int]  						=  None
							if self.use_labels:
													__UpperCAmelCase :       List[str]  						=  ids_tensor([self.batch_size]							,    self.type_sequence_label_size)
													__UpperCAmelCase :       List[str]  						=  ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels)
													__UpperCAmelCase :       List[str]  						=  ids_tensor([self.batch_size]							,    self.num_choices)

							__UpperCAmelCase :       Optional[Any]  						=  self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


	def    A(						self):
							return BioGptConfig(
							    vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=lowercase__							,    initializer_range=self.initializer_range							,    )


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Union[str, Any]  						=  BioGptModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       int  						=  model(lowercase__							,    attention_mask=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  model(lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							__UpperCAmelCase :       Optional[Any]  						=  BioGptForCausalLM(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       List[Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    token_type_ids=lowercase__							,    labels=lowercase__)
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       str  						=  BioGptModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()

							# create attention mask
							__UpperCAmelCase :       str  						=  torch.ones(input_ids.shape							,    dtype=torch.long							,    device=lowercase__)
							__UpperCAmelCase :       int  						=  self.seq_length // 2
							__UpperCAmelCase :       Any  						=  0

							# first forward pass
							__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  model(lowercase__							,    attention_mask=lowercase__).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							__UpperCAmelCase :       Union[str, Any]  						=  ids_tensor((self.batch_size, 1)							,    config.vocab_size)

							# change a random masked slice from input_ids
							__UpperCAmelCase :       Tuple  						=  ids_tensor((1,)							,    lowercase__).item() + 1
							__UpperCAmelCase :       Optional[Any]  						=  ids_tensor((self.batch_size, 1)							,    config.vocab_size).squeeze(-1)
							__UpperCAmelCase :       int  						=  random_other_next_tokens

							# append to next input_ids and attn_mask
							__UpperCAmelCase :       Optional[Any]  						=  torch.cat([input_ids, next_tokens]							,    dim=-1)
							__UpperCAmelCase :       int  						=  torch.cat(
							    [attn_mask, torch.ones((attn_mask.shape[0], 1)							,    dtype=torch.long							,    device=lowercase__)]							,    dim=1							,    )

							# get two different outputs
							__UpperCAmelCase :       Optional[Any]  						=  model(lowercase__							,    attention_mask=lowercase__)['''last_hidden_state''']
							__UpperCAmelCase :       List[Any]  						=  model(lowercase__							,    past_key_values=lowercase__							,    attention_mask=lowercase__)['''last_hidden_state''']

							# select random slice
							__UpperCAmelCase :       Tuple  						=  ids_tensor((1,)							,    output_from_past.shape[-1]).item()
							__UpperCAmelCase :       List[str]  						=  output_from_no_past[:, -1, random_slice_idx].detach()
							__UpperCAmelCase :       int  						=  output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(lowercase__							,    lowercase__							,    atol=1e-3))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       int  						=  BioGptModel(config=lowercase__).to(lowercase__).eval()

							__UpperCAmelCase :       List[str]  						=  torch.ones(input_ids.shape							,    dtype=torch.long							,    device=lowercase__)

							# first forward pass
							__UpperCAmelCase :       Union[str, Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    use_cache=lowercase__)

							__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							__UpperCAmelCase :       Any  						=  ids_tensor((self.batch_size, 3)							,    config.vocab_size)
							__UpperCAmelCase :       Optional[int]  						=  ids_tensor((self.batch_size, 3)							,    2)

							# append to next input_ids and
							__UpperCAmelCase :       Any  						=  torch.cat([input_ids, next_tokens]							,    dim=-1)
							__UpperCAmelCase :       Any  						=  torch.cat([attention_mask, next_attn_mask]							,    dim=-1)

							__UpperCAmelCase :       List[Any]  						=  model(lowercase__							,    attention_mask=lowercase__)['''last_hidden_state''']
							__UpperCAmelCase :       int  						=  model(lowercase__							,    attention_mask=lowercase__							,    past_key_values=lowercase__)[
							    '''last_hidden_state'''
							]

							# select random slice
							__UpperCAmelCase :       List[str]  						=  ids_tensor((1,)							,    output_from_past.shape[-1]).item()
							__UpperCAmelCase :       List[str]  						=  output_from_no_past[:, -3:, random_slice_idx].detach()
							__UpperCAmelCase :       Dict  						=  output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(lowercase__							,    lowercase__							,    atol=1e-3))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__							,    lowercase__=False):
							__UpperCAmelCase :       int  						=  BioGptForCausalLM(lowercase__)
							model.to(lowercase__)
							if gradient_checkpointing:
													model.gradient_checkpointing_enable()

							__UpperCAmelCase :       Tuple  						=  model(lowercase__							,    labels=lowercase__)
							self.parent.assertEqual(result.loss.shape							,    ())
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size))
							result.loss.backward()


	def    A(						self							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       Optional[int]  						=  BioGptModel(lowercase__)
							__UpperCAmelCase :       int  						=  model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
							for key in model.state_dict().keys():
													if "c_proj" in key and "weight" in key:
																			self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std)							,    0.0_0_1)
																			self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)							,    0.0_1)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       Optional[Any]  						=  self.num_labels
							__UpperCAmelCase :       List[str]  						=  BioGptForTokenClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       List[str]  						=  model(lowercase__							,    attention_mask=lowercase__							,    token_type_ids=lowercase__)
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels))


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.prepare_config_and_inputs()
							(
							    (
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			
							) :       int  						=  config_and_inputs
							__UpperCAmelCase :       List[Any]  						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class        lowerCamelCase      (     _UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		str        =	(
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase    :		int        =	(BioGptForCausalLM,) if is_torch_available() else ()
	_lowerCAmelCase    :		Union[str, Any]        =	(
	    {
	        '''feature-extraction''': BioGptModel,
	        '''text-classification''': BioGptForSequenceClassification,
	        '''text-generation''': BioGptForCausalLM,
	        '''token-classification''': BioGptForTokenClassification,
	        '''zero-shot''': BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase    :		List[Any]        =	False


	def    A(						self):
							__UpperCAmelCase :       int  						=  BioGptModelTester(self)
							__UpperCAmelCase :       int  						=  ConfigTester(self							,    config_class=lowercase__							,    hidden_size=3_7)


	def    A(						self):
							self.config_tester.run_common_tests()


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													__UpperCAmelCase :       Dict  						=  type
													self.model_tester.create_and_check_model(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowercase__							,    gradient_checkpointing=lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       Any  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(lowercase__)
							__UpperCAmelCase :       Dict  						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

							__UpperCAmelCase :       List[str]  						=  '''left'''

							# Define PAD Token = EOS Token = 50256
							__UpperCAmelCase :       List[Any]  						=  tokenizer.eos_token
							__UpperCAmelCase :       Tuple  						=  model.config.eos_token_id

							# use different length sentences to test batching
							__UpperCAmelCase :       Optional[Any]  						=  [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							__UpperCAmelCase :       int  						=  tokenizer(lowercase__							,    return_tensors='''pt'''							,    padding=lowercase__)
							__UpperCAmelCase :       Union[str, Any]  						=  inputs['''input_ids'''].to(lowercase__)

							__UpperCAmelCase :       int  						=  model.generate(
							    input_ids=lowercase__							,    attention_mask=inputs['''attention_mask'''].to(lowercase__)							,    )

							__UpperCAmelCase :       Any  						=  tokenizer(sentences[0]							,    return_tensors='''pt''').input_ids.to(lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  model.generate(input_ids=lowercase__)

							__UpperCAmelCase :       Optional[int]  						=  inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
							__UpperCAmelCase :       str  						=  tokenizer(sentences[1]							,    return_tensors='''pt''').input_ids.to(lowercase__)
							__UpperCAmelCase :       Any  						=  model.generate(input_ids=lowercase__							,    max_length=model.config.max_length - num_paddings)

							__UpperCAmelCase :       Optional[int]  						=  tokenizer.batch_decode(lowercase__							,    skip_special_tokens=lowercase__)
							__UpperCAmelCase :       Any  						=  tokenizer.decode(output_non_padded[0]							,    skip_special_tokens=lowercase__)
							__UpperCAmelCase :       Any  						=  tokenizer.decode(output_padded[0]							,    skip_special_tokens=lowercase__)

							__UpperCAmelCase :       str  						=  [
							    '''Hello, my dog is a little bit bigger than a little bit.''',
							    '''Today, I have a good idea of how to use the information''',
							]
							self.assertListEqual(lowercase__							,    lowercase__)
							self.assertListEqual(lowercase__							,    [non_padded_sentence, padded_sentence])


	@slow
	def    A(						self):
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__UpperCAmelCase :       Union[str, Any]  						=  BioGptModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase :       Dict  						=  3
							__UpperCAmelCase :       List[Any]  						=  input_dict['''input_ids''']
							__UpperCAmelCase :       int  						=  input_ids.ne(1).to(lowercase__)
							__UpperCAmelCase :       Optional[Any]  						=  ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size)
							__UpperCAmelCase :       Any  						=  BioGptForSequenceClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       Optional[int]  						=  model(lowercase__							,    attention_mask=lowercase__							,    labels=lowercase__)
							self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels))


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase :       List[Any]  						=  3
							__UpperCAmelCase :       Union[str, Any]  						=  '''multi_label_classification'''
							__UpperCAmelCase :       List[Any]  						=  input_dict['''input_ids''']
							__UpperCAmelCase :       Tuple  						=  input_ids.ne(1).to(lowercase__)
							__UpperCAmelCase :       List[str]  						=  ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size).to(torch.float)
							__UpperCAmelCase :       List[Any]  						=  BioGptForSequenceClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       Optional[Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    labels=lowercase__)
							self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels))






@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@slow
	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							__UpperCAmelCase :       Optional[Any]  						=  torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]])
							__UpperCAmelCase :       int  						=  model(lowercase__)[0]

							__UpperCAmelCase :       Any  						=  4_2_3_8_4

							__UpperCAmelCase :       Tuple  						=  torch.Size((1, 5, vocab_size))
							self.assertEqual(output.shape							,    lowercase__)

							__UpperCAmelCase :       Dict  						=  torch.tensor(
							    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]])

							self.assertTrue(torch.allclose(output[:, :3, :3]							,    lowercase__							,    atol=1e-4))


	@slow
	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
							__UpperCAmelCase :       int  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(lowercase__)

							torch.manual_seed(0)
							__UpperCAmelCase :       int  						=  tokenizer('''COVID-19 is'''							,    return_tensors='''pt''').to(lowercase__)
							__UpperCAmelCase :       List[str]  						=  model.generate(
							    **lowercase__							,    min_length=1_0_0							,    max_length=1_0_2_4							,    num_beams=5							,    early_stopping=lowercase__							,    )
							__UpperCAmelCase :       List[Any]  						=  tokenizer.decode(output_ids[0]							,    skip_special_tokens=lowercase__)

							__UpperCAmelCase :       int  						=  (
							    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
							    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
							    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
							    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
							    ''' more than 800,000 deaths.'''
							)
							self.assertEqual(lowercase__							,    lowercase__)

