

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)






class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def __init__(	self							:       Union[str, Any]					,					a							:       Optional[int]					,					a							:       str=13					,					a							:       str=7					,					a							:       List[Any]=True					,					a							:       List[str]=True					,					a							:       int=True					,					a							:       Any=True					,					a							:       Tuple=99					,					a							:       int=32					,					a							:       Union[str, Any]=5					,					a							:       str=4					,					a							:       Optional[Any]=37					,					a							:       Optional[Any]="gelu"					,					a							:       Any=0.1					,					a							:       Optional[Any]=0.1					,					a							:       Any=512					,					a							:       int=16					,					a							:       Optional[int]=2					,					a							:       Optional[int]=0.02					,					a							:       str=4					,					)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				parent
				SCREAMING_SNAKE_CASE      :					List[str]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				seq_length
				SCREAMING_SNAKE_CASE      :					List[Any]    =				is_training
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_attention_mask
				SCREAMING_SNAKE_CASE      :					Any    =				use_token_type_ids
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				vocab_size
				SCREAMING_SNAKE_CASE      :					int    =				hidden_size
				SCREAMING_SNAKE_CASE      :					int    =				num_hidden_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				num_attention_heads
				SCREAMING_SNAKE_CASE      :					List[str]    =				intermediate_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				hidden_act
				SCREAMING_SNAKE_CASE      :					List[Any]    =				hidden_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				max_position_embeddings
				SCREAMING_SNAKE_CASE      :					int    =				type_vocab_size
				SCREAMING_SNAKE_CASE      :					List[Any]    =				type_sequence_label_size
				SCREAMING_SNAKE_CASE      :					List[str]    =				initializer_range
				SCREAMING_SNAKE_CASE      :					str    =				num_choices
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
				if self.use_attention_mask:
					SCREAMING_SNAKE_CASE      :					List[str]    =				random_attention_mask([self.batch_size, self.seq_length]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
				if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size, self.seq_length]					,					self.type_vocab_size		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				RoFormerConfig(
				    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=a					,					initializer_range=self.initializer_range					,					)

				return config, input_ids, token_type_ids, attention_mask






			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[Any]    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					Dict    =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=True

			lowerCamelCase__							=(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				FlaxRoFormerModelTester(self		)






			@slow
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					int    =				model_class_name.from_pretrained("junnyu/roformer_chinese_small"					,					from_pt=a		)
					SCREAMING_SNAKE_CASE      :					Any    =				model(np.ones((1, 1)		)		)
					self.assertIsNotNone(a		)






@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				jnp.array([[0, 1, 2, 3, 4, 5]]		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				model(a		)[0]

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				5_0000

				SCREAMING_SNAKE_CASE      :					Any    =				(1, 6, vocab_size)
				self.assertEqual(output.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array(
				    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]		)

				self.assertTrue(jnp.allclose(output[:, :3, :3]					,					a					,					atol=1e-4		)		)
"""simple docstring"""


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

UpperCAmelCase__     						=  logging.get_logger(__name__)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """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 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					random.Random()
  _UpperCAmelCase         =					[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 				a (						lowerCAmelCase_      ):
    _snake_case   :     TensorFlowBenchmarkArguments
    _snake_case   :     PretrainedConfig
    _snake_case   :     str   							=							"TensorFlow"


    @property
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return tf.__version__


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      # initialize GPU on separate process
      _UpperCAmelCase         =					self.args.strategy
      if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
      _UpperCAmelCase         =					self._prepare_inference_func(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
      return self._measure_speed(_inference    )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					self.args.strategy
      if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
      _UpperCAmelCase         =					self._prepare_train_func(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
      return self._measure_speed(_train    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      # initialize GPU on separate process
      if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      , __lowerCAmelCase    )
      _UpperCAmelCase         =					self.args.strategy
      if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
      _UpperCAmelCase         =					self._prepare_inference_func(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
      return self._measure_memory(_inference    )


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

      _UpperCAmelCase         =					self._prepare_train_func(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
      return self._measure_memory(_train    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					self.config_dict[model_name]

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

      _UpperCAmelCase         =					(
          hasattr(__lowerCAmelCase      , """architectures"""    )
          and isinstance(config.architectures      , __lowerCAmelCase    )
          and len(config.architectures    ) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
          _UpperCAmelCase         =					"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
          _UpperCAmelCase         =					__import__("""transformers"""      , fromlist=[model_class]    )
          _UpperCAmelCase         =					getattr(__lowerCAmelCase      , __lowerCAmelCase    )
          _UpperCAmelCase         =					model_cls(__lowerCAmelCase    )
        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         =					TF_MODEL_MAPPING[config.__class__](__lowerCAmelCase    )

      # encoder-decoder has vocab size saved differently
      _UpperCAmelCase         =					config.vocab_size if hasattr(__lowerCAmelCase      , """vocab_size"""    ) else config.encoder.vocab_size
      _UpperCAmelCase         =					random_input_ids(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )

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

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

      _UpperCAmelCase         =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

      return _inference


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					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         =					(
          hasattr(__lowerCAmelCase      , """architectures"""    )
          and isinstance(config.architectures      , __lowerCAmelCase    )
          and len(config.architectures    ) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
          _UpperCAmelCase         =					"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
          _UpperCAmelCase         =					__import__("""transformers"""      , fromlist=[model_class]    )
          _UpperCAmelCase         =					getattr(__lowerCAmelCase      , __lowerCAmelCase    )
          _UpperCAmelCase         =					model_cls(__lowerCAmelCase    )
        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         =					TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCAmelCase    )

      # encoder-decoder has vocab size saved differently
      _UpperCAmelCase         =					config.vocab_size if hasattr(__lowerCAmelCase      , """vocab_size"""    ) else config.encoder.vocab_size
      _UpperCAmelCase         =					random_input_ids(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )

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

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

      _UpperCAmelCase         =					encoder_decoder_train if config.is_encoder_decoder else encoder_train

      return _train


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     Any    ):
      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(__lowerCAmelCase      , 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         =					timeit.repeat(
              __lowerCAmelCase      , repeat=self.args.repeat      , number=10      , )

          return min(__lowerCAmelCase    ) / 10.0
        except ResourceExhaustedError as e:
          self.print_fn(f'''Doesn\'t fit on GPU. {e}'''    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     Callable[[], None]    ):
      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         =					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         =					"""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         =					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
              _UpperCAmelCase         =					nvml.nvmlDeviceGetMemoryInfo(__lowerCAmelCase    )
              _UpperCAmelCase         =					meminfo.used
              _UpperCAmelCase         =					Memory(__lowerCAmelCase    )
              # 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         =					None
            else:
              _UpperCAmelCase         =					measure_peak_memory_cpu(__lowerCAmelCase    )
              _UpperCAmelCase         =					Memory(__lowerCAmelCase    ) if isinstance(__lowerCAmelCase      , __lowerCAmelCase    ) else memory_bytes
          if self.args.trace_memory_line_by_line:
            _UpperCAmelCase         =					stop_memory_tracing(__lowerCAmelCase    )
            if memory is None:
              _UpperCAmelCase         =					summary.total
          else:
            _UpperCAmelCase         =					None

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