
'''simple docstring'''





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Tuple       ,					_A   :					int       ,					_A   :					List[Any]=1_3       ,					_A   :					str=7       ,					_A   :					List[Any]=True       ,					_A   :					Dict=True       ,					_A   :					Optional[Any]=True       ,					_A   :					Any=True       ,					_A   :					int=9_9       ,					_A   :					Tuple=3_2       ,					_A   :					Union[str, Any]=5       ,					_A   :					Dict=4       ,					_A   :					Tuple=3_7       ,					_A   :					Dict="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Any=0.1       ,					_A   :					str=5_1_2       ,					_A   :					str=1_6       ,					_A   :					Union[str, Any]=2       ,					_A   :					Any=0.02       ,					_A   :					Union[str, Any]=4       ,					)		->		Optional[Any]:
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      seq_length
         snake_case				  =      is_training
         snake_case				  =      use_attention_mask
         snake_case				  =      use_token_type_ids
         snake_case				  =      use_labels
         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      type_sequence_label_size
         snake_case				  =      initializer_range
         snake_case				  =      num_choices





     def 	UpperCAmelCase(self   :					Any     )		->		List[Any]:
         snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

         snake_case				  =      None
         if self.use_attention_mask:
             snake_case				  =      random_attention_mask([self.batch_size, self.seq_length]     )

         snake_case				  =      None
         if self.use_token_type_ids:
             snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size     )

         snake_case				  =      RobertaPreLayerNormConfig(
             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   :					Dict     )		->		int:
         snake_case				  =      self.prepare_config_and_inputs()
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      config_and_inputs
         snake_case				  =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict





     def 	UpperCAmelCase(self   :					List[str]     )		->		Union[str, Any]:
         snake_case				  =      self.prepare_config_and_inputs()
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      config_and_inputs

         snake_case				  =      True
         snake_case				  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
         snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					vocab_size=2     )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							List[str]     =	True

     UpperCAmelCase__       :							List[str]     =	(
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[str]:
         snake_case				  =      FlaxRobertaPreLayerNormModelTester(self     )





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		List[Any]:
         for model_class_name in self.all_model_classes:
             snake_case				  =      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,					from_pt=_A     )
             snake_case				  =      model(np.ones((1, 1)     )     )
             self.assertIsNotNone(_A     )







@require_flax
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Any:
         snake_case				  =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,					from_pt=_A     )

         snake_case				  =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
         snake_case				  =      model(_A     )[0]
         snake_case				  =      [1, 1_1, 5_0_2_6_5]
         self.assertEqual(list(output.shape     )       ,					_A     )
         # compare the actual values for a slice.
         snake_case				  =      np.array(
             [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]       ,					dtype=np.floataa     )
         self.assertTrue(np.allclose(output[:, :3, :3]       ,					_A       ,					atol=1E-4     )     )





     @slow
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		int:
         snake_case				  =      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,					from_pt=_A     )

         snake_case				  =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
         snake_case				  =      model(_A     )[0]
         # compare the actual values for a slice.
         snake_case				  =      np.array(
             [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]       ,					dtype=np.floataa     )
         self.assertTrue(np.allclose(output[:, :3, :3]       ,					_A       ,					atol=1E-4     )     )



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      PrecisionType(mixed_precision.lower()	)
    except ValueError:
        raise ValueError(
            F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"				,	A__	) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state	) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."	)
        if num_processes is None:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU."	)
        else:
            print("Launching training on one CPU."	)
        function(*A__	)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state	) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."	)

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."	)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                            "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                            "Please review your imports and test them when running the `notebook_launcher()` to identify "
                            "which one is problematic."	) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

