"""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        UpperCAmelCase		(unittest.TestCase    ):



    """simple docstring"""

    def __init__(  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase=13       ,     _UpperCAmelCase=7       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=99       ,     _UpperCAmelCase=32       ,     _UpperCAmelCase=5       ,     _UpperCAmelCase=4       ,     _UpperCAmelCase=37       ,     _UpperCAmelCase="gelu"       ,     _UpperCAmelCase=0.1       ,     _UpperCAmelCase=0.1       ,     _UpperCAmelCase=512       ,     _UpperCAmelCase=16       ,     _UpperCAmelCase=2       ,     _UpperCAmelCase=0.02       ,     _UpperCAmelCase=4       ,     ):
       lowercase__:  Tuple   = parent
       lowercase__:  Any   = batch_size
       lowercase__:  Union[str, Any]   = seq_length
       lowercase__:  Optional[Any]   = is_training
       lowercase__:  List[str]   = use_attention_mask
       lowercase__:  Dict   = use_token_type_ids
       lowercase__:  List[Any]   = use_labels
       lowercase__:  List[Any]   = vocab_size
       lowercase__:  List[str]   = hidden_size
       lowercase__:  Dict   = num_hidden_layers
       lowercase__:  List[str]   = num_attention_heads
       lowercase__:  Dict   = intermediate_size
       lowercase__:  List[Any]   = hidden_act
       lowercase__:  List[str]   = hidden_dropout_prob
       lowercase__:  Dict   = attention_probs_dropout_prob
       lowercase__:  Tuple   = max_position_embeddings
       lowercase__:  List[str]   = type_vocab_size
       lowercase__:  Union[str, Any]   = type_sequence_label_size
       lowercase__:  List[Any]   = initializer_range
       lowercase__:  List[str]   = num_choices






    def _snake_case     (  self				):
       lowercase__:  int   = ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size				)

       lowercase__:  Dict   = None
       if self.use_attention_mask:
          lowercase__:  str   = random_attention_mask([self.batch_size, self.seq_length]				)

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

       lowercase__:  Optional[int]   = 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=_UpperCAmelCase       ,     initializer_range=self.initializer_range       ,     )

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case     (  self				):
       lowercase__:  Tuple   = self.prepare_config_and_inputs()
       lowercase__:  Tuple   = config_and_inputs
       lowercase__:  Tuple   = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict






    def _snake_case     (  self				):
       lowercase__:  int   = self.prepare_config_and_inputs()
       lowercase__:  Any   = config_and_inputs

       lowercase__:  Optional[Any]   = True
       lowercase__:  str   = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
       lowercase__:  str   = 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        UpperCAmelCase		(__A       ,unittest.TestCase    ):



    """simple docstring"""

    _UpperCAmelCase      :str		       =					True

    _UpperCAmelCase      :Any		       =					(
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = FlaxRobertaPreLayerNormModelTester(self				)






    @slow
    def _snake_case     (  self				):

       for model_class_name in self.all_model_classes:
          lowercase__:  Any   = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,     from_pt=_UpperCAmelCase				)
          lowercase__:  int   = model(np.ones((1, 1)				)				)
          self.assertIsNotNone(_UpperCAmelCase				)



@require_flax
class        UpperCAmelCase		(unittest.TestCase    ):



    """simple docstring"""

    @slow
    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,     from_pt=_UpperCAmelCase				)

       lowercase__:  Any   = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,     dtype=jnp.intaa				)
       lowercase__:  List[Any]   = model(_UpperCAmelCase				)[0]
       lowercase__:  str   = [1, 11, 50265]
       self.assertEqual(list(output.shape				)       ,     _UpperCAmelCase				)
       # compare the actual values for a slice.
       lowercase__:  Optional[Any]   = np.array(
           [[[40.4880, 18.0199, -5.2_367], [-1.8_877, -4.0_885, 10.7085], [-2.2_613, -5.6_110, 7.2_665]]]       ,     dtype=np.floataa				)
       self.assertTrue(np.allclose(output[:, :3, :3]       ,     _UpperCAmelCase       ,     atol=1e-4				)				)






    @slow
    def _snake_case     (  self				):
       lowercase__:  int   = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,     from_pt=_UpperCAmelCase				)

       lowercase__:  Optional[Any]   = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,     dtype=jnp.intaa				)
       lowercase__:  str   = model(_UpperCAmelCase				)[0]
       # compare the actual values for a slice.
       lowercase__:  List[str]   = np.array(
           [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]       ,     dtype=np.floataa				)
       self.assertTrue(np.allclose(output[:, :3, :3]       ,     _UpperCAmelCase       ,     atol=1e-4				)				)








'''simple docstring'''



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 				lowercase_					(     __A			:							List[Any]      )							->   Union[str, Any]:



 """simple docstring"""




 return EnvironmentCommand()


def 				lowercase_					(     __A			:							int      )							->   Dict:



 """simple docstring"""




 return EnvironmentCommand(args.accelerate_config_file      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @staticmethod
      def 					A__   (			UpperCAmelCase      :		ArgumentParser		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[int]						=parser.add_parser('''env'''		)
       download_parser.set_defaults(func=UpperCAmelCase		)
       download_parser.add_argument(
           '''--accelerate-config_file'''  ,   default=UpperCAmelCase  ,   help='''The accelerate config file to use for the default values in the launching script.'''  ,   )
       download_parser.set_defaults(func=UpperCAmelCase		)






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   *UpperCAmelCase      :		Tuple		)	->	None:


       '''simple docstring'''

       lowercase       : List[str]						=accelerate_config_file






      def 					A__   (			self      :		Tuple		)	->	int:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''not installed'''
       if is_safetensors_available():
        import safetensors

        lowercase       : List[Any]						=safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''		) is not None:
        import safetensors

        lowercase       : List[str]						=f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

       lowercase       : List[str]						='''not installed'''
       lowercase       : str						='''not found'''
       if is_accelerate_available():
        import accelerate
        from accelerate.commands.config import default_config_file, load_config_from_file

        lowercase       : List[str]						=accelerate.__version__
        # Get the default from the config file.
        if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase		):
         lowercase       : Dict						=load_config_from_file(self._accelerate_config_file		).to_dict()

        lowercase       : Optional[int]						=(
            '''\n'''.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
            if isinstance(UpperCAmelCase  ,   UpperCAmelCase		)
            else f'\t{accelerate_config}'
        )

       lowercase       : Optional[int]						='''not installed'''
       lowercase       : Optional[Any]						='''NA'''
       if is_torch_available():
        import torch

        lowercase       : List[Any]						=torch.__version__
        lowercase       : Dict						=torch.cuda.is_available()

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_tf_available():
        import tensorflow as tf

        lowercase       : List[Any]						=tf.__version__
        try:
         # deprecated in v2.1
         lowercase       : Optional[Any]						=tf.test.is_gpu_available()
        except AttributeError:
         # returns list of devices, convert to bool
         lowercase       : Optional[int]						=bool(tf.config.list_physical_devices('''GPU'''		)		)

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_flax_available():
        import flax
        import jax
        import jaxlib

        lowercase       : Dict						=flax.__version__
        lowercase       : List[Any]						=jax.__version__
        lowercase       : Any						=jaxlib.__version__
        lowercase       : Tuple						=jax.lib.xla_bridge.get_backend().platform

       lowercase       : str						={
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'{safetensors_version}',
           '''Accelerate version''': f'{accelerate_version}',
           '''Accelerate config''': f'{accelerate_config_str}',
           '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
           '''Tensorflow version (GPU?)''': f'{tf_version} ({tf_cuda_available})',
           '''Flax version (CPU?/GPU?/TPU?)''': f'{flax_version} ({jax_backend})',
           '''Jax version''': f'{jax_version}',
           '''JaxLib version''': f'{jaxlib_version}',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''		)
       print(self.format_dict(UpperCAmelCase		)		)

       return info






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Any		)	->	Any:


       '''simple docstring'''

       return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"



