



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

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

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

          UpperCamelCase   :  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=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

          return config, input_ids, token_type_ids, attention_mask
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     config_and_inputs
          UpperCamelCase   :  Union[str, Any]						     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     config_and_inputs

          UpperCamelCase   :  Dict						     =     True
          UpperCamelCase   :  List[Any]						     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
          UpperCamelCase   :  List[Any]						     =     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__    : Union[str, Any]       					=  True

       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     FlaxRobertaPreLayerNormModelTester(self	)






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






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Union[str, Any]						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          UpperCamelCase   :  List[Any]						     =     [1, 11, 5_0265]
          self.assertEqual(list(output.shape	),      SCREAMING_SNAKE_CASE_	)
          # compare the actual values for a slice.
          UpperCamelCase   :  Optional[int]						     =     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],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)






       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Dict						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          # compare the actual values for a slice.
          UpperCamelCase   :  Any						     =     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],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)








import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]=False     )     ->					Optional[Any]:
   try:
      UpperCamelCase   :  List[Any]						     =     os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      UpperCamelCase   :  List[Any]						     =     default
   else:
      # KEY is set, convert it to True or False.
      try:
         UpperCamelCase   :  Optional[Any]						     =     strtobool(snake_case__     )
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F"""If set, {key} must be yes or no."""     )
   return _value


__UpperCAmelCase	       =	parse_flag_from_env('''RUN_SLOW''', default=False)
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					str:
   return unittest.skip('Test was skipped'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(_run_slow_tests	,					'test is slow'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(not torch.cuda.is_available()	,					'test requires only a CPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(torch.cuda.is_available()	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					List[Any]:
   return unittest.skipUnless(is_xpu_available()	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_mps_available()	,					'test requires a `mps` backend support in `torch`'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					Tuple:
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()	,					'test requires the Hugging Face suite'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_bnb_available()	,					'test requires the bitsandbytes library'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   return unittest.skipUnless(is_tpu_available()	,					'test requires TPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[int]:
   return unittest.skipUnless(torch.cuda.device_count() == 1	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(torch.xpu.device_count() == 1	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Any:
   return unittest.skipUnless(torch.cuda.device_count() > 1	,					'test requires multiple GPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   return unittest.skipUnless(torch.xpu.device_count() > 1	,					'test requires multiple XPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Any:
   return unittest.skipUnless(is_safetensors_available()	,					'test requires safetensors'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(is_deepspeed_available()	,					'test requires DeepSpeed'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_torch_version('>='	,					'1.12.0'     )	,					'test requires torch version >= 1.12.0'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict=None	,					snake_case__	:   Union[str, Any]=None     )     ->					Optional[Any]:
   if test_case is None:
      return partial(snake_case__	,					version=snake_case__     )
   return unittest.skipUnless(is_torch_version('>='	,					snake_case__     )	,					F"""test requires torch version >= {version}"""     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_tensorboard_available()	,					'test requires Tensorboard'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					Tuple:
   return unittest.skipUnless(is_wandb_available()	,					'test requires wandb'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					List[Any]:
   return unittest.skipUnless(is_comet_ml_available()	,					'test requires comet_ml'     )(snake_case__     )


__UpperCAmelCase	       =	(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(
       _atleast_one_tracker_available	,					'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,					)(snake_case__     )



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  True
       @classmethod
       def  snake_case_	(      cls	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     tempfile.mkdtemp()
       @classmethod
       def  snake_case_	(      cls	)					->		Tuple:
          if os.path.exists(cls.tmpdir	):
             shutil.rmtree(cls.tmpdir	)






       def  snake_case_	(      self	)					->		str:
          if self.clear_on_setup:
             for path in Path(self.tmpdir	).glob('**/*'	):
                if path.is_file():
                   path.unlink()
                elif path.is_dir():
                   shutil.rmtree(SCREAMING_SNAKE_CASE_	)






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		Optional[int]:
          super().tearDown()
          # Reset the state of the AcceleratorState singleton.
          AcceleratorState._reset_state()
          PartialState._reset_state()






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     mocks if isinstance(SCREAMING_SNAKE_CASE_,      (tuple, list)	) else [mocks]
          for m in self.mocks:
             m.start()
             self.addCleanup(m.stop	)






def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Optional[int]:
   UpperCamelCase   :  Tuple						     =     AcceleratorState()
   UpperCamelCase   :  Tuple						     =     tensor[None].clone().to(state.device     )
   UpperCamelCase   :  str						     =     gather(snake_case__     ).cpu()
   UpperCamelCase   :  Union[str, Any]						     =     tensor[0].cpu()
   for i in range(tensors.shape[0]     ):
      if not torch.equal(tensors[i]	,					snake_case__     ):
         return False
   return True



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  List[Any]						     =     returncode
          UpperCamelCase   :  Tuple						     =     stdout
          UpperCamelCase   :  Any						     =     stderr






async def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Union[str, Any]:
   while True:
      UpperCamelCase   :  List[str]						     =     await stream.readline()
      if line:
         callback(snake_case__     )
      else:
         break
async def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   int=None	,					snake_case__	:   Dict=None	,					snake_case__	:   Any=None	,					snake_case__	:   Optional[Any]=False	,					snake_case__	:   Union[str, Any]=False     )     ->					_RunOutput:
   if echo:
      print('\nRunning: '	,					' '.join(snake_case__     )     )

   UpperCamelCase   :  Dict						     =     await asyncio.create_subprocess_exec(
       cmd[0]	,					*cmd[1:]	,					stdin=snake_case__	,					stdout=asyncio.subprocess.PIPE	,					stderr=asyncio.subprocess.PIPE	,					env=snake_case__	,					)

   # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
   # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
   #
   # If it starts hanging, will need to switch to the following code. The problem is that no data
   # will be seen until it's done and if it hangs for example there will be no debug info.
   # out, err = await p.communicate()
   # return _RunOutput(p.returncode, out, err)

   UpperCamelCase   :  Tuple						     =     []
   UpperCamelCase   :  int						     =     []

   def tee(snake_case__	:   List[Any]	,					snake_case__	:   Optional[int]	,					snake_case__	:   Optional[int]	,					snake_case__	:   List[Any]=""     ):
      UpperCamelCase   :  Union[str, Any]						     =     line.decode('utf-8'     ).rstrip()
      sink.append(snake_case__     )
      if not quiet:
         print(snake_case__	,					snake_case__	,					file=snake_case__     )

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stdout	,					label='stdout:'     )     )     ),
           asyncio.create_task(_read_stream(p.stderr	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stderr	,					label='stderr:'     )     )     ),
       ]	,					timeout=snake_case__	,					)
   return _RunOutput(await p.wait()	,					snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any=None	,					snake_case__	:   Tuple=None	,					snake_case__	:   Any=180	,					snake_case__	:   Any=False	,					snake_case__	:   Optional[int]=True     )     ->					_RunOutput:
   UpperCamelCase   :  int						     =     asyncio.get_event_loop()
   UpperCamelCase   :  Tuple						     =     loop.run_until_complete(
       _stream_subprocess(snake_case__	,					env=snake_case__	,					stdin=snake_case__	,					timeout=snake_case__	,					quiet=snake_case__	,					echo=snake_case__     )     )

   UpperCamelCase   :  str						     =     ' '.join(snake_case__     )
   if result.returncode > 0:
      UpperCamelCase   :  Union[str, Any]						     =     '\n'.join(result.stderr     )
      raise RuntimeError(
          F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
          F"""The combined stderr from workers follows:\n{stderr}"""     )

   return result



class     lowerCAmelCase_		(  a__							):
       pass






def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   str=False     )     ->					int:
   try:
      UpperCamelCase   :  Union[str, Any]						     =     subprocess.check_output(snake_case__	,					stderr=subprocess.STDOUT     )
      if return_stdout:
         if hasattr(snake_case__	,					'decode'     ):
            UpperCamelCase   :  Optional[int]						     =     output.decode('utf-8'     )
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          F"""Command `{" ".join(snake_case__     )}` failed with the following error:\n\n{e.output.decode()}"""     ) from e



