
'''simple docstring'''






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

UpperCAmelCase_       :   Tuple   =       get_logger()

UpperCAmelCase_       :   Optional[dict]   =       None

class  UpperCAmelCase__						(       TensorFormatter[Mapping, 'jax.Array', Mapping] ):



    def __init__(	self      :       Tuple,__A      :       Optional[Any]=None,__A      :       Optional[Any]=None,**__A      :       Optional[int]     ):
          super().__init__(features=__A     )
          import jax
          from jaxlib.xla_client import Device

          if isinstance(__A,__A     ):
                raise ValueError(
                    f'Expected {device} to be a `str` not {type(__A     )}, as `jaxlib.xla_extension.Device` '
                    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                    "the device with `str()` to get its string identifier that will be internally mapped "
                    "to the actual `jaxlib.xla_extension.Device`."     )
          _lowerCamelCase				:   List[str]								=   device if isinstance(__A,__A     ) else str(jax.devices()[0]     )
          # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
          # with `pickle` nor with `dill`, so we need to use a global variable instead
          global DEVICE_MAPPING
          if DEVICE_MAPPING is None:
                _lowerCamelCase				:   Tuple								=   self._map_devices_to_str()
          if self.device not in list(DEVICE_MAPPING.keys()     ):
                logger.warning(
                    f'Device with string identifier {self.device} not listed among the available '
                    f'devices: {list(DEVICE_MAPPING.keys()     )}, so falling back to the default '
                    f'device: {str(jax.devices()[0]     )}.'     )
                _lowerCamelCase				:   Optional[int]								=   str(jax.devices()[0]     )
          _lowerCamelCase				:   Tuple								=   jnp_array_kwargs



    @staticmethod
    def   lowerCamelCase_  (	):
          import jax

          return {str(__A     ): device for device in jax.devices()}



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       str     ):
          import jax
          import jax.numpy as jnp

          if isinstance(__A,__A     ) and column:
                if all(
                    isinstance(__A,jax.Array     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                      return jnp.stack(__A,axis=0     )
          return column



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Dict     ):
          import jax
          import jax.numpy as jnp

          if isinstance(__A,(str, bytes, type(__A     ))     ):
                return value
          elif isinstance(__A,(np.character, np.ndarray)     ) and np.issubdtype(value.dtype,np.character     ):
                return value.tolist()

          _lowerCamelCase				:   List[Any]								=   {}

          if isinstance(__A,(np.number, np.ndarray)     ) and np.issubdtype(value.dtype,np.integer     ):
                # the default int precision depends on the jax config
                # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
                if jax.config.jax_enable_xaa:
                      _lowerCamelCase				:   Union[str, Any]								=   {"dtype": jnp.intaa}
                else:
                      _lowerCamelCase				:   Optional[Any]								=   {"dtype": jnp.intaa}
          elif isinstance(__A,(np.number, np.ndarray)     ) and np.issubdtype(value.dtype,np.floating     ):
                _lowerCamelCase				:   List[Any]								=   {"dtype": jnp.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                import PIL.Image

                if isinstance(__A,PIL.Image.Image     ):
                      _lowerCamelCase				:   int								=   np.asarray(__A     )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
          global DEVICE_MAPPING
          if DEVICE_MAPPING is None:
                _lowerCamelCase				:   str								=   self._map_devices_to_str()

          with jax.default_device(DEVICE_MAPPING[self.device]     ):
                # calling jnp.array on a np.ndarray does copy the data
                # see https://github.com/google/jax/issues/4486
                return jnp.array(__A,**{**default_dtype, **self.jnp_array_kwargs}     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Optional[int]     ):
          import jax

          # support for torch, tf, jax etc.
          if config.TORCH_AVAILABLE and "torch" in sys.modules:
                import torch

                if isinstance(__A,torch.Tensor     ):
                      return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
          if hasattr(__A,"__array__"     ) and not isinstance(__A,jax.Array     ):
                _lowerCamelCase				:   Optional[int]								=   data_struct.__array__()
          # support for nested types like struct of list of struct
          if isinstance(__A,np.ndarray     ):
                if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
          elif isinstance(__A,(list, tuple)     ):
                return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
          return self._tensorize(__A     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       dict     ):
          return map_nested(self._recursive_tensorize,__A,map_list=__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       pa.Table     ):
          _lowerCamelCase				:   Dict								=   self.numpy_arrow_extractor().extract_row(__A     )
          _lowerCamelCase				:   str								=   self.python_features_decoder.decode_row(__A     )
          return self.recursive_tensorize(__A     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       pa.Table     ):
          _lowerCamelCase				:   Optional[int]								=   self.numpy_arrow_extractor().extract_column(__A     )
          _lowerCamelCase				:   Dict								=   self.python_features_decoder.decode_column(__A,pa_table.column_names[0]     )
          _lowerCamelCase				:   Optional[Any]								=   self.recursive_tensorize(__A     )
          _lowerCamelCase				:   Tuple								=   self._consolidate(__A     )
          return column



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       pa.Table     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.numpy_arrow_extractor().extract_batch(__A     )
          _lowerCamelCase				:   Dict								=   self.python_features_decoder.decode_batch(__A     )
          _lowerCamelCase				:   int								=   self.recursive_tensorize(__A     )
          for column_name in batch:
                _lowerCamelCase				:   Optional[int]								=   self._consolidate(batch[column_name]     )
          return batch

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, 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():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



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



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              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   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   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
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )