




'''simple docstring'''






import unittest

import numpy as np

from transformers import ElectraConfig, 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():
      from transformers.models.electra.modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
      )

class 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):



 def __init__(	self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=True ,		A=True ,		A=True ,		A=99 ,		A=24 ,		A=32 ,		A=5 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=16 ,		A=2 ,		A=0.0_2 ,		A=4 ,		):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   parent
        lowercase	:		int    =   batch_size
        lowercase	:		str    =   seq_length
        lowercase	:		Optional[int]    =   is_training
        lowercase	:		Union[str, Any]    =   use_attention_mask
        lowercase	:		List[str]    =   use_token_type_ids
        lowercase	:		Tuple    =   use_labels
        lowercase	:		Optional[int]    =   vocab_size
        lowercase	:		Union[str, Any]    =   hidden_size
        lowercase	:		Union[str, Any]    =   embedding_size
        lowercase	:		Optional[int]    =   num_hidden_layers
        lowercase	:		List[Any]    =   num_attention_heads
        lowercase	:		Any    =   intermediate_size
        lowercase	:		Any    =   hidden_act
        lowercase	:		Optional[Any]    =   hidden_dropout_prob
        lowercase	:		int    =   attention_probs_dropout_prob
        lowercase	:		Optional[int]    =   max_position_embeddings
        lowercase	:		List[Any]    =   type_vocab_size
        lowercase	:		Optional[int]    =   type_sequence_label_size
        lowercase	:		Any    =   initializer_range
        lowercase	:		Dict    =   num_choices



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		int    =   ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size			)

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

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

        lowercase	:		Tuple    =   ElectraConfig(
            vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		embedding_size=self.embedding_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 ,		initializer_range=self.initializer_range ,		)

        return config, input_ids, token_type_ids, attention_mask





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Any    =   self.prepare_config_and_inputs()
        lowercase,  lowercase,  lowercase,  lowercase	:		List[str]    =   config_and_inputs
        lowercase	:		Tuple    =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict






@require_flax
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_		,  unittest.TestCase					):
 __snake_case						:  Dict       =   True

 __snake_case						:  List[Any]       =   (
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   FlaxElectraModelTester(self			)





 @slow
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                      lowercase	:		str    =   model_class_name.from_pretrained('''google/electra-small-generator'''			)
               else:
                      lowercase	:		List[str]    =   model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
               lowercase	:		int    =   model(np.ones((1, 1)			)			)
               self.assertIsNotNone(A			)










'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




