



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax







@require_flax
class 							_a							:

   def     __snake_case					(self				)					->      str:
      UpperCAmelCase_:		Optional[Any]       			=    self.prepare_init_args_and_inputs_for_common()

      UpperCAmelCase_:		Any       			=    self.model_class(**SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		Optional[Any]       			=    model.init(inputs_dict["""prng_key"""],      inputs_dict["""sample"""]				)
      jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_				)

      UpperCAmelCase_:		List[str]       			=    model.apply(SCREAMING_SNAKE_CASE_,      inputs_dict["""sample"""]				)

      if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				):
         UpperCAmelCase_:		Any       			=    output.sample

      self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		Any       			=    inputs_dict["sample"].shape
      self.assertEqual(output.shape,      SCREAMING_SNAKE_CASE_,      """Input and output shapes do not match"""				)

   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		List[Any]       			=    self.prepare_init_args_and_inputs_for_common()

      UpperCAmelCase_:		List[str]       			=    16
      UpperCAmelCase_:		Optional[int]       			=    (16, 32)

      UpperCAmelCase_:		Optional[Any]       			=    self.model_class(**SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		Tuple       			=    model.init(inputs_dict["""prng_key"""],      inputs_dict["""sample"""]				)
      jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_				)

      UpperCAmelCase_:		Any       			=    model.apply(SCREAMING_SNAKE_CASE_,      inputs_dict["""sample"""]				)

      if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				):
         UpperCAmelCase_:		List[Any]       			=    output.sample

      self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		str       			=    inputs_dict["sample"].shape
      self.assertEqual(output.shape,      SCREAMING_SNAKE_CASE_,      """Input and output shapes do not match"""				)

   def     __snake_case					(self				)					->      Optional[int]:
      UpperCAmelCase_:		Tuple       			=    "kwargs" in inspect.signature(self.model_class.__init__				).parameters
      UpperCAmelCase_:		Optional[int]       			=    len(self.model_class._deprecated_kwargs				) > 0

      if has_kwarg_in_model_class and not has_deprecated_kwarg:
         raise ValueError(
             f'{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'
             """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
             """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
             """ [<deprecated_argument>]`"""				)

      if not has_kwarg_in_model_class and has_deprecated_kwarg:
         raise ValueError(
             f'{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'
             """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
             f' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'
             """ from `_deprecated_kwargs = [<deprecated_argument>]`"""				)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






