






from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=2		,				_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=10		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=0.6		,				_SCREAMING_SNAKE_CASE=None		,				) ->				Dict:
								snake_case_				:							Optional[Any]						 =       parent
								snake_case_				:							List[Any]						 =       batch_size
								snake_case_				:							Tuple						 =       image_size
								snake_case_				:							List[Any]						 =       patch_size
								snake_case_				:							Dict						 =       num_channels
								snake_case_				:							Optional[Any]						 =       is_training
								snake_case_				:							Tuple						 =       use_labels
								snake_case_				:							Union[str, Any]						 =       hidden_size
								snake_case_				:							Any						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       num_attention_heads
								snake_case_				:							List[Any]						 =       intermediate_size
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Tuple						 =       hidden_dropout_prob
								snake_case_				:							Dict						 =       attention_probs_dropout_prob
								snake_case_				:							Dict						 =       type_sequence_label_size
								snake_case_				:							str						 =       initializer_range
								snake_case_				:							Dict						 =       mask_ratio
								snake_case_				:							List[str]						 =       scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								snake_case_				:							Optional[Any]						 =       (image_size // patch_size) ** 2
								snake_case_				:							Union[str, Any]						 =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								snake_case_				:							Any						 =       None
								if self.use_labels:
										snake_case_				:							List[Any]						 =       ids_tensor([self.batch_size]		,				self.type_sequence_label_size				)

								snake_case_				:							Tuple						 =       self.get_config()

								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								return ViTMAEConfig(
								    image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				decoder_hidden_size=self.hidden_size		,				decoder_num_hidden_layers=self.num_hidden_layers		,				decoder_num_attention_heads=self.num_attention_heads		,				decoder_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		,				is_decoder=_SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				mask_ratio=self.mask_ratio		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Optional[int]						 =       TFViTMAEModel(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Dict						 =       TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       model(_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								# expected sequence length = num_patches
								snake_case_				:							int						 =       (self.image_size // self.patch_size) ** 2
								snake_case_				:							Optional[Any]						 =       self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)				)

								# test greyscale images
								snake_case_				:							Optional[Any]						 =       1
								snake_case_				:							Union[str, Any]						 =       TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								snake_case_				:							Optional[int]						 =       model(_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       self.patch_size**2
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							Tuple						 =       self.prepare_config_and_inputs()
								((snake_case_) ,       (snake_case_) ,       (snake_case_))				:							Dict						 =       config_and_inputs
								snake_case_				:							str						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   str						=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
						A     :   List[Any]						=					{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

						A     :   Dict						=					False
						A     :   Union[str, Any]						=					False
						A     :   Tuple						=					False
						A     :   Optional[int]						=					False






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							Optional[int]						 =       TFViTMAEModelTester(self				)
								snake_case_				:							List[str]						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE		,				hidden_size=37				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								self.config_tester.run_common_tests()






						@unittest.skip(reason="ViTMAE does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								pass






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_ ,       snake_case_				:							Optional[int]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							str						 =       model_class(_SCREAMING_SNAKE_CASE				)
										self.assertIsInstance(model.get_input_embeddings()		,				(tf.keras.layers.Layer)				)
										snake_case_				:							str						 =       model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE		,				tf.keras.layers.Layer				)				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_ ,       snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							Optional[int]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							int						 =       inspect.signature(model.call				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case_				:							Dict						 =       [*signature.parameters.keys()]

										snake_case_				:							List[Any]						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							str						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								# make the mask reproducible
								np.random.seed(2				)

								snake_case_ ,       snake_case_				:							Any						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							str						 =       int((config.image_size // config.patch_size) ** 2				)
								snake_case_				:							int						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
										snake_case_				:							Any						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							str						 =       model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Optional[Any]						 =       copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)
										snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[int]						 =       outputs_dict[0].numpy()
										snake_case_				:							Any						 =       outputs_keywords[0].numpy()

										self.assertLess(np.sum(np.abs(output_dict - output_keywords				)				)		,				1e-6				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								# make the mask reproducible
								np.random.seed(2				)

								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							Tuple						 =       int((config.image_size // config.patch_size) ** 2				)
								snake_case_				:							str						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								def prepare_numpy_arrays(_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Union[str, Any]						 =       {}
										for k, v in inputs_dict.items():
												if tf.is_tensor(_SCREAMING_SNAKE_CASE				):
														snake_case_				:							int						 =       v.numpy()
												else:
														snake_case_				:							Tuple						 =       np.array(_SCREAMING_SNAKE_CASE				)

										return inputs_np_dict

								for model_class in self.all_model_classes:
										snake_case_				:							Union[str, Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       prepare_numpy_arrays(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Optional[int]						 =       model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)
										self.assert_outputs_same(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								# make masks reproducible
								np.random.seed(2				)

								snake_case_				:							Any						 =       int((tf_model.config.image_size // tf_model.config.patch_size) ** 2				)
								snake_case_				:							Union[str, Any]						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
								snake_case_				:							Union[str, Any]						 =       tf.constant(_SCREAMING_SNAKE_CASE				)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								snake_case_				:							Tuple						 =       tf_noise

								super().check_pt_tf_models(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				int:
								# make mask reproducible
								np.random.seed(2				)

								snake_case_ ,       snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							Union[str, Any]						 =       {
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__				),)
								    for module_member_name in dir(_SCREAMING_SNAKE_CASE				)
								    if module_member_name.endswith("MainLayer"				)
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len("MainLayer"				)] == model_class.__name__[: -len("Model"				)]
								    for module_member in (getattr(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				),)
								    if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(_SCREAMING_SNAKE_CASE		,				"_keras_serializable"		,				_SCREAMING_SNAKE_CASE				)
								}

								snake_case_				:							Tuple						 =       int((config.image_size // config.patch_size) ** 2				)
								snake_case_				:							str						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
								snake_case_				:							Dict						 =       tf.convert_to_tensor(_SCREAMING_SNAKE_CASE				)
								inputs_dict.update({"noise": noise}				)

								for main_layer_class in tf_main_layer_classes:
										snake_case_				:							Tuple						 =       main_layer_class(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							List[Any]						 =       {
										    name: tf.keras.Input(tensor.shape[1:]		,				dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
										}

										snake_case_				:							Optional[int]						 =       tf.keras.Model(_SCREAMING_SNAKE_CASE		,				outputs=main_layer(_SCREAMING_SNAKE_CASE				)				)
										snake_case_				:							Tuple						 =       model(_SCREAMING_SNAKE_CASE				)

										with tempfile.TemporaryDirectory() as tmpdirname:
												snake_case_				:							Dict						 =       os.path.join(_SCREAMING_SNAKE_CASE		,				"keras_model.h5"				)
												model.save(_SCREAMING_SNAKE_CASE				)
												snake_case_				:							Dict						 =       tf.keras.models.load_model(
												    _SCREAMING_SNAKE_CASE		,				custom_objects={main_layer_class.__name__: main_layer_class}				)
												assert isinstance(_SCREAMING_SNAKE_CASE		,				tf.keras.Model				)
												snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE				)
												self.assert_outputs_same(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								# make mask reproducible
								np.random.seed(2				)

								snake_case_ ,       snake_case_				:							int						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							List[Any]						 =       int((config.image_size // config.patch_size) ** 2				)
								snake_case_				:							List[str]						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
										snake_case_				:							Union[str, Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							str						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)

										if model_class.__name__ == "TFViTMAEModel":
												snake_case_				:							Union[str, Any]						 =       outputs.last_hidden_state.numpy()
												snake_case_				:							Optional[Any]						 =       0
										else:
												snake_case_				:							Tuple						 =       outputs.logits.numpy()
												snake_case_				:							Union[str, Any]						 =       0

										with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(_SCREAMING_SNAKE_CASE		,				saved_model=_SCREAMING_SNAKE_CASE				)
												snake_case_				:							Optional[int]						 =       model_class.from_pretrained(_SCREAMING_SNAKE_CASE				)
												snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)

												if model_class.__name__ == "TFViTMAEModel":
														snake_case_				:							str						 =       after_outputs["last_hidden_state"].numpy()
														snake_case_				:							Optional[int]						 =       0
												else:
														snake_case_				:							str						 =       after_outputs["logits"].numpy()
														snake_case_				:							Optional[int]						 =       0

												snake_case_				:							List[str]						 =       np.amax(np.abs(out_a - out_a				)				)
												self.assertLessEqual(_SCREAMING_SNAKE_CASE		,				1e-5				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								# make mask reproducible
								np.random.seed(2				)

								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							Optional[int]						 =       int((config.image_size // config.patch_size) ** 2				)
								snake_case_				:							List[str]						 =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
										snake_case_				:							Dict						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[str]						 =       model.get_config()
										# make sure that returned config is jsonifiable, which is required by keras
										json.dumps(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model_class.from_config(model.get_config()				)
										# make sure it also accepts a normal config
										snake_case_				:							Tuple						 =       model_class.from_config(model.config				)
										snake_case_				:							Optional[int]						 =       new_model(_SCREAMING_SNAKE_CASE				)  # Build model
										new_model.set_weights(model.get_weights()				)
										snake_case_				:							List[str]						 =       new_model(_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)

										self.assert_outputs_same(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."				)
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								pass






						@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"				)
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								pass






						@slow
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Any						 =       TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"				)
								self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							Any						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




@require_tf
@require_vision
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"				) if is_vision_available() else None






						@slow
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# make random mask reproducible across the PT and TF model
								np.random.seed(2				)

								snake_case_				:							List[Any]						 =       TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"				)

								snake_case_				:							Union[str, Any]						 =       self.default_image_processor
								snake_case_				:							List[str]						 =       prepare_img()
								snake_case_				:							Dict						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="tf"				)

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								snake_case_				:							Optional[Any]						 =       ViTMAEConfig()
								snake_case_				:							Optional[int]						 =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
								snake_case_				:							str						 =       np.random.uniform(size=(1, num_patches)				)

								# forward pass
								snake_case_				:							Any						 =       model(**_SCREAMING_SNAKE_CASE		,				noise=_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							Union[str, Any]						 =       tf.convert_to_tensor([1, 196, 768]				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Union[str, Any]						 =       tf.convert_to_tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]				)

								tf.debugging.assert_near(outputs.logits[0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-4				)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
