





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image

class 		UpperCamelCase__			:



					'''simple docstring'''

					def __init__(							self						:   str						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=2						,lowerCamelCase__						:   Tuple=True						,lowerCamelCase__						:   Dict=False						,lowerCamelCase__						:   List[Any]=10						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   Union[str, Any]=32 * 8						,lowerCamelCase__						:   Optional[Any]=32 * 8						,lowerCamelCase__						:   Any=4						,lowerCamelCase__						:   str=64						,)					->		Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						parent
											SCREAMING_SNAKE_CASE		=						batch_size
											SCREAMING_SNAKE_CASE		=						is_training
											SCREAMING_SNAKE_CASE		=						use_auxiliary_loss
											SCREAMING_SNAKE_CASE		=						num_queries
											SCREAMING_SNAKE_CASE		=						num_channels
											SCREAMING_SNAKE_CASE		=						min_size
											SCREAMING_SNAKE_CASE		=						max_size
											SCREAMING_SNAKE_CASE		=						num_labels
											SCREAMING_SNAKE_CASE		=						hidden_dim
											SCREAMING_SNAKE_CASE		=						hidden_dim






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Union[str, Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
											    lowerCamelCase__	)

											SCREAMING_SNAKE_CASE		=						torch.ones([self.batch_size, self.min_size, self.max_size]						,device=lowerCamelCase__	)

											SCREAMING_SNAKE_CASE		=						(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,device=lowerCamelCase__	) > 0.5
											).float()
											SCREAMING_SNAKE_CASE		=						(torch.rand((self.batch_size, self.num_labels)						,device=lowerCamelCase__	) > 0.5).long()

											SCREAMING_SNAKE_CASE		=						self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerConfig(
											    hidden_size=self.hidden_dim						,)
											SCREAMING_SNAKE_CASE		=						self.num_queries
											SCREAMING_SNAKE_CASE		=						self.num_labels

											SCREAMING_SNAKE_CASE		=						[1, 1, 1, 1]
											SCREAMING_SNAKE_CASE		=						self.num_channels

											SCREAMING_SNAKE_CASE		=						64
											SCREAMING_SNAKE_CASE		=						128
											SCREAMING_SNAKE_CASE		=						self.hidden_dim
											SCREAMING_SNAKE_CASE		=						self.hidden_dim
											SCREAMING_SNAKE_CASE		=						self.hidden_dim
											return config






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Any:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
											return config, inputs_dict






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   int	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						output.encoder_hidden_states
											SCREAMING_SNAKE_CASE		=						output.pixel_decoder_hidden_states
											SCREAMING_SNAKE_CASE		=						output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(lowerCamelCase__	)						,len(config.backbone_config.depths	)	)
											self.parent.assertTrue(len(lowerCamelCase__	)						,len(config.backbone_config.depths	)	)
											self.parent.assertTrue(len(lowerCamelCase__	)						,config.decoder_layers	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Optional[int]=False	)					->		Dict:




											'''simple docstring'''


											with torch.no_grad():
																	SCREAMING_SNAKE_CASE		=						MaskaFormerModel(config=lowerCamelCase__	)
																	model.to(lowerCamelCase__	)
																	model.eval()

																	SCREAMING_SNAKE_CASE		=						model(pixel_values=lowerCamelCase__						,pixel_mask=lowerCamelCase__	)
																	SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,output_hidden_states=lowerCamelCase__	)

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape						,(self.batch_size, self.num_queries, self.hidden_dim)						,)
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None	)
											self.parent.assertTrue(output.encoder_last_hidden_state is not None	)

											if output_hidden_states:
																	self.check_output_hidden_state(lowerCamelCase__						,lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerForUniversalSegmentation(config=lowerCamelCase__	)
											model.to(lowerCamelCase__	)
											model.eval()

											def comm_check_on_output(lowerCamelCase__						:   List[Any]	):
																	# let's still check that all the required stuff is there
																	self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None	)
																	self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None	)
																	self.parent.assertTrue(result.encoder_last_hidden_state is not None	)
																	# okay, now we need to check the logits shape
																	# due to the encoder compression, masks have a //4 spatial size
																	self.parent.assertEqual(
																	    result.masks_queries_logits.shape						,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,)
																	# + 1 for null class
																	self.parent.assertEqual(
																	    result.class_queries_logits.shape						,(self.batch_size, self.num_queries, self.num_labels + 1)	)

											with torch.no_grad():
																	SCREAMING_SNAKE_CASE		=						model(pixel_values=lowerCamelCase__						,pixel_mask=lowerCamelCase__	)
																	SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)

																	comm_check_on_output(lowerCamelCase__	)

																	SCREAMING_SNAKE_CASE		=						model(
																	    pixel_values=lowerCamelCase__						,pixel_mask=lowerCamelCase__						,mask_labels=lowerCamelCase__						,class_labels=lowerCamelCase__	)

											comm_check_on_output(lowerCamelCase__	)

											self.parent.assertTrue(result.loss is not None	)
											self.parent.assertEqual(result.loss.shape						,torch.Size([1]	)	)

@require_torch
class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_							,				unittest.TestCase       ):



					'''simple docstring'''

					__snake_case   :     int		     =      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					__snake_case   :     Tuple		     =      {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

					__snake_case   :     int		     =      False
					__snake_case   :     List[Any]		     =      False
					__snake_case   :     List[Any]		     =      False
					__snake_case   :     Any		     =      False






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerModelTester(self	)
											SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[str]:




											'''simple docstring'''


											self.config_tester.run_common_tests()






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__						,**lowerCamelCase__						,output_hidden_states=lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__	)






					@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[Any]:




											'''simple docstring'''


											pass






					@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Dict:




											'''simple docstring'''


											pass






					@unittest.skip(reason="""Mask2Former is not a generative model"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Tuple:




											'''simple docstring'''


											pass






					@unittest.skip(reason="""Mask2Former does not use token embeddings"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		int:




											'''simple docstring'''


											pass






					@require_torch_multi_gpu
					@unittest.skip(
					    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[Any]:




											'''simple docstring'''


											pass






					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




											'''simple docstring'''


											pass






					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)
																	SCREAMING_SNAKE_CASE		=						inspect.signature(model.forward	)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE		=						[*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






					@slow
					def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Union[str, Any]:




											'''simple docstring'''


											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																	SCREAMING_SNAKE_CASE		=						MaskaFormerModel.from_pretrained(lowerCamelCase__	)
																	self.assertIsNotNone(lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						(self.model_tester.min_size,) * 2
											SCREAMING_SNAKE_CASE		=						{
											    """pixel_values""": torch.randn((2, 3, *size)						,device=lowerCamelCase__	),
											    """mask_labels""": torch.randn((2, 10, *size)						,device=lowerCamelCase__	),
											    """class_labels""": torch.zeros(2						,10						,device=lowerCamelCase__	).long(),
											}
											SCREAMING_SNAKE_CASE		=						self.model_tester.get_config()

											SCREAMING_SNAKE_CASE		=						MaskaFormerForUniversalSegmentation(lowerCamelCase__	).to(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)
											self.assertTrue(outputs.loss is not None	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Union[str, Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__						,**lowerCamelCase__						,output_hidden_states=lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	).to(lowerCamelCase__	)
																	SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__						,output_attentions=lowerCamelCase__	)
																	self.assertTrue(outputs.attentions is not None	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Dict:




											'''simple docstring'''


											if not self.model_tester.is_training:
																	return

											SCREAMING_SNAKE_CASE		=						self.all_model_classes[1]
											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()

											SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)
											model.to(lowerCamelCase__	)
											model.train()

											SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,mask_labels=lowerCamelCase__						,class_labels=lowerCamelCase__	).loss
											loss.backward()






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.all_model_classes[1]
											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE		=						True
											SCREAMING_SNAKE_CASE		=						True

											SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	).to(lowerCamelCase__	)
											model.train()

											SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,mask_labels=lowerCamelCase__						,class_labels=lowerCamelCase__	)

											SCREAMING_SNAKE_CASE		=						outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE		=						outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE		=						outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE		=						outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=lowerCamelCase__	)

											self.assertIsNotNone(encoder_hidden_states.grad	)
											self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
											self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
											self.assertIsNotNone(attentions.grad	)
SCREAMING_SNAKE_CASE_						       =	1e-4



def        __lowercase       (				)			->       int:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
						return image



@require_vision
@slow
class 		UpperCamelCase__			(				unittest.TestCase       ):



					'''simple docstring'''

					@cached_property
					def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




											'''simple docstring'''


											return "facebook/mask2former-swin-small-coco-instance"






					@cached_property
					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[Any]:




											'''simple docstring'''


											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None






					def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						self.default_image_processor
											SCREAMING_SNAKE_CASE		=						prepare_img()
											SCREAMING_SNAKE_CASE		=						image_processor(lowerCamelCase__						,return_tensors="""pt"""	).to(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						inputs["""pixel_values"""].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
											# check size
											self.assertEqual(lowerCamelCase__						,(1, 3, 384, 384)	)

											with torch.no_grad():
																	SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]	).to(lowerCamelCase__	)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,lowerCamelCase__						,atol=lowerCamelCase__	)	)

											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]	).to(lowerCamelCase__	)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,lowerCamelCase__						,atol=lowerCamelCase__	)	)

											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]	).to(lowerCamelCase__	)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,lowerCamelCase__						,atol=lowerCamelCase__	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCamelCase__	).eval()
											SCREAMING_SNAKE_CASE		=						self.default_image_processor
											SCREAMING_SNAKE_CASE		=						prepare_img()
											SCREAMING_SNAKE_CASE		=						image_processor(lowerCamelCase__						,return_tensors="""pt"""	).to(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						inputs["""pixel_values"""].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
											# check size
											self.assertEqual(lowerCamelCase__						,(1, 3, 384, 384)	)

											with torch.no_grad():
																	SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)
											# masks_queries_logits
											SCREAMING_SNAKE_CASE		=						outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape						,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
											SCREAMING_SNAKE_CASE		=						[
											    [-8.7839, -9.0056, -8.8121],
											    [-7.4104, -7.0313, -6.5401],
											    [-6.6105, -6.3427, -6.4675],
											]
											SCREAMING_SNAKE_CASE		=						torch.tensor(lowerCamelCase__	).to(lowerCamelCase__	)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,lowerCamelCase__						,atol=lowerCamelCase__	)	)
											# class_queries_logits
											SCREAMING_SNAKE_CASE		=						outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape						,(1, model.config.num_queries, model.config.num_labels + 1)	)
											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [
											        [1.8324, -8.0835, -4.1922],
											        [0.8450, -9.0050, -3.6053],
											        [0.3045, -7.7293, -3.0275],
											    ]	).to(lowerCamelCase__	)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,lowerCamelCase__						,atol=lowerCamelCase__	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCamelCase__	).eval()
											SCREAMING_SNAKE_CASE		=						self.default_image_processor

											SCREAMING_SNAKE_CASE		=						image_processor(
											    [np.zeros((3, 800, 1333)	), np.zeros((3, 800, 1333)	)]						,segmentation_maps=[np.zeros((384, 384)	).astype(np.floataa	), np.zeros((384, 384)	).astype(np.floataa	)]						,return_tensors="""pt"""						,)

											SCREAMING_SNAKE_CASE		=						inputs["""pixel_values"""].to(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						[el.to(lowerCamelCase__	) for el in inputs["""mask_labels"""]]
											SCREAMING_SNAKE_CASE		=						[el.to(lowerCamelCase__	) for el in inputs["""class_labels"""]]

											with torch.no_grad():
																	SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

											self.assertTrue(outputs.loss is not None	)









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


