import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
     from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=32		, snake_case__=2		, snake_case__=3		, snake_case__=16		, snake_case__=[1, 2, 1]		, snake_case__=[2, 2, 4]		, snake_case__=2		, snake_case__=2.0		, snake_case__=True		, snake_case__=0.0		, snake_case__=0.0		, snake_case__=0.1		, snake_case__="gelu"		, snake_case__=False		, snake_case__=True		, snake_case__=0.02		, snake_case__=1e-5		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=10		, snake_case__=8		, snake_case__=["stage1", "stage2"]		, snake_case__=[1, 2]		, )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=parent
       UpperCAmelCase     :			Tuple     	=batch_size
       UpperCAmelCase     :			Union[str, Any]     	=image_size
       UpperCAmelCase     :			Optional[Any]     	=patch_size
       UpperCAmelCase     :			Tuple     	=num_channels
       UpperCAmelCase     :			Dict     	=embed_dim
       UpperCAmelCase     :			Optional[Any]     	=depths
       UpperCAmelCase     :			Optional[Any]     	=num_heads
       UpperCAmelCase     :			Tuple     	=window_size
       UpperCAmelCase     :			List[Any]     	=mlp_ratio
       UpperCAmelCase     :			Optional[Any]     	=qkv_bias
       UpperCAmelCase     :			List[str]     	=hidden_dropout_prob
       UpperCAmelCase     :			List[Any]     	=attention_probs_dropout_prob
       UpperCAmelCase     :			Tuple     	=drop_path_rate
       UpperCAmelCase     :			Any     	=hidden_act
       UpperCAmelCase     :			Union[str, Any]     	=use_absolute_embeddings
       UpperCAmelCase     :			int     	=patch_norm
       UpperCAmelCase     :			List[Any]     	=layer_norm_eps
       UpperCAmelCase     :			Dict     	=initializer_range
       UpperCAmelCase     :			Union[str, Any]     	=is_training
       UpperCAmelCase     :			Dict     	=scope
       UpperCAmelCase     :			Tuple     	=use_labels
       UpperCAmelCase     :			Tuple     	=type_sequence_label_size
       UpperCAmelCase     :			int     	=encoder_stride
       UpperCAmelCase     :			Dict     	=out_features
       UpperCAmelCase     :			Dict     	=out_indices
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       UpperCAmelCase     :			Optional[Any]     	=None
       if self.use_labels:
             UpperCAmelCase     :			str     	=ids_tensor([self.batch_size]		, self.type_sequence_label_size    )

       UpperCAmelCase     :			Tuple     	=self.get_config()

       return config, pixel_values, labels
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       return SwinConfig(
           image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, embed_dim=self.embed_dim		, depths=self.depths		, num_heads=self.num_heads		, window_size=self.window_size		, mlp_ratio=self.mlp_ratio		, qkv_bias=self.qkv_bias		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, drop_path_rate=self.drop_path_rate		, hidden_act=self.hidden_act		, use_absolute_embeddings=self.use_absolute_embeddings		, path_norm=self.patch_norm		, layer_norm_eps=self.layer_norm_eps		, initializer_range=self.initializer_range		, encoder_stride=self.encoder_stride		, out_features=self.out_features		, out_indices=self.out_indices		, )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=SwinModel(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       UpperCAmelCase     :			Tuple     	=model(snake_case__    )

       UpperCAmelCase     :			int     	=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
       UpperCAmelCase     :			Tuple     	=int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

       self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, expected_seq_len, expected_dim)    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=SwinBackbone(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       UpperCAmelCase     :			Tuple     	=model(snake_case__    )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps    )		, len(config.out_features    )    )
       self.parent.assertListEqual(list(result.feature_maps[0].shape    )		, [self.batch_size, model.channels[0], 16, 16]    )

       # verify channels
       self.parent.assertEqual(len(model.channels    )		, len(config.out_features    )    )

       # verify backbone works with out_features=None
       UpperCAmelCase     :			Union[str, Any]     	=None
       UpperCAmelCase     :			str     	=SwinBackbone(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       UpperCAmelCase     :			Tuple     	=model(snake_case__    )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps    )		, 1    )
       self.parent.assertListEqual(list(result.feature_maps[0].shape    )		, [self.batch_size, model.channels[-1], 4, 4]    )

       # verify channels
       self.parent.assertEqual(len(model.channels    )		, 1    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=SwinForMaskedImageModeling(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       UpperCAmelCase     :			int     	=model(snake_case__    )
       self.parent.assertEqual(
           result.logits.shape		, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

       # test greyscale images
       UpperCAmelCase     :			str     	=1
       UpperCAmelCase     :			Optional[Any]     	=SwinForMaskedImageModeling(snake_case__    )
       model.to(snake_case__    )
       model.eval()

       UpperCAmelCase     :			Tuple     	=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       UpperCAmelCase     :			Union[str, Any]     	=model(snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, 1, self.image_size, self.image_size)    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.type_sequence_label_size
       UpperCAmelCase     :			Any     	=SwinForImageClassification(snake_case__    )
       model.to(snake_case__    )
       model.eval()
       UpperCAmelCase     :			Optional[Any]     	=model(snake_case__		, labels=snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)    )

       # test greyscale images
       UpperCAmelCase     :			int     	=1
       UpperCAmelCase     :			Any     	=SwinForImageClassification(snake_case__    )
       model.to(snake_case__    )
       model.eval()

       UpperCAmelCase     :			List[str]     	=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       UpperCAmelCase     :			Optional[Any]     	=model(snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)    )






 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase
       )	,    (
           UpperCAmelCase
       )	,    (
           UpperCAmelCase
       )	,    
       )     :			Tuple     	=config_and_inputs
       UpperCAmelCase     :			Union[str, Any]     	={'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class       __snake_case (  lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : List[str] 	=   (
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase     : Union[str, Any] 	=   (
     {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase     : Union[str, Any] 	=   True

 __lowerCamelCase     : Tuple 	=   False
 __lowerCamelCase     : Tuple 	=   False
 __lowerCamelCase     : Any 	=   False
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=SwinModelTester(self    )
       UpperCAmelCase     :			Tuple     	=ConfigTester(self		, config_class=snake_case__		, embed_dim=37    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       return
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__    )
 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''    )
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       pass
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       super().test_training_gradient_checkpointing()
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__    )
 @unittest.skip(reason='''Swin does not use inputs_embeds'''    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       pass
 @unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       pass
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





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

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			List[Any]     	=model_class(snake_case__    )
             self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)    )
             UpperCAmelCase     :			List[Any]     	=model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__		, nn.Linear    )    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





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

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			List[str]     	=model_class(snake_case__    )
             UpperCAmelCase     :			Any     	=inspect.signature(model.forward    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase     :			Union[str, Any]     	=[*signature.parameters.keys()]

             UpperCAmelCase     :			Tuple     	=['''pixel_values''']
             self.assertListEqual(arg_names[:1]		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase     :			Optional[int]     	=True

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			Tuple     	=True
             UpperCAmelCase     :			Dict     	=False
             UpperCAmelCase     :			int     	=True
             UpperCAmelCase     :			Tuple     	=model_class(snake_case__    )
             model.to(snake_case__    )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase     :			Tuple     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )
             UpperCAmelCase     :			Any     	=outputs.attentions
             UpperCAmelCase     :			Dict     	=len(self.model_tester.depths    )
             self.assertEqual(len(snake_case__    )		, snake_case__    )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCAmelCase     :			str     	=True
             UpperCAmelCase     :			Optional[Any]     	=config.window_size**2
             UpperCAmelCase     :			int     	=model_class(snake_case__    )
             model.to(snake_case__    )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase     :			int     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )
             UpperCAmelCase     :			Optional[int]     	=outputs.attentions
             self.assertEqual(len(snake_case__    )		, snake_case__    )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]    )		, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		, )
             UpperCAmelCase     :			Union[str, Any]     	=len(snake_case__    )

             # Check attention is always last and order is fine
             UpperCAmelCase     :			Any     	=True
             UpperCAmelCase     :			Optional[Any]     	=True
             UpperCAmelCase     :			Optional[Any]     	=model_class(snake_case__    )
             model.to(snake_case__    )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase     :			Optional[Any]     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )

             # also another +1 for reshaped_hidden_states
             UpperCAmelCase     :			Optional[Any]     	=1 if model_class.__name__ == '''SwinBackbone''' else 2
             self.assertEqual(out_len + added_hidden_states		, len(snake_case__    )    )

             UpperCAmelCase     :			Tuple     	=outputs.attentions

             self.assertEqual(len(snake_case__    )		, snake_case__    )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]    )		, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		, )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=model_class(snake_case__    )
       model.to(snake_case__    )
       model.eval()

       with torch.no_grad():
             UpperCAmelCase     :			int     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )

       UpperCAmelCase     :			Tuple     	=outputs.hidden_states

       UpperCAmelCase     :			str     	=getattr(
           self.model_tester		, '''expected_num_hidden_layers'''		, len(self.model_tester.depths    ) + 1    )
       self.assertEqual(len(snake_case__    )		, snake_case__    )

       # Swin has a different seq_length
       UpperCAmelCase     :			int     	=(
           config.patch_size
           if isinstance(config.patch_size		, collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       UpperCAmelCase     :			Dict     	=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]    )		, [num_patches, self.model_tester.embed_dim]		, )

       if not model_class.__name__ == "SwinBackbone":
             UpperCAmelCase     :			Optional[int]     	=outputs.reshaped_hidden_states
             self.assertEqual(len(snake_case__    )		, snake_case__    )

             UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Any     	=reshaped_hidden_states[0].shape
             UpperCAmelCase     :			List[Any]     	=(
                 reshaped_hidden_states[0].view(snake_case__		, snake_case__		, height * width    ).permute(0		, 2		, 1    )
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]    )		, [num_patches, self.model_tester.embed_dim]		, )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





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

       UpperCAmelCase     :			List[Any]     	=(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		, collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			int     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, snake_case__    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase     :			Dict     	=True

             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Dict     	=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase     :			Dict     	=3

       UpperCAmelCase     :			Optional[int]     	=(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		, collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       UpperCAmelCase     :			Optional[Any]     	=(
           config.patch_size
           if isinstance(config.patch_size		, collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       UpperCAmelCase     :			List[str]     	=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       UpperCAmelCase     :			Optional[int]     	=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			Optional[Any]     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, (padded_height, padded_width)    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase     :			Any     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, (padded_height, padded_width)    )
 @slow
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase     :			List[Any]     	=SwinModel.from_pretrained(snake_case__    )
             self.assertIsNotNone(snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase     :			List[Any]     	=_config_zero_init(snake_case__    )
       for model_class in self.all_model_classes:
             UpperCAmelCase     :			List[str]     	=model_class(config=snake_case__    )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1e9).round() / 1e9).item()		, [0.0, 1.0]		, msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		, )







@require_vision
@require_torch
class       __snake_case (  unittest.TestCase ):
 @cached_property
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       return (
           AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    )
           if is_vision_available()
           else None
       )






 @slow
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    ).to(snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	=self.default_image_processor

       UpperCAmelCase     :			List[Any]     	=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       UpperCAmelCase     :			int     	=image_processor(images=snake_case__		, return_tensors='''pt'''    ).to(snake_case__    )

       # forward pass
       with torch.no_grad():
             UpperCAmelCase     :			Any     	=model(**snake_case__    )

       # verify the logits
       UpperCAmelCase     :			int     	=torch.Size((1, 1000)    )
       self.assertEqual(outputs.logits.shape		, snake_case__    )
       UpperCAmelCase     :			List[str]     	=torch.tensor([-0.0948, -0.6454, -0.0921]    ).to(snake_case__    )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]		, snake_case__		, atol=1e-4    )    )







@require_torch
class       __snake_case (  unittest.TestCase       , lowerCamelCase__ ):
 __lowerCamelCase     : Tuple 	=   (SwinBackbone,) if is_torch_available() else ()
 __lowerCamelCase     : List[str] 	=   SwinConfig
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=SwinModelTester(self    )

from typing import TYPE_CHECKING

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


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
