


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   ,							UpperCamelCase_   ,							UpperCamelCase_=2   ,							UpperCamelCase_=True   ,							UpperCamelCase_=False   ,							UpperCamelCase_=10   ,							UpperCamelCase_=3   ,							UpperCamelCase_=32 * 8   ,							UpperCamelCase_=32 * 8   ,							UpperCamelCase_=4   ,							UpperCamelCase_=64   ,							):
        lowercase_       :Tuple     =       parent
        lowercase_       :Dict     =       batch_size
        lowercase_       :Tuple     =       is_training
        lowercase_       :Optional[Any]     =       use_auxiliary_loss
        lowercase_       :Tuple     =       num_queries
        lowercase_       :Dict     =       num_channels
        lowercase_       :List[str]     =       min_size
        lowercase_       :int     =       max_size
        lowercase_       :Optional[Any]     =       num_labels
        lowercase_       :Tuple     =       hidden_dim
        lowercase_       :List[str]     =       hidden_dim
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
            UpperCamelCase_		)

        lowercase_       :Tuple     =       torch.ones([self.batch_size, self.min_size, self.max_size]   ,							device=UpperCamelCase_		)

        lowercase_       :Optional[int]     =       (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,							device=UpperCamelCase_		) > 0.5
        ).float()
        lowercase_       :Optional[int]     =       (torch.rand((self.batch_size, self.num_labels)   ,							device=UpperCamelCase_		) > 0.5).long()

        lowercase_       :Tuple     =       self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       MaskaFormerConfig(
            hidden_size=self.hidden_dim   ,							)
        lowercase_       :str     =       self.num_queries
        lowercase_       :List[str]     =       self.num_labels

        lowercase_       :Any     =       [1, 1, 1, 1]
        lowercase_       :Optional[Any]     =       self.num_channels

        lowercase_       :str     =       64
        lowercase_       :Optional[Any]     =       128
        lowercase_       :List[str]     =       self.hidden_dim
        lowercase_       :str     =       self.hidden_dim
        lowercase_       :Optional[Any]     =       self.hidden_dim
        return config
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Any     =       self.prepare_config_and_inputs()
        lowercase_       :Tuple     =       {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       output.encoder_hidden_states
        lowercase_       :Any     =       output.pixel_decoder_hidden_states
        lowercase_       :Optional[Any]     =       output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(UpperCamelCase_		)   ,							len(config.backbone_config.depths		)		)
        self.parent.assertTrue(len(UpperCamelCase_		)   ,							len(config.backbone_config.depths		)		)
        self.parent.assertTrue(len(UpperCamelCase_		)   ,							config.decoder_layers		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=False		):
        with torch.no_grad():
             lowercase_       :str     =       MaskaFormerModel(config=UpperCamelCase_		)
             model.to(UpperCamelCase_		)
             model.eval()

             lowercase_       :str     =       model(pixel_values=UpperCamelCase_   ,							pixel_mask=UpperCamelCase_		)
             lowercase_       :Optional[int]     =       model(UpperCamelCase_   ,							output_hidden_states=UpperCamelCase_		)

        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(UpperCamelCase_   ,							UpperCamelCase_		)




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :List[Any]     =       MaskaFormerForUniversalSegmentation(config=UpperCamelCase_		)
        model.to(UpperCamelCase_		)
        model.eval()

        def comm_check_on_output(UpperCamelCase_		):
             # 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():
             lowercase_       :Union[str, Any]     =       model(pixel_values=UpperCamelCase_   ,							pixel_mask=UpperCamelCase_		)
             lowercase_       :Tuple     =       model(UpperCamelCase_		)

             comm_check_on_output(UpperCamelCase_		)

             lowercase_       :Union[str, Any]     =       model(
                 pixel_values=UpperCamelCase_   ,							pixel_mask=UpperCamelCase_   ,							mask_labels=UpperCamelCase_   ,							class_labels=UpperCamelCase_		)

        comm_check_on_output(UpperCamelCase_		)

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






@require_torch
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







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

   lowercase					:	List[str]									=False
   lowercase					:	Union[str, Any]									=False
   lowercase					:	Union[str, Any]									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       MaskaFormerModelTester(self		)
        lowercase_       :Tuple     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							has_text_modality=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Union[str, Any]     =       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(UpperCamelCase_   ,							**UpperCamelCase_   ,							output_hidden_states=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Tuple     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase_		)
   @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip(reason='''Mask2Former is not a generative model'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip(reason='''Mask2Former does not use token embeddings'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @require_torch_multi_gpu
   @unittest.skip(
       reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
   def 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Union[str, Any]     =       self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_       :Optional[Any]     =       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]   ,							UpperCamelCase_		)
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             lowercase_       :Any     =       MaskaFormerModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       (self.model_tester.min_size,) * 2
        lowercase_       :Optional[Any]     =       {
            '''pixel_values''': torch.randn((2, 3, *size)   ,							device=UpperCamelCase_		),
            '''mask_labels''': torch.randn((2, 10, *size)   ,							device=UpperCamelCase_		),
            '''class_labels''': torch.zeros(2   ,							10   ,							device=UpperCamelCase_		).long(),
        }
        lowercase_       :Tuple     =       self.model_tester.get_config()

        lowercase_       :List[Any]     =       MaskaFormerForUniversalSegmentation(UpperCamelCase_		).to(UpperCamelCase_		)
        lowercase_       :List[Any]     =       model(**UpperCamelCase_		)
        self.assertTrue(outputs.loss is not None		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Dict     =       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(UpperCamelCase_   ,							**UpperCamelCase_   ,							output_hidden_states=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :int     =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_       :Optional[Any]     =       model_class(UpperCamelCase_		).to(UpperCamelCase_		)
             lowercase_       :Optional[int]     =       model(**UpperCamelCase_   ,							output_attentions=UpperCamelCase_		)
             self.assertTrue(outputs.attentions is not None		)
   def 		UpperCamelCase  (     self		):
        if not self.model_tester.is_training:
             return

        lowercase_       :Dict     =       self.all_model_classes[1]
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()

        lowercase_       :str     =       model_class(UpperCamelCase_		)
        model.to(UpperCamelCase_		)
        model.train()

        lowercase_       :Optional[int]     =       model(UpperCamelCase_   ,							mask_labels=UpperCamelCase_   ,							class_labels=UpperCamelCase_		).loss
        loss.backward()




   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       self.all_model_classes[1]
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Union[str, Any]     =       self.model_tester.prepare_config_and_inputs()
        lowercase_       :Any     =       True
        lowercase_       :List[str]     =       True

        lowercase_       :Dict     =       model_class(UpperCamelCase_		).to(UpperCamelCase_		)
        model.train()

        lowercase_       :str     =       model(UpperCamelCase_   ,							mask_labels=UpperCamelCase_   ,							class_labels=UpperCamelCase_		)

        lowercase_       :Any     =       outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        lowercase_       :Optional[Any]     =       outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        lowercase_       :Any     =       outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        lowercase_       :Any     =       outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=UpperCamelCase_		)

        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       :		List[str]         =				1e-4




def     UpperCamelCase    (      )					->							str:
     '''simple docstring'''





     lowercase_       :List[Any]     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
     return image






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


   '''simple docstring'''







   @cached_property
   def 		UpperCamelCase  (     self		):
        return "facebook/mask2former-swin-small-coco-instance"
   @cached_property
   def 		UpperCamelCase  (     self		):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(UpperCamelCase_		)
        lowercase_       :Dict     =       self.default_image_processor
        lowercase_       :Optional[int]     =       prepare_img()
        lowercase_       :Optional[int]     =       image_processor(UpperCamelCase_   ,							return_tensors='''pt'''		).to(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       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(UpperCamelCase_   ,							(1, 3, 384, 384)		)

        with torch.no_grad():
             lowercase_       :Optional[int]     =       model(**UpperCamelCase_		)

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

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

        lowercase_       :str     =       torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]		).to(UpperCamelCase_		)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,							UpperCamelCase_   ,							atol=UpperCamelCase_		)		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(UpperCamelCase_		).eval()
        lowercase_       :Any     =       self.default_image_processor
        lowercase_       :Dict     =       prepare_img()
        lowercase_       :Optional[int]     =       image_processor(UpperCamelCase_   ,							return_tensors='''pt'''		).to(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       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(UpperCamelCase_   ,							(1, 3, 384, 384)		)

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




   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(UpperCamelCase_		).eval()
        lowercase_       :Dict     =       self.default_image_processor

        lowercase_       :str     =       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'''   ,							)

        lowercase_       :Any     =       inputs['''pixel_values'''].to(UpperCamelCase_		)
        lowercase_       :Optional[int]     =       [el.to(UpperCamelCase_		) for el in inputs['''mask_labels''']]
        lowercase_       :Any     =       [el.to(UpperCamelCase_		) for el in inputs['''class_labels''']]

        with torch.no_grad():
             lowercase_       :Dict     =       model(**UpperCamelCase_		)

        self.assertTrue(outputs.loss is not None		)





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     if openai_config_file == "":
          lowercase_       :str     =       OpenAIGPTConfig()
     else:
          lowercase_       :int     =       OpenAIGPTConfig.from_json_file(_a		)
     lowercase_       :int     =       OpenAIGPTModel(_a		)

     # Load weights from numpy
     load_tf_weights_in_openai_gpt(_a       ,		_a       ,		_a		)

     # Save pytorch-model
     lowercase_       :Optional[int]     =       pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     lowercase_       :List[str]     =       pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(f"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)
     print(f"Save configuration file to {pytorch_config_dump_path}"		)
     with open(_a       ,		'''w'''       ,		encoding='''utf-8'''		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		List[str]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--openai_checkpoint_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the TensorFlow checkpoint path.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--openai_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained OpenAI model. \n"
           "This specifies the model architecture."
       ),
   )
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )

