





"""simple docstring"""






import inspect
import unittest

from transformers import MobileViTConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
    from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor



class 	lowerCAmelCase					(      snake_case							):






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.config_class(**self.inputs_dict     )
              self.parent.assertTrue(hasattr(a__    ,  'hidden_sizes'     )     )
              self.parent.assertTrue(hasattr(a__    ,  'neck_hidden_sizes'     )     )
              self.parent.assertTrue(hasattr(a__    ,  'num_attention_heads'     )     )



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=32    ,  a__=2    ,  a__=3    ,  a__=6_40    ,  a__=4    ,  a__="silu"    ,  a__=3    ,  a__=32    ,  a__=0.1    ,  a__=0.1    ,  a__=0.1    ,  a__=0.02    ,  a__=True    ,  a__=True    ,  a__=10    ,  a__=None    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     image_size
              _UpperCAmelCase     =     patch_size
              _UpperCAmelCase     =     num_channels
              _UpperCAmelCase     =     last_hidden_size
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     conv_kernel_size
              _UpperCAmelCase     =     output_stride
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     classifier_dropout_prob
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     num_labels
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     scope






       def 							__A (   self     ):
              _UpperCAmelCase     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              if self.use_labels:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.num_labels     )
                     _UpperCAmelCase     =     ids_tensor([self.batch_size, self.image_size, self.image_size]    ,  self.num_labels     )

              _UpperCAmelCase     =     self.get_config()

              return config, pixel_values, labels, pixel_labels






       def 							__A (   self     ):
              return MobileViTConfig(
                  image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  num_attention_heads=self.num_attention_heads    ,  hidden_act=self.hidden_act    ,  conv_kernel_size=self.conv_kernel_size    ,  output_stride=self.output_stride    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  classifier_dropout_prob=self.classifier_dropout_prob    ,  initializer_range=self.initializer_range    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     MobileViTModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(
                  result.last_hidden_state.shape    ,  (
                      self.batch_size,
                      self.last_hidden_size,
                      self.image_size // self.output_stride,
                      self.image_size // self.output_stride,
                  )    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.num_labels
              _UpperCAmelCase     =     MobileViTForImageClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.num_labels
              _UpperCAmelCase     =     MobileViTForSemanticSegmentation(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(
                  result.logits.shape    ,  (
                      self.batch_size,
                      self.num_labels,
                      self.image_size // self.output_stride,
                      self.image_size // self.output_stride,
                  )    ,  )
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertEqual(
                  result.logits.shape    ,  (
                      self.batch_size,
                      self.num_labels,
                      self.image_size // self.output_stride,
                      self.image_size // self.output_stride,
                  )    ,  )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     config_and_inputs
              _UpperCAmelCase     =     {'pixel_values': pixel_values}
              return config, inputs_dict



@require_torch
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__						 =  (
           {
               """feature-extraction""": MobileViTModel,
               """image-classification""": MobileViTForImageClassification,
               """image-segmentation""": MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     MobileViTModelTester(self     )
              _UpperCAmelCase     =     MobileViTConfigTester(self    ,  config_class=a__    ,  has_text_modality=a__     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       @unittest.skip(reason='MobileViT does not use inputs_embeds'     )
       def 							__A (   self     ):
              pass






       @unittest.skip(reason='MobileViT does not support input and output embeddings'     )
       def 							__A (   self     ):
              pass






       @unittest.skip(reason='MobileViT does not output attentions'     )
       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

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

                     _UpperCAmelCase     =     ['pixel_values']
                     self.assertListEqual(arg_names[:1]    ,  a__     )






       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              def check_hidden_states_output(a__    ,  a__    ,  a__     ):
                     _UpperCAmelCase     =     model_class(a__     )
                     model.to(a__     )
                     model.eval()

                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**self._prepare_for_class(a__    ,  a__     )     )

                     _UpperCAmelCase     =     outputs.hidden_states

                     _UpperCAmelCase     =     5
                     self.assertEqual(len(a__     )    ,  a__     )

                     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                     # with the width and height being successively divided by 2.
                     _UpperCAmelCase     =     2
                     for i in range(len(a__     )     ):
                            self.assertListEqual(
                                list(hidden_states[i].shape[-2:]     )    ,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,  )
                            divisor *= 2

                     self.assertEqual(self.model_tester.output_stride    ,  divisor // 2     )

              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     True
                     check_hidden_states_output(a__    ,  a__    ,  a__     )

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     _UpperCAmelCase     =     True

                     check_hidden_states_output(a__    ,  a__    ,  a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_semantic_segmentation(*a__     )






       @slow
       def 							__A (   self     ):
              for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     MobileViTModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )


def       __lowerCamelCase    (       )		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       return image



@require_torch
@require_vision
class 	lowerCAmelCase					(      unittest.TestCase							):






       @cached_property
       def 							__A (   self     ):
              return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'     ) if is_vision_available() else None






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'     ).to(a__     )

              _UpperCAmelCase     =     self.default_image_processor
              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     ).to(a__     )

              # forward pass
              with torch.no_grad():
                     _UpperCAmelCase     =     model(**a__     )

              # verify the logits
              _UpperCAmelCase     =     torch.Size((1, 10_00)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor([-1.9_364, -1.2_327, -0.4_653]     ).to(a__     )

              self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  a__    ,  atol=1E-4     )     )






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )
              _UpperCAmelCase     =     model.to(a__     )

              _UpperCAmelCase     =     MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )

              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     ).to(a__     )

              # forward pass
              with torch.no_grad():
                     _UpperCAmelCase     =     model(**a__     )
              _UpperCAmelCase     =     outputs.logits

              # verify the logits
              _UpperCAmelCase     =     torch.Size((1, 21, 32, 32)     )
              self.assertEqual(logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor(
                  [
                      [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                      [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                      [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
                  ]    ,  device=a__    ,  )

              self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,  a__    ,  atol=1E-4     )     )






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )
              _UpperCAmelCase     =     model.to(a__     )

              _UpperCAmelCase     =     MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )

              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     ).to(a__     )

              # forward pass
              with torch.no_grad():
                     _UpperCAmelCase     =     model(**a__     )

              _UpperCAmelCase     =     outputs.logits.detach().cpu()

              _UpperCAmelCase     =     image_processor.post_process_semantic_segmentation(outputs=a__    ,  target_sizes=[(50, 60)]     )
              _UpperCAmelCase     =     torch.Size((50, 60)     )
              self.assertEqual(segmentation[0].shape    ,  a__     )

              _UpperCAmelCase     =     image_processor.post_process_semantic_segmentation(outputs=a__     )
              _UpperCAmelCase     =     torch.Size((32, 32)     )
              self.assertEqual(segmentation[0].shape    ,  a__     )













"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       return model


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






