





"""simple docstring"""






import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
    from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=32    ,  a__=2    ,  a__=3    ,  a__=16    ,  a__=[1, 2, 1]    ,  a__=[2, 2, 4]    ,  a__=2    ,  a__=2.0    ,  a__=True    ,  a__=0.0    ,  a__=0.0    ,  a__=0.1    ,  a__="gelu"    ,  a__=False    ,  a__=True    ,  a__=0.02    ,  a__=1E-5    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=10    ,  a__=8    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     image_size
              _UpperCAmelCase     =     patch_size
              _UpperCAmelCase     =     num_channels
              _UpperCAmelCase     =     embed_dim
              _UpperCAmelCase     =     depths
              _UpperCAmelCase     =     num_heads
              _UpperCAmelCase     =     window_size
              _UpperCAmelCase     =     mlp_ratio
              _UpperCAmelCase     =     qkv_bias
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     drop_path_rate
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     use_absolute_embeddings
              _UpperCAmelCase     =     patch_norm
              _UpperCAmelCase     =     layer_norm_eps
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     scope
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     type_sequence_label_size
              _UpperCAmelCase     =     encoder_stride






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

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

              _UpperCAmelCase     =     self.get_config()

              return config, pixel_values, labels






       def 							__A (   self     ):
              return SwinvaConfig(
                  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    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     SwinvaModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )

              _UpperCAmelCase     =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
              _UpperCAmelCase     =     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 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     SwinvaForMaskedImageModeling(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(
                  result.logits.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

              # test greyscale images
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     SwinvaForMaskedImageModeling(a__     )
              model.to(a__     )
              model.eval()

              _UpperCAmelCase     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, 1, self.image_size, self.image_size)     )






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






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              _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__						 =  (
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       lowerCAmelCase__						 =  (
           {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

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






       def 							__A (   self     ):
              _UpperCAmelCase     =     SwinvaModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  embed_dim=37     )






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






       @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'     )
       def 							__A (   self     ):
              pass






       @unittest.skip(reason='Swinv2 does not use inputs_embeds'     )
       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__     )
                     self.assertIsInstance(model.get_input_embeddings()    ,  (nn.Module)     )
                     _UpperCAmelCase     =     model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(a__    ,  nn.Linear     )     )






       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__     )






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

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     False
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     model_class(a__     )
                     model.to(a__     )
                     model.eval()
                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**self._prepare_for_class(a__    ,  a__     )     )
                     _UpperCAmelCase     =     outputs.attentions
                     _UpperCAmelCase     =     len(self.model_tester.depths     )
                     self.assertEqual(len(a__     )    ,  a__     )

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

                     self.assertListEqual(
                         list(attentions[0].shape[-3:]     )    ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,  )
                     _UpperCAmelCase     =     len(a__     )

                     # Check attention is always last and order is fine
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     model_class(a__     )
                     model.to(a__     )
                     model.eval()
                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**self._prepare_for_class(a__    ,  a__     )     )

                     if hasattr(self.model_tester    ,  'num_hidden_states_types'     ):
                            _UpperCAmelCase     =     self.model_tester.num_hidden_states_types
                     else:
                            # also another +1 for reshaped_hidden_states
                            _UpperCAmelCase     =     2
                     self.assertEqual(out_len + added_hidden_states    ,  len(a__     )     )

                     _UpperCAmelCase     =     outputs.attentions

                     self.assertEqual(len(a__     )    ,  a__     )

                     self.assertListEqual(
                         list(self_attentions[0].shape[-3:]     )    ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,  )






       def 							__A (   self    ,  a__    ,  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     =     getattr(
                  self.model_tester    ,  'expected_num_hidden_layers'    ,  len(self.model_tester.depths     ) + 1     )
              self.assertEqual(len(a__     )    ,  a__     )

              # Swinv2 has a different seq_length
              _UpperCAmelCase     =     (
                  config.patch_size
                  if isinstance(config.patch_size    ,  collections.abc.Iterable     )
                  else (config.patch_size, config.patch_size)
              )

              _UpperCAmelCase     =     (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]    ,  )

              _UpperCAmelCase     =     outputs.reshaped_hidden_states
              self.assertEqual(len(a__     )    ,  a__     )

              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     reshaped_hidden_states[0].shape
              _UpperCAmelCase     =     (
                  reshaped_hidden_states[0].view(a__    ,  a__    ,  height * width     ).permute(0    ,  2    ,  1     )
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-2:]     )    ,  [num_patches, self.model_tester.embed_dim]    ,  )






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

              _UpperCAmelCase     =     (
                  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     =     True
                     self.check_hidden_states_output(a__    ,  a__    ,  a__    ,  a__     )

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

                     self.check_hidden_states_output(a__    ,  a__    ,  a__    ,  a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3

              _UpperCAmelCase     =     (
                  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     =     (
                  config.patch_size
                  if isinstance(config.patch_size    ,  collections.abc.Iterable     )
                  else (config.patch_size, config.patch_size)
              )

              _UpperCAmelCase     =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
              _UpperCAmelCase     =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     _UpperCAmelCase     =     True
                     self.check_hidden_states_output(a__    ,  a__    ,  a__    ,  (padded_height, padded_width)     )






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






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






       @slow
       def 							__A (   self     ):




              for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     SwinvaModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )






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

              _UpperCAmelCase     =     _config_zero_init(a__     )
              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(config=a__     )
                     for name, param in model.named_parameters():
                            if "embeddings" not in name and "logit_scale" 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 	lowerCAmelCase					(      unittest.TestCase							):






       @cached_property
       def 							__A (   self     ):
              return (
                  AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'     )
                  if is_vision_available()
                  else None
              )






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'     ).to(
                  a__     )
              _UpperCAmelCase     =     self.default_image_processor

              _UpperCAmelCase     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
              _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([-0.3_947, -0.4_306, 0.0_026]     ).to(a__     )
              self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  a__    ,  atol=1E-4     )     )









"""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)






