






'''simple docstring'''







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=64   ,_lowerCAmelCase=2   ,_lowerCAmelCase=3   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=32   ,_lowerCAmelCase=5   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=10   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=[1, 16, 4, 4]   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  patch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_labels
								lowerCamelCase__         =  hidden_size
								lowerCamelCase__         =  num_hidden_layers
								lowerCamelCase__         =  num_attention_heads
								lowerCamelCase__         =  intermediate_size
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  hidden_dropout_prob
								lowerCamelCase__         =  attention_probs_dropout_prob
								lowerCamelCase__         =  type_sequence_label_size
								lowerCamelCase__         =  initializer_range
								lowerCamelCase__         =  scope
								lowerCamelCase__         =  backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								lowerCamelCase__         =  (self.image_size // 32) ** 2
								lowerCamelCase__         =  num_patches + 1






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

								lowerCamelCase__         =  self.get_config()

								return config, pixel_values, labels






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {
								    """global_padding""": """same""",
								    """layer_type""": """bottleneck""",
								    """depths""": [3, 4, 9],
								    """out_features""": ["""stage1""", """stage2""", """stage3"""],
								    """embedding_dynamic_padding""": True,
								    """hidden_sizes""": [4, 8, 16, 32],
								    """num_groups""": 2,
								}

								return ViTHybridConfig(
								    image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,is_decoder=_lowerCAmelCase   ,initializer_range=self.initializer_range   ,backbone_featmap_shape=self.backbone_featmap_shape   ,backbone_config=_lowerCAmelCase   ,)






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  ViTHybridModel(config=_lowerCAmelCase       )
								model.to(_lowerCAmelCase       )
								model.eval()
								lowerCamelCase__         =  model(_lowerCAmelCase       )
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.type_sequence_label_size
								lowerCamelCase__         =  ViTHybridForImageClassification(_lowerCAmelCase       )
								model.to(_lowerCAmelCase       )
								model.eval()
								lowerCamelCase__         =  model(_lowerCAmelCase   ,labels=_lowerCAmelCase       )
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  config_and_inputs
								lowerCamelCase__         =  {"""pixel_values""": pixel_values}
								return config, inputs_dict




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




		'''simple docstring'''





		_UpperCamelCase				  =					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		_UpperCamelCase				  =					(
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  ViTHybridModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=_lowerCAmelCase   ,has_text_modality=_lowerCAmelCase   ,hidden_size=37       )






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






		@unittest.skip(reason="""ViT does not use inputs_embeds"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														self.assertIsInstance(model.get_input_embeddings()   ,(nn.Module)       )
														lowerCamelCase__         =  model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_lowerCAmelCase   ,nn.Linear       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

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

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  _config_zero_init(_lowerCAmelCase       )
								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(config=_lowerCAmelCase       )
														# Skip the check for the backbone
														for name, module in model.named_modules():
																				if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																										lowerCamelCase__         =  [F'''{name}.{key}''' for key in module.state_dict().keys()]
																										break

														for name, param in model.named_parameters():
																				if param.requires_grad:
																										if name in backbone_params:
																																continue
																										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'''   ,)






		@slow
		def 			UpperCamelCase_					(      self       ):
								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCamelCase__         =  ViTHybridModel.from_pretrained(_lowerCAmelCase       )
														self.assertIsNotNone(_lowerCAmelCase       )



def 						A__			(				):
						lowerCamelCase__         =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image





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




		'''simple docstring'''





		@cached_property
		def 			UpperCamelCase_					(      self       ):
								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
								    if is_vision_available()
								    else None
								)






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
								    _lowerCAmelCase       )

								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(images=_lowerCAmelCase   ,return_tensors="""pt"""       ).to(_lowerCAmelCase       )

								# forward pass
								with torch.no_grad():
														lowerCamelCase__         =  model(**_lowerCAmelCase       )

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

								lowerCamelCase__         =  torch.tensor([-1.9090, -0.4993, -0.2389]       ).to(_lowerCAmelCase       )

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






		@slow
		@require_accelerate
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""       )
								lowerCamelCase__         =  ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""   ,device_map="""auto"""       )

								lowerCamelCase__         =  prepare_img()

								lowerCamelCase__         =  image_processor(images=_lowerCAmelCase   ,return_tensors="""pt"""       )
								lowerCamelCase__         =  model(**_lowerCAmelCase       )
								lowerCamelCase__         =  outputs.logits
								# model predicts one of the 1000 ImageNet classes
								lowerCamelCase__         =  logits.argmax(-1       ).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]   ,"""tabby, tabby cat"""       )









'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						lowerCamelCase__         =  [
						    """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(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Tuple						):
						lowerCamelCase__       ,			lowerCamelCase__         =  emb.weight.shape
						lowerCamelCase__         =  nn.Linear(__lowerCAmelCase   ,    __lowerCAmelCase   ,    bias=__lowerCAmelCase						)
						lowerCamelCase__         =  emb.weight.data
						return lin_layer
def 						A__			(				__lowerCAmelCase		:			Dict						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)
						lowerCamelCase__         =  mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						lowerCamelCase__         =  mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCAmelCase						)
						lowerCamelCase__         =  state_dict["""encoder.embed_tokens.weight"""].shape[0]

						lowerCamelCase__         =  MaMaaaConfig(
						    vocab_size=__lowerCAmelCase   ,    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"""   ,    )

						lowerCamelCase__         =  state_dict["""decoder.embed_tokens.weight"""]
						lowerCamelCase__         =  MaMaaaForConditionalGeneration(__lowerCAmelCase						)
						model.model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase						)
						lowerCamelCase__         =  make_linear_from_emb(model.model.shared						)

						return model


if __name__ == "__main__":
							UpperCamelCase    :      Optional[Any]  			=		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.')
							UpperCamelCase    :      Tuple  			=		parser.parse_args()
							UpperCamelCase    :      List[Any]  			=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)

