


'''simple docstring'''




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_a   :   str												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   Tuple						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)
     if "model" in sd.keys():
          __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)['model']

     # pop unnecessary weights
     __snake_case           =						[
         'decoder.version',
         'decoder.output_projection.weight',
     ]
     for key in keys_to_delete:
          if key in sd:
               sd.pop(lowercase__						)

     __snake_case           =						{
         'decoder.project_in_dim.weight': 'decoder.project_in.weight',
         'decoder.project_out_dim.weight': 'decoder.project_out.weight',
         'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
         'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
     }
     for old_key, new_key in keys_to_rename.items():
          if old_key in sd:
               __snake_case           =						sd.pop(lowercase__						)

     __snake_case           =						list(sd.keys()						)
     for key in keys:
          if ".qkv_proj." in key:
               __snake_case           =						sd[key]
               # We split QKV in separate Q,K,V

               __snake_case           =						key.replace('.qkv_proj.'							,   '.q_proj.'						)
               __snake_case           =						key.replace('.qkv_proj.'							,   '.k_proj.'						)
               __snake_case           =						key.replace('.qkv_proj.'							,   '.v_proj.'						)

               __snake_case           =						value.shape[0]
               assert depth % 3 == 0
               # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
               # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
               __snake_case      ,       __snake_case      ,       __snake_case           =						torch.split(lowercase__							,   depth // 3							,   dim=0						)

               __snake_case           =						q
               __snake_case           =						k
               __snake_case           =						v
               del sd[key]

     return sd





@torch.no_grad()
def   _a		(lowercase__			:   str							,   lowercase__			:   Tuple							,   lowercase__			:   List[Any]=None						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						load_checkpoint(lowercase__						)

     if config is not None:
          __snake_case           =						OPTConfig.from_pretrained(lowercase__						)
     else:
          __snake_case           =						OPTConfig()

     __snake_case           =						OPTModel(lowercase__						).half().eval()
     model.load_state_dict(lowercase__						)

     # Check results
     Path(lowercase__						).mkdir(exist_ok=lowercase__						)
     model.save_pretrained(lowercase__						)


if __name__ == "__main__":
       _a   :   List[Any]												=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--fairseq_path",
           type=str,
           help=(
               "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
               " https://huggingface.co/models?other=opt_metasq"
           ),
       )
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
       _a   :   List[Any]												=   parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)












"""simple docstring"""







import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class      UpperCamelCase		:






						def __init__(		self  :Union[str, Any]			, __magic_name__  :Any			, __magic_name__  :List[str]=13			, __magic_name__  :Any=3			, __magic_name__  :str=True			, __magic_name__  :int=True			, __magic_name__  :Dict=0.1			, __magic_name__  :Optional[int]=0.1			, __magic_name__  :List[str]=224			, __magic_name__  :Any=1_000			, __magic_name__  :int=[3, 3, 6, 4]			, __magic_name__  :int=[48, 56, 112, 220]			, )      ->int:
										lowercase    :      Dict       = parent
										lowercase    :      List[str]       = batch_size
										lowercase    :      Union[str, Any]       = num_channels
										lowercase    :      Dict       = is_training
										lowercase    :      Any       = use_labels
										lowercase    :      Dict       = hidden_dropout_prob
										lowercase    :      Union[str, Any]       = attention_probs_dropout_prob
										lowercase    :      Optional[Any]       = num_labels
										lowercase    :      Optional[Any]       = image_size
										lowercase    :      Optional[int]       = layer_depths
										lowercase    :      Optional[int]       = embed_dims






						def      __snake_case      (		self  :Tuple				)      ->str:
										lowercase    :      Dict       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

										lowercase    :      List[Any]       = self.get_config()

										return config, pixel_values, labels






						def      __snake_case      (		self  :Dict				)      ->Union[str, Any]:
										return SwiftFormerConfig(
										    depths=self.layer_depths			, embed_dims=self.embed_dims			, mlp_ratio=4			, downsamples=[True, True, True, True]			, hidden_act="""gelu"""			, num_labels=self.num_labels			, down_patch_size=3			, down_stride=2			, down_pad=1			, drop_rate=0.0			, drop_path_rate=0.0			, use_layer_scale=__magic_name__			, layer_scale_init_value=1E-5			, )






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Union[str, Any]			, __magic_name__  :Dict			, __magic_name__  :Dict				)      ->Optional[Any]:
										lowercase    :      Optional[int]       = SwiftFormerModel(config=__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.embed_dims[-1], 7, 7)				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any			, __magic_name__  :Tuple			, __magic_name__  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = self.num_labels
										lowercase    :      List[str]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      List[Any]       = model(__magic_name__			, labels=__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)

										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()

										lowercase    :      Tuple       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										((lowercase)      ,      (lowercase)      ,      (lowercase))    :      Optional[Any]       = self.prepare_config_and_inputs()
										lowercase    :      Any       = {"""pixel_values""": pixel_values}
										return config, inputs_dict


@require_torch
class      UpperCamelCase		(__snake_case						,						__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[str]												=		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE  : Any												=		(
						    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : List[str]												=		False
						_SCREAMING_SNAKE_CASE  : Tuple												=		False
						_SCREAMING_SNAKE_CASE  : List[Any]												=		False






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = SwiftFormerModelTester(self				)
										lowercase    :      List[str]       = ConfigTester(
										    self			, config_class=__magic_name__			, has_text_modality=__magic_name__			, hidden_size=37			, num_attention_heads=12			, num_hidden_layers=12			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Union[str, Any]:
										self.config_tester.run_common_tests()






						@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""				)
						def      __snake_case      (		self  :Any				)      ->Any:
										pass






						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Dict       = model_class(__magic_name__				)
														lowercase    :      int       = model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__magic_name__			, nn.Linear				)				)






						def      __snake_case      (		self  :Any				)      ->List[str]:
										lowercase      ,      lowercase    :      int       = self.model_tester.prepare_config_and_inputs_for_common()

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

														lowercase    :      Any       = ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]			, __magic_name__				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->List[str]:
										lowercase    :      Any       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__magic_name__				)






						@slow
						def      __snake_case      (		self  :Any				)      ->Dict:
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase    :      List[Any]       = SwiftFormerModel.from_pretrained(__magic_name__				)
														self.assertIsNotNone(__magic_name__				)






						@unittest.skip(reason="""SwiftFormer does not output attentions"""				)
						def      __snake_case      (		self  :List[str]				)      ->Optional[Any]:
										pass






						def      __snake_case      (		self  :List[str]				)      ->List[str]:
										def check_hidden_states_output(__magic_name__  :str			, __magic_name__  :Optional[Any]			, __magic_name__  :Tuple				):
														lowercase    :      Optional[Any]       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()

														with torch.no_grad():
																		lowercase    :      Dict       = model(**self._prepare_for_class(__magic_name__			, __magic_name__				)				)

														lowercase    :      Tuple       = outputs.hidden_states

														lowercase    :      List[str]       = 8
														self.assertEqual(len(__magic_name__				)			, __magic_name__				)  # TODO

														# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
														# with the width and height being successively divided by 2, after every 2 blocks
														for i in range(len(__magic_name__				)				):
																		self.assertEqual(
																		    hidden_states[i].shape			, torch.Size(
																		        [
																		            self.model_tester.batch_size,
																		            self.model_tester.embed_dims[i // 2],
																		            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																		            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																		        ]				)			, )

										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Optional[int]       = True
														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)

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

														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										def _config_zero_init(__magic_name__  :List[Any]				):
														lowercase    :      Tuple       = copy.deepcopy(__magic_name__				)
														for key in configs_no_init.__dict__.keys():
																		if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																						setattr(__magic_name__			, __magic_name__			, 1E-10				)
																		if isinstance(getattr(__magic_name__			, __magic_name__			, __magic_name__				)			, __magic_name__				):
																						lowercase    :      Any       = _config_zero_init(getattr(__magic_name__			, __magic_name__				)				)
																						setattr(__magic_name__			, __magic_name__			, __magic_name__				)
														return configs_no_init

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

										lowercase    :      int       = _config_zero_init(__magic_name__				)
										for model_class in self.all_model_classes:
														lowercase    :      Union[str, Any]       = model_class(config=__magic_name__				)
														for name, param in model.named_parameters():
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1E9) / 1E9).round().item()			, [0.0, 1.0]			, msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			, )






						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
						def      __snake_case      (		self  :Any				)      ->Any:
										pass


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      int       = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image


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






						@cached_property
						def      __snake_case      (		self  :Optional[Any]				)      ->List[str]:
										return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""				) if is_vision_available() else None






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""				).to(__magic_name__				)

										lowercase    :      Any       = self.default_image_processor
										lowercase    :      str       = prepare_img()
										lowercase    :      Tuple       = image_processor(images=__magic_name__			, return_tensors="""pt"""				).to(__magic_name__				)

										# forward pass
										with torch.no_grad():
														lowercase    :      Union[str, Any]       = model(**__magic_name__				)

										# verify the logits
										lowercase    :      Optional[int]       = torch.Size((1, 1_000)				)
										self.assertEqual(outputs.logits.shape			, __magic_name__				)

										lowercase    :      Optional[Any]       = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]				).to(__magic_name__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			, __magic_name__			, atol=1E-4				)				)
