




"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Optional[int]     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Dict     							=							torch.device("""cpu""")



def     a__ (				)						->					Union[str, Any]:
			lowerCamelCase			      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase			      =					Image.open(requests.get(snake_case__       , stream=snake_case__				).raw				)
			return im



def     a__ (				snake_case__				)						->					Any:
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					Any:
			lowerCamelCase			      =					dct.pop(snake_case__				)
			lowerCamelCase			      =					val



def     a__ (				snake_case__				)						->					str:
			lowerCamelCase			      =					[]
			for k in state_dict.keys():
						lowerCamelCase			      =					k
						if ".pwconv" in k:
									lowerCamelCase			      =					k_new.replace(""".pwconv"""       , """.point_wise_conv"""				)
						if ".dwconv" in k:
									lowerCamelCase			      =					k_new.replace(""".dwconv"""       , """.depth_wise_conv"""				)
						if ".Proj." in k:
									lowerCamelCase			      =					k_new.replace(""".Proj."""       , """.proj."""				)
						if "patch_embed" in k_new:
									lowerCamelCase			      =					k_new.replace("""patch_embed"""       , """swiftformer.patch_embed.patch_embedding"""				)
						if "network" in k_new:
									lowerCamelCase			      =					k_new.split("""."""				)
									if ls[2].isdigit():
												lowerCamelCase			      =					"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]				)
									else:
												lowerCamelCase			      =					k_new.replace("""network"""       , """swiftformer.encoder.network"""				)
						rename_keys.append((k, k_new)				)
			return rename_keys



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCamelCase			      =					10_00
			lowerCamelCase			      =					"""huggingface/label-files"""
			lowerCamelCase			      =					"""imagenet-1k-id2label.json"""
			lowerCamelCase			      =					json.load(open(hf_hub_download(snake_case__       , snake_case__       , repo_type="""dataset"""				)       , """r"""				)				)
			lowerCamelCase			      =					{int(snake_case__				): v for k, v in idalabel.items()}
			lowerCamelCase			      =					idalabel
			lowerCamelCase			      =					{v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						lowerCamelCase			      =					[3, 3, 6, 4]
						lowerCamelCase			      =					[48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						lowerCamelCase			      =					[3, 3, 9, 6]
						lowerCamelCase			      =					[48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						lowerCamelCase			      =					[4, 3, 10, 5]
						lowerCamelCase			      =					[48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						lowerCamelCase			      =					[4, 4, 12, 6]
						lowerCamelCase			      =					[64, 1_28, 3_20, 5_12]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("""https"""				):
									lowerCamelCase			      =					torch.hub.load_state_dict_from_url(snake_case__       , map_location="""cpu"""       , check_hash=snake_case__				)
						else:
									lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			lowerCamelCase			      =					checkpoint

			lowerCamelCase			      =					create_rename_keys(snake_case__				)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(snake_case__       , snake_case__       , snake_case__				)

			# load HuggingFace model
			lowerCamelCase			      =					SwiftFormerForImageClassification(snake_case__				).eval()
			hf_model.load_state_dict(snake_case__				)

			# prepare test inputs
			lowerCamelCase			      =					prepare_img()
			lowerCamelCase			      =					ViTImageProcessor.from_pretrained("""preprocessor_config"""				)
			lowerCamelCase			      =					processor(images=snake_case__       , return_tensors="""pt"""				)

			# compare outputs from both models
			lowerCamelCase			      =					get_expected_output(snake_case__				)
			lowerCamelCase			      =					hf_model(inputs["""pixel_values"""]				).logits

			assert hf_logits.shape == torch.Size([1, 10_00]				)
			assert torch.allclose(hf_logits[0, 0:5]       , snake_case__       , atol=1E-3				)

			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'				)
			hf_model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--swiftformer_name""",
					    default="""swiftformer_xs""",
					    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
					    type=str,
					    help="""Name of the SwiftFormer model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""./converted_outputs/""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

					lowerCAmelCase							:			str     							=							parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



