





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__											= logging.get_logger(__name__)





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Optional[int]   ,			a__   :Dict   ,			a__   :Dict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			original_name.split("""."""       )[0]
							UpperCamelCase__				     =			key.split("""."""       )
							UpperCamelCase__				     =			int(key_list[key_list.index(a__       ) - 2]       )
							UpperCamelCase__				     =			int(key_list[key_list.index(a__       ) - 1]       )
							UpperCamelCase__				     =			orig_block_num - offset

							UpperCamelCase__				     =			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""   ,			f"""block.{new_block_num}.{layer_num}.{new_name}"""       )
							return key





def     _UpperCamelCase     (a__   :Tuple       ):






							"""simple docstring"""





							UpperCamelCase__				     =			OrderedDict()
							UpperCamelCase__     ,     UpperCamelCase__				     =			0, 0
							for key, value in state_dict.items():
														if key.startswith("""network"""       ):
																					UpperCamelCase__				     =			key.replace("""network"""   ,			"""poolformer.encoder"""       )
														if "proj" in key:
																					# Works for the first embedding as well as the internal embedding layers
																					if key.endswith("""bias"""       ) and "patch_embed" not in key:
																												patch_emb_offset += 1
																					UpperCamelCase__				     =			key[: key.find("""proj"""       )]
																					UpperCamelCase__				     =			key.replace(a__   ,			f"""patch_embeddings.{total_embed_found}."""       )
																					UpperCamelCase__				     =			key.replace("""proj"""   ,			"""projection"""       )
																					if key.endswith("""bias"""       ):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					UpperCamelCase__				     =			"""poolformer.encoder.""" + key
														if "mlp.fc1" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""mlp.fc1"""   ,			"""output.conv1"""       )
														if "mlp.fc2" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""mlp.fc2"""   ,			"""output.conv2"""       )
														if "norm1" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""norm1"""   ,			"""before_norm"""       )
														if "norm2" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""norm2"""   ,			"""after_norm"""       )
														if "layer_scale_1" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""layer_scale_1"""   ,			"""layer_scale_1"""       )
														if "layer_scale_2" in key:
																					UpperCamelCase__				     =			replace_key_with_offset(a__   ,			a__   ,			"""layer_scale_2"""   ,			"""layer_scale_2"""       )
														if "head" in key:
																					UpperCamelCase__				     =			key.replace("""head"""   ,			"""classifier"""       )
														UpperCamelCase__				     =			value
							return new_state_dict





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCamelCase__				     =			Image.open(requests.get(a__   ,			stream=a__       ).raw       )

							return image





@torch.no_grad()
def     _UpperCamelCase     (a__   :Dict   ,			a__   :Optional[Any]   ,			a__   :Tuple       ):






							"""simple docstring"""





							UpperCamelCase__				     =			PoolFormerConfig()

							# set attributes based on model_name
							UpperCamelCase__				     =			"""huggingface/label-files"""
							UpperCamelCase__				     =			model_name[-3:]
							UpperCamelCase__				     =			1000
							UpperCamelCase__				     =			"""imagenet-1k-id2label.json"""
							UpperCamelCase__				     =			(1, 1000)

							# set config attributes
							UpperCamelCase__				     =			json.load(open(hf_hub_download(a__   ,			a__   ,			repo_type="""dataset"""       )   ,			"""r"""       )       )
							UpperCamelCase__				     =			{int(a__       ): v for k, v in idalabel.items()}
							UpperCamelCase__				     =			idalabel
							UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}
							if size == "s12":
														UpperCamelCase__				     =			[2, 2, 6, 2]
														UpperCamelCase__				     =			[64, 128, 320, 512]
														UpperCamelCase__				     =			4.0
														UpperCamelCase__				     =			0.9
							elif size == "s24":
														UpperCamelCase__				     =			[4, 4, 12, 4]
														UpperCamelCase__				     =			[64, 128, 320, 512]
														UpperCamelCase__				     =			4.0
														UpperCamelCase__				     =			0.9
							elif size == "s36":
														UpperCamelCase__				     =			[6, 6, 18, 6]
														UpperCamelCase__				     =			[64, 128, 320, 512]
														UpperCamelCase__				     =			4.0
														UpperCamelCase__				     =			1e-6
														UpperCamelCase__				     =			0.9
							elif size == "m36":
														UpperCamelCase__				     =			[6, 6, 18, 6]
														UpperCamelCase__				     =			[96, 192, 384, 768]
														UpperCamelCase__				     =			4.0
														UpperCamelCase__				     =			1e-6
														UpperCamelCase__				     =			0.95
							elif size == "m48":
														UpperCamelCase__				     =			[8, 8, 24, 8]
														UpperCamelCase__				     =			[96, 192, 384, 768]
														UpperCamelCase__				     =			4.0
														UpperCamelCase__				     =			1e-6
														UpperCamelCase__				     =			0.95
							else:
														raise ValueError(f"""Size {size} not supported"""       )

							# load image processor
							UpperCamelCase__				     =			PoolFormerImageProcessor(crop_pct=a__       )

							# Prepare image
							UpperCamelCase__				     =			prepare_img()
							UpperCamelCase__				     =			image_processor(images=a__   ,			return_tensors="""pt"""       ).pixel_values

							logger.info(f"""Converting model {model_name}..."""       )

							# load original state dict
							UpperCamelCase__				     =			torch.load(a__   ,			map_location=torch.device("""cpu"""       )       )

							# rename keys
							UpperCamelCase__				     =			rename_keys(a__       )

							# create HuggingFace model and load state dict
							UpperCamelCase__				     =			PoolFormerForImageClassification(a__       )
							model.load_state_dict(a__       )
							model.eval()

							# Define image processor
							UpperCamelCase__				     =			PoolFormerImageProcessor(crop_pct=a__       )
							UpperCamelCase__				     =			image_processor(images=prepare_img()   ,			return_tensors="""pt"""       ).pixel_values

							# forward pass
							UpperCamelCase__				     =			model(a__       )
							UpperCamelCase__				     =			outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														UpperCamelCase__				     =			torch.tensor([-0.3045, -0.6758, -0.4869]       )
							elif size == "s24":
														UpperCamelCase__				     =			torch.tensor([0.4402, -0.1374, -0.8045]       )
							elif size == "s36":
														UpperCamelCase__				     =			torch.tensor([-0.6080, -0.5133, -0.5898]       )
							elif size == "m36":
														UpperCamelCase__				     =			torch.tensor([0.3952, 0.2263, -1.2668]       )
							elif size == "m48":
														UpperCamelCase__				     =			torch.tensor([0.1167, -0.0656, -0.3423]       )
							else:
														raise ValueError(f"""Size {size} not supported"""       )

							# verify logits
							assert logits.shape == expected_shape
							assert torch.allclose(logits[0, :3]   ,			a__   ,			atol=1e-2       )

							# finally, save model and image processor
							logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
							Path(a__       ).mkdir(exist_ok=a__       )
							model.save_pretrained(a__       )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
							image_processor.save_pretrained(a__       )


if __name__ == "__main__":
		UpperCamelCase__											= argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    default="poolformer_s12",
		    type=str,
		    help="Name of the model you\'d like to convert.",
		)
		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		UpperCamelCase__											= parser.parse_args()
		convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       MODEL_FOR_CAUSAL_LM_MAPPING
							snake_case     :      List[str]						     =       TF_MODEL_FOR_CAUSAL_LM_MAPPING






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""pt"""  )
														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                """ oscope. FiliFili@@"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                    """ oscope. FiliFili@@"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
														                    """ oscope. oscope. FiliFili@@"""
														                )
														            }
														        ],
														    ]  , )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , return_tensors=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														    ]  , )
														UpperCamelCase__				     =			text_generator.model.config.eos_token_id
														UpperCamelCase__				     =			"""<pad>"""
														UpperCamelCase__				     =			text_generator(
														    ["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , batch_size=2  , return_tensors=__lowerCAmelCase  , )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														    ]  , )






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""tf"""  )

														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                """ please,"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                    """ please,"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
														                    """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
														                )
														            }
														        ],
														    ]  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TextGenerationPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														return text_generator, ["This is a test", "Another test"]






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello I believe in"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]  , )

														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , stop_sequence=""" fe"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe"""}]  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			text_generator.model
														UpperCamelCase__				     =			text_generator.tokenizer

														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , return_full_text=__lowerCAmelCase  )
														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

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

														if text_generator.tokenizer.pad_token is not None:
																					UpperCamelCase__				     =			text_generator(
																					    ["""This is great !""", """Something else"""]  , num_return_sequences=2  , batch_size=2  , do_sample=__lowerCAmelCase  )
																					self.assertEqual(
																					    __lowerCAmelCase  , [
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					    ]  , )

														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_text=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )

														# Empty prompt is slighly special
														# it requires BOS token to exist.
														# Special case for Pegasus which will always append EOS so will
														# work even without BOS.
														if (
														    text_generator.tokenizer.bos_token_id is not None
														    or "Pegasus" in tokenizer.__class__.__name__
														    or "Git" in model.__class__.__name__
														):
																					UpperCamelCase__				     =			text_generator(""""""  )
																					self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														else:
																					with self.assertRaises((ValueError, AssertionError)  ):
																												UpperCamelCase__				     =			text_generator(""""""  )

														if text_generator.framework == "tf":
																					# TF generation does not support max_new_tokens, and it's impossible
																					# to control long generation with only max_length without
																					# fancy calculation, dismissing tests for now.
																					return
														# We don't care about infinite range models.
														# They already work.
														# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
														UpperCamelCase__				     =			["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
														if (
														    tokenizer.model_max_length < 10000
														    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
														):
																					# Handling of large generations
																					with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)  ):
																												text_generator("""This is a test""" * 500  , max_new_tokens=20  )

																					UpperCamelCase__				     =			text_generator("""This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=20  )
																					# Hole strategy cannot work
																					with self.assertRaises(__lowerCAmelCase  ):
																												text_generator(
																												    """This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=tokenizer.model_max_length + 10  , )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														# Classic `model_kwargs`
														UpperCamelCase__				     =			pipeline(
														    model="""hf-internal-testing/tiny-random-bloom"""  , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}  , )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.bfloataa  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.floataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )






							@require_torch
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device=0  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  , do_sample=__lowerCAmelCase  , top_p=0.5  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello world"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														if text_generator.model.framework == "tf":
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.tf_utils"""  )
														else:
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.utils"""  )
														UpperCamelCase__				     =			"""Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

														# Both are set by the user -> log warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  , max_new_tokens=1  )
														self.assertIn(__lowerCAmelCase  , cl.out  )

														# The user only sets one -> no warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_new_tokens=1  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )

														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )




