




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


lowerCamelCase							:						Any					       =  "hf-internal-testing/tiny-random-bert"
lowerCamelCase							:						Optional[int]					       =  os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
lowerCamelCase							:						str					       =  "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    List[Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       cached_file(A_		,				A_    )
       # Should have downloaded the file in here
       self.assertTrue(os.path.isdir(A_    )    )
       # Cache should contain at least those three subfolders:
       for subfolder in ["blobs", "refs", "snapshots"]:
             self.assertTrue(os.path.isdir(os.path.join(A_		,				A_    )    )    )
       with open(os.path.join(A_		,				'refs'		,				'main'    )    ) as f:
             lowerCamelCase_							=       f.read()
       self.assertEqual(A_		,				os.path.join(A_		,				'snapshots'		,				A_		,				A_    )    )
       self.assertTrue(os.path.isfile(A_    )    )

       # File is cached at the same place the second time.
       lowerCamelCase_							=       cached_file(A_		,				A_    )
       self.assertEqual(A_		,				A_    )

       # Using a specific revision to test the full commit hash.
       lowerCamelCase_							=       cached_file(A_		,				A_		,				revision='9b8c223'    )
       self.assertEqual(A_		,				os.path.join(A_		,				'snapshots'		,				A_		,				A_    )    )

 def 							a__					(							self   :    Tuple    )  ->      str:

       """simple docstring"""






       with self.assertRaisesRegex(A_		,				'is not a valid model identifier'    ):
             lowerCamelCase_							=       cached_file('tiny-random-bert'		,				A_    )

       with self.assertRaisesRegex(A_		,				'is not a valid git identifier'    ):
             lowerCamelCase_							=       cached_file(A_		,				A_		,				revision='aaaa'    )

       with self.assertRaisesRegex(A_		,				'does not appear to have a file named'    ):
             lowerCamelCase_							=       cached_file(A_		,				'conf'    )

 def 							a__					(							self   :    Dict    )  ->      str:

       """simple docstring"""






       with self.assertRaisesRegex(A_		,				'does not appear to have a file named'    ):
             lowerCamelCase_							=       cached_file(A_		,				'conf'    )

       with open(os.path.join(A_		,				'refs'		,				'main'    )    ) as f:
             lowerCamelCase_							=       f.read()
       self.assertTrue(os.path.isfile(os.path.join(A_		,				'.no_exist'		,				A_		,				'conf'    )    )    )

       lowerCamelCase_							=       cached_file(A_		,				'conf'		,				_raise_exceptions_for_missing_entries=A_    )
       self.assertIsNone(A_    )

       lowerCamelCase_							=       cached_file(A_		,				'conf'		,				local_files_only=A_		,				_raise_exceptions_for_missing_entries=A_    )
       self.assertIsNone(A_    )

       lowerCamelCase_							=       mock.Mock()
       lowerCamelCase_							=       500
       lowerCamelCase_							=       {}
       lowerCamelCase_							=       HTTPError
       lowerCamelCase_							=       {}

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch('requests.Session.request'		,				return_value=A_    ) as mock_head:
             lowerCamelCase_							=       cached_file(A_		,				'conf'		,				_raise_exceptions_for_connection_errors=A_    )
             self.assertIsNone(A_    )
             # This check we did call the fake head request
             mock_head.assert_called()

 def 							a__					(							self   :    List[str]    )  ->      Optional[Any]:

       """simple docstring"""






       self.assertTrue(has_file('hf-internal-testing/tiny-bert-pt-only'		,				A_    )    )
       self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'		,				A_    )    )
       self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'		,				A_    )    )

 def 							a__					(							self   :    Dict    )  ->      List[str]:

       """simple docstring"""






       self.assertIsNone(get_file_from_repo('bert-base-cased'		,				'ahah.txt'    )    )

       # The function raises if the repository does not exist.
       with self.assertRaisesRegex(A_		,				'is not a valid model identifier'    ):
             get_file_from_repo('bert-base-case'		,				A_    )

       # The function raises if the revision does not exist.
       with self.assertRaisesRegex(A_		,				'is not a valid git identifier'    ):
             get_file_from_repo('bert-base-cased'		,				A_		,				revision='ahaha'    )

       lowerCamelCase_							=       get_file_from_repo('bert-base-cased'		,				A_    )
       # The name is the cached name which is not very easy to test, so instead we load the content.
       lowerCamelCase_							=       json.loads(open(A_		,				'r'    ).read()    )
       self.assertEqual(config['hidden_size']		,				768    )





 def 							a__					(							self   :    Dict    )  ->      List[str]:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmp_dir:
             lowerCamelCase_							=       Path(A_    ) / 'a.txt'
             filename.touch()
             self.assertEqual(get_file_from_repo(A_		,				'a.txt'    )		,				str(A_    )    )

             self.assertIsNone(get_file_from_repo(A_		,				'b.txt'    )    )





"""simple docstring"""





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()
_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    original_name.split('''.'''      )[0]
					_UpperCamelCase									=    key.split('''.'''      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 2]      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 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  lowerCamelCase__						(     __snake_case      )				->						str:


					"""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(__snake_case,	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(__snake_case,	__snake_case,	'''mlp.fc1''',	'''output.conv1'''      )
										if "mlp.fc2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''mlp.fc2''',	'''output.conv2'''      )
										if "norm1" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''norm1''',	'''before_norm'''      )
										if "norm2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''norm2''',	'''after_norm'''      )
										if "layer_scale_1" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''layer_scale_1''',	'''layer_scale_1'''      )
										if "layer_scale_2" in key:
															_UpperCamelCase									=    replace_key_with_offset(__snake_case,	__snake_case,	'''layer_scale_2''',	'''layer_scale_2'''      )
										if "head" in key:
															_UpperCamelCase									=    key.replace('''head''',	'''classifier'''      )
										_UpperCamelCase									=    value
					return new_state_dict



def  lowerCamelCase__						(     )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase									=    Image.open(requests.get(__snake_case,	stream=__snake_case      ).raw      )

					return image



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    PoolFormerConfig()

					# set attributes based on model_name
					_UpperCamelCase									=    '''huggingface/label-files'''
					_UpperCamelCase									=    model_name[-3:]
					_UpperCamelCase									=    10_00
					_UpperCamelCase									=    '''imagenet-1k-id2label.json'''
					_UpperCamelCase									=    (1, 10_00)

					# set config attributes
					_UpperCamelCase									=    json.load(open(hf_hub_download(__snake_case,	__snake_case,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(__snake_case      ): 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, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s24":
										_UpperCamelCase									=    [4, 4, 12, 4]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.9
					elif size == "m36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					elif size == "m48":
										_UpperCamelCase									=    [8, 8, 24, 8]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					else:
										raise ValueError(F'''Size {size} not supported'''      )

					# load image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )

					# Prepare image
					_UpperCamelCase									=    prepare_img()
					_UpperCamelCase									=    image_processor(images=__snake_case,	return_tensors='''pt'''      ).pixel_values

					logger.info(F'''Converting model {model_name}...'''      )

					# load original state dict
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=torch.device('''cpu'''      )      )

					# rename keys
					_UpperCamelCase									=    rename_keys(__snake_case      )

					# create HuggingFace model and load state dict
					_UpperCamelCase									=    PoolFormerForImageClassification(__snake_case      )
					model.load_state_dict(__snake_case      )
					model.eval()

					# Define image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )
					_UpperCamelCase									=    image_processor(images=prepare_img(),	return_tensors='''pt'''      ).pixel_values

					# forward pass
					_UpperCamelCase									=    model(__snake_case      )
					_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],	__snake_case,	atol=1e-2      )

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


if __name__ == "__main__":
				_a								=						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."""
				)
				_a								=						parser.parse_args()
				convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






