


"""simple docstring"""


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    old_name

			if "patch_embed" in old_name:
						__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    old_name.split("."   )

						if layer == "0":
									__lowerCAmelCase       					=    old_name.replace("0"					,				"convolution1"   )
						elif layer == "1":
									__lowerCAmelCase       					=    old_name.replace("1"					,				"batchnorm_before"   )
						elif layer == "3":
									__lowerCAmelCase       					=    old_name.replace("3"					,				"convolution2"   )
						else:
									__lowerCAmelCase       					=    old_name.replace("4"					,				"batchnorm_after"   )

			if "network" in old_name and re.search(R"\d\.\d"					,				_UpperCamelCase   ):
						__lowerCAmelCase       					=    R"\b\d{2}\b"
						if bool(re.search(_UpperCamelCase					,				_UpperCamelCase   )   ):
									__lowerCAmelCase       					=    re.search(R"\d\.\d\d."					,				_UpperCamelCase   ).group()
						else:
									__lowerCAmelCase       					=    re.search(R"\d\.\d."					,				_UpperCamelCase   ).group()
						if int(match[0]   ) < 6:
									__lowerCAmelCase       					=    old_name.replace(_UpperCamelCase					,				""   )
									__lowerCAmelCase       					=    trimmed_name.replace("network"					,				match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
									__lowerCAmelCase       					=    "intermediate_stages." + trimmed_name
						else:
									__lowerCAmelCase       					=    old_name.replace(_UpperCamelCase					,				""   )
									if int(match[2]   ) < num_meta4D_last_stage:
												__lowerCAmelCase       					=    trimmed_name.replace("network"					,				"meta4D_layers.blocks." + match[2]   )
									else:
												__lowerCAmelCase       					=    str(int(match[2]   ) - num_meta4D_last_stage   )
												__lowerCAmelCase       					=    trimmed_name.replace("network"					,				"meta3D_layers.blocks." + layer_index   )
												if "norm1" in old_name:
															__lowerCAmelCase       					=    trimmed_name.replace("norm1"					,				"layernorm1"   )
												elif "norm2" in old_name:
															__lowerCAmelCase       					=    trimmed_name.replace("norm2"					,				"layernorm2"   )
												elif "fc1" in old_name:
															__lowerCAmelCase       					=    trimmed_name.replace("fc1"					,				"linear_in"   )
												elif "fc2" in old_name:
															__lowerCAmelCase       					=    trimmed_name.replace("fc2"					,				"linear_out"   )

									__lowerCAmelCase       					=    "last_stage." + trimmed_name

			elif "network" in old_name and re.search(R".\d."					,				_UpperCamelCase   ):
						__lowerCAmelCase       					=    old_name.replace("network"					,				"intermediate_stages"   )

			if "fc" in new_name:
						__lowerCAmelCase       					=    new_name.replace("fc"					,				"convolution"   )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						__lowerCAmelCase       					=    new_name.replace("norm1"					,				"batchnorm_before"   )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						__lowerCAmelCase       					=    new_name.replace("norm2"					,				"batchnorm_after"   )
			if "proj" in new_name:
						__lowerCAmelCase       					=    new_name.replace("proj"					,				"projection"   )
			if "dist_head" in new_name:
						__lowerCAmelCase       					=    new_name.replace("dist_head"					,				"distillation_classifier"   )
			elif "head" in new_name:
						__lowerCAmelCase       					=    new_name.replace("head"					,				"classifier"   )
			elif "patch_embed" in new_name:
						__lowerCAmelCase       					=    "efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						__lowerCAmelCase       					=    new_name.replace("norm"					,				"layernorm"   )
						__lowerCAmelCase       					=    "efficientformer." + new_name
			else:
						__lowerCAmelCase       					=    "efficientformer.encoder." + new_name

			return new_name


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			for key in checkpoint.copy().keys():
						__lowerCAmelCase       					=    checkpoint.pop(_UpperCamelCase   )
						__lowerCAmelCase       					=    val

			return checkpoint


def _lowerCamelCase			(	):

			'''simple docstring'''




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

			return image


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )["model"]
			__lowerCAmelCase       					=    EfficientFormerConfig.from_json_file(_UpperCamelCase   )
			__lowerCAmelCase       					=    EfficientFormerForImageClassificationWithTeacher(_UpperCamelCase   )
			__lowerCAmelCase       					=    "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

			__lowerCAmelCase       					=    config.depths[-1] - config.num_metaad_blocks + 1
			__lowerCAmelCase       					=    convert_torch_checkpoint(_UpperCamelCase					,				_UpperCamelCase   )

			model.load_state_dict(_UpperCamelCase   )
			model.eval()

			__lowerCAmelCase       					=    {
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			# prepare image
			__lowerCAmelCase       					=    prepare_img()
			__lowerCAmelCase       					=    256
			__lowerCAmelCase       					=    224
			__lowerCAmelCase       					=    EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}					,				crop_size={"height": crop_size, "width": crop_size}					,				resample=pillow_resamplings["bicubic"]					,				)
			__lowerCAmelCase       					=    processor(images=_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			# original processing pipeline
			__lowerCAmelCase       					=    Compose(
			    [
			        Resize(_UpperCamelCase					,				interpolation=pillow_resamplings["bicubic"]   ),
			        CenterCrop(_UpperCamelCase   ),
			        ToTensor(),
			        Normalize(_UpperCamelCase					,				_UpperCamelCase   ),
			    ]   )
			__lowerCAmelCase       					=    image_transforms(_UpperCamelCase   ).unsqueeze(0   )

			assert torch.allclose(_UpperCamelCase					,				_UpperCamelCase   )

			__lowerCAmelCase       					=    model(_UpperCamelCase   )
			__lowerCAmelCase       					=    outputs.logits

			__lowerCAmelCase       					=    (1, 1000)

			if "l1" in model_name:
						__lowerCAmelCase       					=    torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]   )
						assert torch.allclose(logits[0, :10]					,				_UpperCamelCase					,				atol=1e-3   )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__lowerCAmelCase       					=    torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]   )
						assert torch.allclose(logits[0, :10]					,				_UpperCamelCase					,				atol=1e-3   )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__lowerCAmelCase       					=    torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]   )
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"   )

			# Save Checkpoints
			Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
			model.save_pretrained(_UpperCamelCase   )
			print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )
			processor.save_pretrained(_UpperCamelCase   )
			print(f"Processor successfuly saved at {pytorch_dump_path}"   )

			if push_to_hub:
						print("Pushing model to the hub..."   )

						model.push_to_hub(
						    repo_id=f"Bearnardd/{pytorch_dump_path}"					,				commit_message="Add model"					,				use_temp_dir=_UpperCamelCase					,				)
						processor.push_to_hub(
						    repo_id=f"Bearnardd/{pytorch_dump_path}"					,				commit_message="Add image processor"					,				use_temp_dir=_UpperCamelCase					,				)


if __name__ == "__main__":
	A  :     Optional[int]    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--pytorch_model_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path to EfficientFormer pytorch checkpoint.",
	)
	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The json file for EfficientFormer model config.",
	)
	parser.add_argument(
	    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)

	parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
	parser.add_argument(
	    "--no-push_to_hub",
	    dest="push_to_hub",
	    action="store_false",
	    help="Do not push model and image processor to the hub",
	)
	parser.set_defaults(push_to_hub=True)

	A  :     Tuple    					=    parser.parse_args()
	convert_efficientformer_checkpoint(
	    checkpoint_path=args.pytorch_model_path,
	    efficientformer_config_file=args.config_file,
	    pytorch_dump_path=args.pytorch_dump_path,
	    push_to_hub=args.push_to_hub,
	)








"""simple docstring"""


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

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402


A  :     Dict    					=    {
    "return_dict": False,
    "output_hidden_states": True,
    "output_attentions": True,
    "torchscript": True,
    "torch_dtype": "float16",
    "use_bfloat16": True,
    "tf_legacy_loss": True,
    "pruned_heads": {"a": 1},
    "tie_word_embeddings": False,
    "is_decoder": True,
    "cross_attention_hidden_size": 1_2_8,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 5_0,
    "min_length": 3,
    "do_sample": True,
    "early_stopping": True,
    "num_beams": 3,
    "num_beam_groups": 3,
    "diversity_penalty": 0.5,
    "temperature": 2.0,
    "top_k": 1_0,
    "top_p": 0.7,
    "typical_p": 0.2,
    "repetition_penalty": 0.8,
    "length_penalty": 0.8,
    "no_repeat_ngram_size": 5,
    "encoder_no_repeat_ngram_size": 5,
    "bad_words_ids": [1, 2, 3],
    "num_return_sequences": 3,
    "chunk_size_feed_forward": 5,
    "output_scores": True,
    "return_dict_in_generate": True,
    "forced_bos_token_id": 2,
    "forced_eos_token_id": 3,
    "remove_invalid_values": True,
    "architectures": ["BertModel"],
    "finetuning_task": "translation",
    "id2label": {0: "label"},
    "label2id": {"label": "0"},
    "tokenizer_class": "BertTokenizerFast",
    "prefix": "prefix",
    "bos_token_id": 6,
    "pad_token_id": 7,
    "eos_token_id": 8,
    "sep_token_id": 9,
    "decoder_start_token_id": 1_0,
    "exponential_decay_length_penalty": (5, 1.01),
    "suppress_tokens": [0, 1],
    "begin_suppress_tokens": 2,
    "task_specific_params": {"translation": "some_params"},
    "problem_type": "regression",
}
@is_staging_test
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@classmethod
							def     snake_case       (  cls			):
										__lowerCAmelCase       					=    TOKEN
										HfFolder.save_token(__a			)

							@classmethod
							def     snake_case       (  cls			):
										try:
													delete_repo(token=cls._token					,     repo_id="test-config"			)
										except HTTPError:
													pass

										try:
													delete_repo(token=cls._token					,     repo_id="valid_org/test-config-org"			)
										except HTTPError:
													pass

										try:
													delete_repo(token=cls._token					,     repo_id="test-dynamic-config"			)
										except HTTPError:
													pass

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    BertConfig(
										    vocab_size=99					,     hidden_size=32					,     num_hidden_layers=5					,     num_attention_heads=4					,     intermediate_size=37			)
										config.push_to_hub("test-config"					,     use_auth_token=self._token			)

										__lowerCAmelCase       					=    BertConfig.from_pretrained(f"{USER}/test-config"			)
										for k, v in config.to_dict().items():
													if k != "transformers_version":
																self.assertEqual(__a					,     getattr(__a					,     __a			)			)

        # Reset repo
										delete_repo(token=self._token					,     repo_id="test-config"			)

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(__a					,     repo_id="test-config"					,     push_to_hub=__a					,     use_auth_token=self._token			)

										__lowerCAmelCase       					=    BertConfig.from_pretrained(f"{USER}/test-config"			)
										for k, v in config.to_dict().items():
													if k != "transformers_version":
																self.assertEqual(__a					,     getattr(__a					,     __a			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    BertConfig(
										    vocab_size=99					,     hidden_size=32					,     num_hidden_layers=5					,     num_attention_heads=4					,     intermediate_size=37			)
										config.push_to_hub("valid_org/test-config-org"					,     use_auth_token=self._token			)

										__lowerCAmelCase       					=    BertConfig.from_pretrained("valid_org/test-config-org"			)
										for k, v in config.to_dict().items():
													if k != "transformers_version":
																self.assertEqual(__a					,     getattr(__a					,     __a			)			)

        # Reset repo
										delete_repo(token=self._token					,     repo_id="valid_org/test-config-org"			)

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(
													    __a					,     repo_id="valid_org/test-config-org"					,     push_to_hub=__a					,     use_auth_token=self._token			)

										__lowerCAmelCase       					=    BertConfig.from_pretrained("valid_org/test-config-org"			)
										for k, v in config.to_dict().items():
													if k != "transformers_version":
																self.assertEqual(__a					,     getattr(__a					,     __a			)			)



							def     snake_case       (  self			):
										CustomConfig.register_for_auto_class()
										__lowerCAmelCase       					=    CustomConfig(attribute=42			)

										config.push_to_hub("test-dynamic-config"					,     use_auth_token=self._token			)

										# This has added the proper auto_map field to the config
										self.assertDictEqual(config.auto_map					,     {"AutoConfig": "custom_configuration.CustomConfig"}			)

										__lowerCAmelCase       					=    AutoConfig.from_pretrained(f"{USER}/test-dynamic-config"					,     trust_remote_code=__a			)
										# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
										self.assertEqual(new_config.__class__.__name__					,     "CustomConfig"			)
										self.assertEqual(new_config.attribute					,     42			)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    GPTaConfig()

										# attempt to modify each of int/float/bool/str config records and verify they were updated
										__lowerCAmelCase       					=    c.n_embd + 1  # int
										__lowerCAmelCase       					=    c.resid_pdrop + 1.0  # float
										__lowerCAmelCase       					=    not c.scale_attn_weights  # bool
										__lowerCAmelCase       					=    c.summary_type + "foo"  # str
										c.update_from_string(
										    f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"			)
										self.assertEqual(__a					,     c.n_embd					,     "mismatch for key: n_embd"			)
										self.assertEqual(__a					,     c.resid_pdrop					,     "mismatch for key: resid_pdrop"			)
										self.assertEqual(__a					,     c.scale_attn_weights					,     "mismatch for key: scale_attn_weights"			)
										self.assertEqual(__a					,     c.summary_type					,     "mismatch for key: summary_type"			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    PretrainedConfig()
										__lowerCAmelCase       					=    [key for key in base_config.__dict__ if key not in config_common_kwargs]
										# If this part of the test fails, you have arguments to addin config_common_kwargs above.
										self.assertListEqual(
										    __a					,     ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]			)
										__lowerCAmelCase       					=    [key for key, value in config_common_kwargs.items() if value == getattr(__a					,     __a			)]
										if len(__a			) > 0:
													raise ValueError(
													    "The following keys are set with the default values in"
													    " `test_configuration_common.config_common_kwargs` pick another value for them:"
													    f" {', '.join(__a			)}."			)

							def     snake_case       (  self			):
										with self.assertRaises(__a			):
													# config is in subfolder, the following should not work without specifying the subfolder
													__lowerCAmelCase       					=    BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"			)

										__lowerCAmelCase       					=    BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"					,     subfolder="bert"			)

										self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										# A mock response for an HTTP head request to emulate server down
										__lowerCAmelCase       					=    mock.Mock()
										__lowerCAmelCase       					=    5_00
										__lowerCAmelCase       					=    {}
										__lowerCAmelCase       					=    HTTPError
										__lowerCAmelCase       					=    {}

										# Download this model to make sure it's in the cache.
										__lowerCAmelCase       					=    BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"			)

										# Under the mock environment we get a 500 error when trying to reach the model.
										with mock.patch("requests.Session.request"					,     return_value=__a			) as mock_head:
													__lowerCAmelCase       					=    BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"			)
													# This check we did call the fake head request
													mock_head.assert_called()

							def     snake_case       (  self			):
										# This test is for deprecated behavior and can be removed in v5
										__lowerCAmelCase       					=    BertConfig.from_pretrained(
										    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    AutoConfig.from_pretrained("bert-base-cased"			)
										__lowerCAmelCase       					=    ["config.4.0.0.json"]

										with tempfile.TemporaryDirectory() as tmp_dir:
													configuration.save_pretrained(__a			)
													__lowerCAmelCase       					=    2
													json.dump(configuration.to_dict()					,     open(os.path.join(__a					,     "config.4.0.0.json"			)					,     "w"			)			)

													# This should pick the new configuration file as the version of Transformers is > 4.0.0
													__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
													self.assertEqual(new_configuration.hidden_size					,     2			)

													# Will need to be adjusted if we reach v42 and this test is still here.
													# Should pick the old configuration file as the version of Transformers is < 4.42.0
													__lowerCAmelCase       					=    ["config.42.0.0.json"]
													__lowerCAmelCase       					=    7_68
													configuration.save_pretrained(__a			)
													shutil.move(os.path.join(__a					,     "config.4.0.0.json"			)					,     os.path.join(__a					,     "config.42.0.0.json"			)			)
													__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
													self.assertEqual(new_configuration.hidden_size					,     7_68			)



							def     snake_case       (  self			):
										# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
										__lowerCAmelCase       					=    "hf-internal-testing/test-two-configs"

										import transformers as new_transformers

										__lowerCAmelCase       					=    "v4.0.0"
										__lowerCAmelCase					,			__lowerCAmelCase       					=    new_transformers.models.auto.AutoConfig.from_pretrained(
										    __a					,     return_unused_kwargs=__a			)
										self.assertEqual(new_configuration.hidden_size					,     2			)
										# This checks `_configuration_file` ia not kept in the kwargs by mistake.
										self.assertDictEqual(__a					,     {}			)

										# Testing an older version by monkey-patching the version in the module it's used.
										import transformers as old_transformers

										__lowerCAmelCase       					=    "v3.0.0"
										__lowerCAmelCase       					=    old_transformers.models.auto.AutoConfig.from_pretrained(__a			)
										self.assertEqual(old_configuration.hidden_size					,     7_68			)




