
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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= old_name

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

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

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

															SCREAMING_SNAKE_CASE__													= 'last_stage.' + trimmed_name

					elif "network" in old_name and re.search(r'.\d.'	,	UpperCamelCase_		):
										SCREAMING_SNAKE_CASE__													= old_name.replace('network'	,	'intermediate_stages'		)

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

					return new_name






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					for key in checkpoint.copy().keys():
										SCREAMING_SNAKE_CASE__													= checkpoint.pop(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= val

					return checkpoint






def 							_lowercase							(     )		->		Union[str, Any]:
					'''simple docstring'''


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

					return image






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


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

					SCREAMING_SNAKE_CASE__													= config.depths[-1] - config.num_metaad_blocks + 1
					SCREAMING_SNAKE_CASE__													= convert_torch_checkpoint(UpperCamelCase_	,	UpperCamelCase_		)

					model.load_state_dict(UpperCamelCase_		)
					model.eval()

					SCREAMING_SNAKE_CASE__													= {
					    'bilinear': PILImageResampling.BILINEAR,
					    'bicubic': PILImageResampling.BICUBIC,
					    'nearest': PILImageResampling.NEAREST,
					}

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

					# original processing pipeline
					SCREAMING_SNAKE_CASE__													= Compose(
					    [
					        Resize(UpperCamelCase_	,	interpolation=pillow_resamplings['bicubic']		),
					        CenterCrop(UpperCamelCase_		),
					        ToTensor(),
					        Normalize(UpperCamelCase_	,	UpperCamelCase_		),
					    ]		)
					SCREAMING_SNAKE_CASE__													= image_transforms(UpperCamelCase_		).unsqueeze(0		)

					assert torch.allclose(UpperCamelCase_	,	UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= model(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= outputs.logits

					SCREAMING_SNAKE_CASE__													= (1, 1000)

					if "l1" in model_name:
										SCREAMING_SNAKE_CASE__													= torch.Tensor(
										    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]		)
										assert torch.allclose(logits[0, :10]	,	UpperCamelCase_	,	atol=1e-3		)
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										SCREAMING_SNAKE_CASE__													= torch.Tensor(
										    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]		)
										assert torch.allclose(logits[0, :10]	,	UpperCamelCase_	,	atol=1e-3		)
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										SCREAMING_SNAKE_CASE__													= torch.Tensor(
										    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]		)
										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__":
				__snake_case								=  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)

				__snake_case								=  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,
				)







import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =GPTSanJapaneseTokenizer
	A__    :   str				       =False
	A__    :   int				       ={"""do_clean_text""": False, """add_prefix_space""": False}
	def      A_				(			self							:   Any							):
						super().setUp()

						# fmt: off
						SCREAMING_SNAKE_CASE__													= ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
						# fmt: on
						SCREAMING_SNAKE_CASE__													= {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
						SCREAMING_SNAKE_CASE__													= {'unk_token': '<unk>'}

						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['emoji_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
						with open(self.emoji_file       ,   'w'							) as emoji_writer:
											emoji_writer.write(json.dumps(UpperCAmelCase_							)							)
	def      A_				(			self							:   str       ,   **UpperCAmelCase_							:   Optional[Any]							):
						kwargs.update(self.special_tokens_map							)
						return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。 \nこんばんは、㔺界。😀'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。 \nこんばんは、世界。😀'
						return input_text, output_text
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.get_input_output_texts(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_       ,   clean_up_tokenization_spaces=UpperCAmelCase_							)
						return text, ids
	def      A_				(			self							:   str							):
						pass  # TODO add if relevant
	def      A_				(			self							:   Tuple							):
						pass  # TODO add if relevant
	def      A_				(			self							:   int							):
						pass  # TODO add if relevant
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。　こんばんは、㔺界。'
						SCREAMING_SNAKE_CASE__													= ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# Testing conversion to ids without special tokens
						SCREAMING_SNAKE_CASE__													= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# Testing conversion to ids with special tokens
						SCREAMING_SNAKE_CASE__													= tokens + [tokenizer.unk_token]
						SCREAMING_SNAKE_CASE__													= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、、、、世界。こんばんは、、、、世界。'
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。'
						SCREAMING_SNAKE_CASE__													= 'こんばんは、㔺界。😀'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。こんばんは、世界。😀'
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(prefix_text + input_text							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(''       ,   prefix_text=prefix_text + input_text							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   prefix_text=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。'
						SCREAMING_SNAKE_CASE__													= 'こんばんは、㔺界。😀'

						SCREAMING_SNAKE_CASE__													= len(tokenizer.encode(UpperCAmelCase_							)							) - 2
						SCREAMING_SNAKE_CASE__													= len(tokenizer.encode(UpperCAmelCase_							)							) - 2

						SCREAMING_SNAKE_CASE__													= [1] + [0] * (len_prefix + len_text + 1)
						SCREAMING_SNAKE_CASE__													= [1] * (len_prefix + len_text + 1) + [0]
						SCREAMING_SNAKE_CASE__													= [1] + [1] * (len_prefix) + [0] * (len_text + 1)

						SCREAMING_SNAKE_CASE__													= tokenizer(prefix_text + input_text							).token_type_ids
						SCREAMING_SNAKE_CASE__													= tokenizer(''       ,   prefix_text=prefix_text + input_text							).token_type_ids
						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_       ,   prefix_text=UpperCAmelCase_							).token_type_ids
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode('あンいワ'							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(''       ,   prefix_text='あンいワ'							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode('いワ'       ,   prefix_text='あン'							)

						self.assertEqual(tokenizer.decode(UpperCAmelCase_							)       ,   tokenizer.decode(UpperCAmelCase_							)							)
						self.assertEqual(tokenizer.decode(UpperCAmelCase_							)       ,   tokenizer.decode(UpperCAmelCase_							)							)
						self.assertNotEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertNotEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(x_token_a[1]       ,   x_token_a[-1]							)  # SEG token
						self.assertEqual(x_token_a[1]       ,   x_token_a[3]							)  # SEG token
	@slow
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						SCREAMING_SNAKE_CASE__													= [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_       ,   padding=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.batch_encode_plus(UpperCAmelCase_       ,   padding=UpperCAmelCase_							)

						# fmt: off
						SCREAMING_SNAKE_CASE__													= [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
						SCREAMING_SNAKE_CASE__													= [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
						SCREAMING_SNAKE_CASE__													= [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
						# fmt: on
						self.assertListEqual(x_token.input_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token.token_type_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token.attention_mask       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.input_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.token_type_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.attention_mask       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass





	def      A_				(			self							:   List[str]							):
						# tokenizer has no padding token
						pass





