

'''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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				old_name

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

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

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

   __lowercase													=				'''last_stage.''' + trimmed_name

 elif "network" in old_name and re.search(R'''.\d.'''  ,       A__   ):
  __lowercase													=				old_name.replace('''network'''  ,       '''intermediate_stages'''   )

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

 return new_name


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 for key in checkpoint.copy().keys():
  __lowercase													=				checkpoint.pop(A__   )
  __lowercase													=				val

 return checkpoint


def 	_A  (					):






 """simple docstring"""



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

 return image


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				config.depths[-1] - config.num_metaad_blocks + 1
 __lowercase													=				convert_torch_checkpoint(A__  ,       A__   )

 model.load_state_dict(A__   )
 model.eval()

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

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

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

 assert torch.allclose(A__  ,       A__   )

 __lowercase													=				model(A__   )
 __lowercase													=				outputs.logits

 __lowercase													=				(1, 1000)

 if "l1" in model_name:
  __lowercase													=				torch.Tensor(
      [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]   )
  assert torch.allclose(logits[0, :10]  ,       A__  ,       atol=1e-3   )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __lowercase													=				torch.Tensor(
      [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]   )
  assert torch.allclose(logits[0, :10]  ,       A__  ,       atol=1e-3   )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __lowercase													=				torch.Tensor(
      [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]   )
  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(A__   ).mkdir(exist_ok=A__   )
 model.save_pretrained(A__   )
 print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )
 processor.save_pretrained(A__   )
 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=A__  ,       )
  processor.push_to_hub(
      repo_id=F"Bearnardd/{pytorch_dump_path}"  ,       commit_message='''Add image processor'''  ,       use_temp_dir=A__  ,       )


if __name__ == "__main__":
    lowerCAmelCase__      		=			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)

    lowerCAmelCase__      		=			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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

