



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roformer.modeling_flax_roformer import (
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
    )



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=99,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=512,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=4,			)     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	parent
          UpperCamelCase    :  Dict							=	batch_size
          UpperCamelCase    :  Optional[Any]							=	seq_length
          UpperCamelCase    :  Optional[Any]							=	is_training
          UpperCamelCase    :  List[Any]							=	use_attention_mask
          UpperCamelCase    :  List[Any]							=	use_token_type_ids
          UpperCamelCase    :  Any							=	use_labels
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[Any]							=	hidden_size
          UpperCamelCase    :  str							=	num_hidden_layers
          UpperCamelCase    :  int							=	num_attention_heads
          UpperCamelCase    :  Any							=	intermediate_size
          UpperCamelCase    :  Union[str, Any]							=	hidden_act
          UpperCamelCase    :  Any							=	hidden_dropout_prob
          UpperCamelCase    :  Union[str, Any]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[int]							=	max_position_embeddings
          UpperCamelCase    :  str							=	type_vocab_size
          UpperCamelCase    :  Union[str, Any]							=	type_sequence_label_size
          UpperCamelCase    :  Tuple							=	initializer_range
          UpperCamelCase    :  Optional[Any]							=	num_choices
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Any							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )

          UpperCamelCase    :  Union[str, Any]							=	None
          if self.use_attention_mask:
             UpperCamelCase    :  int							=	random_attention_mask([self.batch_size, self.seq_length]       )

          UpperCamelCase    :  Dict							=	None
          if self.use_token_type_ids:
             UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size       )

          UpperCamelCase    :  Dict							=	RoFormerConfig(
              vocab_size=self.vocab_size,			hidden_size=self.hidden_size,			num_hidden_layers=self.num_hidden_layers,			num_attention_heads=self.num_attention_heads,			intermediate_size=self.intermediate_size,			hidden_act=self.hidden_act,			hidden_dropout_prob=self.hidden_dropout_prob,			attention_probs_dropout_prob=self.attention_probs_dropout_prob,			max_position_embeddings=self.max_position_embeddings,			type_vocab_size=self.type_vocab_size,			is_decoder=SCREAMING_SNAKE_CASE_,			initializer_range=self.initializer_range,			)

          return config, input_ids, token_type_ids, attention_mask






       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Dict							=	self.prepare_config_and_inputs()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	config_and_inputs
          UpperCamelCase    :  Tuple							=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






@require_flax
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Union[str, Any]        =  True

       UpperCAmelCase__				:					Any        =  (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Union[str, Any]							=	FlaxRoFormerModelTester(self       )






       @slow
       def 					snake_case_       (			self       )     ->		str:
          for model_class_name in self.all_model_classes:
             UpperCamelCase    :  Optional[Any]							=	model_class_name.from_pretrained('junnyu/roformer_chinese_small',			from_pt=SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Any							=	model(np.ones((1, 1)       )       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






@require_flax
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  str							=	FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'       )
          UpperCamelCase    :  List[str]							=	jnp.array([[0, 1, 2, 3, 4, 5]]       )
          UpperCamelCase    :  int							=	model(SCREAMING_SNAKE_CASE_       )[0]

          UpperCamelCase    :  Dict							=	5_0000

          UpperCamelCase    :  Any							=	(1, 6, vocab_size)
          self.assertEqual(output.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	jnp.array(
              [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]       )

          self.assertTrue(jnp.allclose(output[:, :3, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-4       )       )








import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

__UpperCAmelCase          =       {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__UpperCAmelCase          =       {
    '''b0''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1_408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1_536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1_792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2_304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2_560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}
def 		UpperCamelCase	(   snake_case__       :    int	)	->     Optional[int]:
   UpperCamelCase    :  str							=	EfficientNetConfig()
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['hidden_dim']
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['width_coef']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['depth_coef']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['dropout_rate']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['dw_padding']

   UpperCamelCase    :  str							=	'huggingface/label-files'
   UpperCamelCase    :  Optional[Any]							=	'imagenet-1k-id2label.json'
   UpperCamelCase    :  Optional[Any]							=	1000
   UpperCamelCase    :  Dict							=	json.load(open(hf_hub_download(snake_case__       ,     snake_case__       ,     repo_type='dataset'	)       ,     'r'	)	)
   UpperCamelCase    :  Tuple							=	{int(snake_case__	): v for k, v in idalabel.items()}

   UpperCamelCase    :  Optional[int]							=	idalabel
   UpperCamelCase    :  Tuple							=	{v: k for k, v in idalabel.items()}
   return config
def 		UpperCamelCase	(   )	->     Tuple:
   UpperCamelCase    :  Optional[Any]							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  Union[str, Any]							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   return im
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     List[Any]:
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	EfficientNetImageProcessor(
       size={'height': size, 'width': size}       ,     image_mean=[0.485, 0.456, 0.406]       ,     image_std=[0.47853944, 0.4732864, 0.47434163]       ,     do_center_crop=snake_case__       ,     )
   return preprocessor
def 		UpperCamelCase	(   snake_case__       :    Optional[int]	)	->     Dict:
   UpperCamelCase    :  int							=	[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
   UpperCamelCase    :  str							=	sorted(set(snake_case__	)	)
   UpperCamelCase    :  int							=	len(snake_case__	)
   UpperCamelCase    :  str							=	{b: str(snake_case__	) for b, i in zip(snake_case__       ,     range(snake_case__	)	)}

   UpperCamelCase    :  Optional[int]							=	[]
   rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
   rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
   rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
   rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
   rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

   for b in block_names:
      UpperCamelCase    :  Union[str, Any]							=	block_name_mapping[b]
      rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")	)
      rename_keys.append(
          (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")	)
      rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")	)
      rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")	)

      rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")	)
      rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")	)
      rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")	)
      rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")	)

   rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
   rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
   rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
   rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
   rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

   UpperCamelCase    :  List[str]							=	{}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCamelCase    :  Dict							=	'efficientnet.' + item[1]

   UpperCamelCase    :  Dict							=	'classifier.weight'
   UpperCamelCase    :  Dict							=	'classifier.bias'
   return key_mapping
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    int	)	->     Dict:
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      UpperCamelCase    :  Any							=	key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	).permute(3       ,     2       ,     0       ,     1	)
      elif "depthwise_kernel" in key:
         UpperCamelCase    :  Any							=	torch.from_numpy(snake_case__	).permute(2       ,     3       ,     0       ,     1	)
      elif "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(np.transpose(snake_case__	)	)
      else:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	)

      # Replace HF parameters with original TF model parameters
      assert hf_params[hf_key].shape == new_hf_value.shape
      hf_params[hf_key].copy_(snake_case__	)
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    int       ,     snake_case__       :    Optional[int]	)	->     Any:
   UpperCamelCase    :  Union[str, Any]							=	model_classes[model_name](
       include_top=snake_case__       ,     weights='imagenet'       ,     input_tensor=snake_case__       ,     input_shape=snake_case__       ,     pooling=snake_case__       ,     classes=1000       ,     classifier_activation='softmax'       ,     )

   UpperCamelCase    :  Optional[int]							=	original_model.trainable_variables
   UpperCamelCase    :  Optional[int]							=	original_model.non_trainable_variables
   UpperCamelCase    :  Tuple							=	{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      UpperCamelCase    :  List[Any]							=	param.numpy()
   UpperCamelCase    :  List[str]							=	list(tf_params.keys()	)

   # Load HuggingFace model
   UpperCamelCase    :  str							=	get_efficientnet_config(snake_case__	)
   UpperCamelCase    :  Any							=	EfficientNetForImageClassification(snake_case__	).eval()
   UpperCamelCase    :  Tuple							=	hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('Converting parameters...'	)
   UpperCamelCase    :  List[Any]							=	rename_keys(snake_case__	)
   replace_params(snake_case__       ,     snake_case__       ,     snake_case__	)

   # Initialize preprocessor and preprocess input image
   UpperCamelCase    :  List[Any]							=	convert_image_processor(snake_case__	)
   UpperCamelCase    :  Dict							=	preprocessor(images=prepare_img()       ,     return_tensors='pt'	)

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCamelCase    :  Optional[int]							=	hf_model(**snake_case__	)
   UpperCamelCase    :  Dict							=	outputs.logits.detach().numpy()

   # Original model inference
   UpperCamelCase    :  Optional[int]							=	False
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[Any]							=	prepare_img().resize((image_size, image_size)       ,     resample=PIL.Image.NEAREST	)
   UpperCamelCase    :  List[Any]							=	image.img_to_array(snake_case__	)
   UpperCamelCase    :  str							=	np.expand_dims(snake_case__       ,     axis=0	)
   UpperCamelCase    :  Any							=	original_model.predict(snake_case__	)

   # Check whether original and HF model outputs match  -> np.allclose
   assert np.allclose(snake_case__       ,     snake_case__       ,     atol=1E-3	), "The predicted logits are not the same."
   print('Model outputs match!'	)

   if save_model:
      # Create folder to save model
      if not os.path.isdir(snake_case__	):
         os.mkdir(snake_case__	)
      # Save converted model and image processor
      hf_model.save_pretrained(snake_case__	)
      preprocessor.save_pretrained(snake_case__	)

   if push_to_hub:
      # Push model and image processor to hub
      print(F"""Pushing converted {model_name} to the hub..."""	)
      UpperCamelCase    :  List[str]							=	F"""efficientnet-{model_name}"""
      preprocessor.push_to_hub(snake_case__	)
      hf_model.push_to_hub(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''b0''',
        type=str,
        help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''hf_model''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

    __UpperCAmelCase          =       parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



