
"""simple docstring"""



import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class       lowerCAmelCase__				(						unittest.TestCase	):







     def __init__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			List[Any]=13    ,      _lowerCamelCase			:			List[str]=30    ,      _lowerCamelCase			:			List[str]=2    ,      _lowerCamelCase			:			Union[str, Any]=3    ,      _lowerCamelCase			:			List[Any]=True    ,      _lowerCamelCase			:			str=True    ,      _lowerCamelCase			:			str=32    ,      _lowerCamelCase			:			List[Any]=5    ,      _lowerCamelCase			:			Union[str, Any]=4    ,      _lowerCamelCase			:			str=37    ,      _lowerCamelCase			:			Dict="gelu"    ,      _lowerCamelCase			:			Union[str, Any]=0.1    ,      _lowerCamelCase			:			str=0.1    ,      _lowerCamelCase			:			List[Any]=10    ,      _lowerCamelCase			:			str=0.0_2    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       patch_size
       _snake_case					     =       num_channels
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_act
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       type_sequence_label_size
       _snake_case					     =       initializer_range

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       _snake_case					     =       (image_size // patch_size) ** 2
       _snake_case					     =       num_patches + 1







     def      lowercase	(							self			:			str			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _snake_case					     =       ViTConfig(
           image_size=self.image_size    ,      patch_size=self.patch_size    ,      num_channels=self.num_channels    ,      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    ,      is_decoder=_lowerCamelCase    ,      initializer_range=self.initializer_range    ,      )

       return config, pixel_values







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			List[str]			):
       _snake_case					     =       FlaxViTModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       _snake_case					     =       (self.image_size, self.image_size)
       _snake_case					     =       (self.patch_size, self.patch_size)
       _snake_case					     =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, num_patches + 1, self.hidden_size)			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       self.type_sequence_label_size
       _snake_case					     =       FlaxViTForImageClassification(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       _snake_case					     =       1
       _snake_case					     =       FlaxViTForImageClassification(_lowerCamelCase			)

       _snake_case					     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       _snake_case					     =       model(_lowerCamelCase			)







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.prepare_config_and_inputs()
       (
           (
           _snake_case
       )     ,					(
           _snake_case
       )     ,					
       )					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_flax
class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







     def      lowercase	(							self			:			str			):
       _snake_case					     =       FlaxViTModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase    ,      hidden_size=37			)







     def      lowercase	(							self			:			List[Any]			):
       self.config_tester.run_common_tests()







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       inspect.signature(model.__call__			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _snake_case					     =       [*signature.parameters.keys()]

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
           _snake_case					     =       self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)
           _snake_case					     =       model_class(_lowerCamelCase			)

           @jax.jit
           def model_jitted(_lowerCamelCase			:			Optional[int]    ,      **_lowerCamelCase			:			Union[str, Any]			):
             return model(pixel_values=_lowerCamelCase    ,      **_lowerCamelCase			)

           with self.subTest('''JIT Enabled'''			):
             _snake_case					     =       model_jitted(**_lowerCamelCase			).to_tuple()

           with self.subTest('''JIT Disabled'''			):
             with jax.disable_jit():
               _snake_case					     =       model_jitted(**_lowerCamelCase			).to_tuple()

           self.assertEqual(len(_lowerCamelCase			)    ,      len(_lowerCamelCase			)			)
           for jitted_output, output in zip(_lowerCamelCase    ,      _lowerCamelCase			):
             self.assertEqual(jitted_output.shape    ,      output.shape			)







     @slow
     def      lowercase	(							self			:			Any			):
       for model_class_name in self.all_model_classes:
         _snake_case					     =       model_class_name.from_pretrained('''google/vit-base-patch16-224'''			)
         _snake_case					     =       model(np.ones((1, 3, 224, 224)			)			)
         self.assertIsNotNone(_lowerCamelCase			)





"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser(
     description=(
         'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
         ' Distillation'
     )
 )
 parser.add_argument('--model_type', default='bert', choices=['bert'])
 parser.add_argument('--model_name', default='bert-base-uncased', type=str)
 parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
 parser.add_argument('--vocab_transform', action='store_true')
 UpperCAmelCase__          = parser.parse_args()

 if args.model_type == "bert":
  UpperCAmelCase__          = BertForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase__          = 'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

 for w in ["word_embeddings", "position_embeddings"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.{w}.weight"]
 for w in ["weight", "bias"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

 UpperCAmelCase__          = 0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

 UpperCAmelCase__          = state_dict['cls.predictions.decoder.weight']
 UpperCAmelCase__          = state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.dense.{w}"]
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

 print(F"N layers selected for distillation: {std_idx}")
 print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

 print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
 torch.save(compressed_sd, args.dump_checkpoint)



