



"""simple docstring"""
import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
      import os

      from flax.core.frozen_dict import unfreeze
      from flax.traverse_util import flatten_dict

      from transformers import FlaxBertModel

      A:   List[Any]								=		"0.12"  # assumed parallelism: 8



@require_flax
@is_staging_test
class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   @classmethod
   def 	SCREAMING_SNAKE_CASE     (   cls						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	TOKEN
    HfFolder.save_token(_SCREAMING_SNAKE_CASE						)

   @classmethod
   def 	SCREAMING_SNAKE_CASE     (   cls						)  ->      Dict:





    '''simple docstring'''




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

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	BertConfig(
        vocab_size=99      ,     hidden_size=32      ,     num_hidden_layers=5      ,     num_attention_heads=4      ,     intermediate_size=37						)
    UpperCAmelCase	:					Dict	=	FlaxBertModel(_SCREAMING_SNAKE_CASE						)
    model.push_to_hub("""test-model-flax"""      ,     use_auth_token=self._token						)

    UpperCAmelCase	:					List[Any]	=	FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"						)

    UpperCAmelCase	:					Union[str, Any]	=	flatten_dict(unfreeze(model.params						)						)
    UpperCAmelCase	:					Any	=	flatten_dict(unfreeze(new_model.params						)						)

    for key in base_params.keys():
     UpperCAmelCase	:					Union[str, Any]	=	(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-3      ,     msg=F"{key} not identical"						)

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

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

    UpperCAmelCase	:					str	=	FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"						)

    UpperCAmelCase	:					int	=	flatten_dict(unfreeze(model.params						)						)
    UpperCAmelCase	:					Dict	=	flatten_dict(unfreeze(new_model.params						)						)

    for key in base_params.keys():
     UpperCAmelCase	:					Tuple	=	(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-3      ,     msg=F"{key} not identical"						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	BertConfig(
        vocab_size=99      ,     hidden_size=32      ,     num_hidden_layers=5      ,     num_attention_heads=4      ,     intermediate_size=37						)
    UpperCAmelCase	:					Optional[Any]	=	FlaxBertModel(_SCREAMING_SNAKE_CASE						)
    model.push_to_hub("""valid_org/test-model-flax-org"""      ,     use_auth_token=self._token						)

    UpperCAmelCase	:					List[str]	=	FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""						)

    UpperCAmelCase	:					str	=	flatten_dict(unfreeze(model.params						)						)
    UpperCAmelCase	:					Optional[Any]	=	flatten_dict(unfreeze(new_model.params						)						)

    for key in base_params.keys():
     UpperCAmelCase	:					List[Any]	=	(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-3      ,     msg=F"{key} not identical"						)

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

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

    UpperCAmelCase	:					Tuple	=	FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""						)

    UpperCAmelCase	:					Dict	=	flatten_dict(unfreeze(model.params						)						)
    UpperCAmelCase	:					int	=	flatten_dict(unfreeze(new_model.params						)						)

    for key in base_params.keys():
     UpperCAmelCase	:					Dict	=	(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-3      ,     msg=F"{key} not identical"						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict						):
 UpperCAmelCase	:					Any	=	True
 UpperCAmelCase	:					Dict	=	flatten_dict(modela.params						)
 UpperCAmelCase	:					int	=	flatten_dict(modela.params						)
 for key in flat_params_a.keys():
  if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]						)						) > 1e-4:
   UpperCAmelCase	:					Tuple	=	False

 return models_are_equal



@require_flax
class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""						)
    UpperCAmelCase	:					int	=	FlaxBertModel(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	"""bert"""
    with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)						)

     with self.assertRaises(_SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Optional[int]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Union[str, Any]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,     subfolder=_SCREAMING_SNAKE_CASE						)

    self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""						)
    UpperCAmelCase	:					Tuple	=	FlaxBertModel(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	"""bert"""
    with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)      ,     max_shard_size="""10KB"""						)

     with self.assertRaises(_SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Union[str, Any]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Optional[int]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,     subfolder=_SCREAMING_SNAKE_CASE						)

    self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	"""bert"""
    UpperCAmelCase	:					Union[str, Any]	=	"""hf-internal-testing/tiny-random-bert-subfolder"""

    with self.assertRaises(_SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[Any]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,     subfolder=_SCREAMING_SNAKE_CASE						)

    self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	"""bert"""
    UpperCAmelCase	:					Dict	=	"""hf-internal-testing/tiny-random-bert-sharded-subfolder"""
    with self.assertRaises(_SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Union[str, Any]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,     subfolder=_SCREAMING_SNAKE_CASE						)

    self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)


"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a														=		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""")
				_a														=		parser.parse_args()

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

				_a														=		model.state_dict()
				_a														=		{}

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

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

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

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

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






