






'''simple docstring'''






import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


__A    	=   {
    '''return_dict''': False,
    '''output_hidden_states''': True,
    '''output_attentions''': True,
    '''torchscript''': True,
    '''torch_dtype''': '''float16''',
    '''use_bfloat16''': True,
    '''tf_legacy_loss''': True,
    '''pruned_heads''': {'''a''': 1},
    '''tie_word_embeddings''': False,
    '''is_decoder''': True,
    '''cross_attention_hidden_size''': 1_2_8,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 5_0,
    '''min_length''': 3,
    '''do_sample''': True,
    '''early_stopping''': True,
    '''num_beams''': 3,
    '''num_beam_groups''': 3,
    '''diversity_penalty''': 0.5,
    '''temperature''': 2.0,
    '''top_k''': 1_0,
    '''top_p''': 0.7,
    '''typical_p''': 0.2,
    '''repetition_penalty''': 0.8,
    '''length_penalty''': 0.8,
    '''no_repeat_ngram_size''': 5,
    '''encoder_no_repeat_ngram_size''': 5,
    '''bad_words_ids''': [1, 2, 3],
    '''num_return_sequences''': 3,
    '''chunk_size_feed_forward''': 5,
    '''output_scores''': True,
    '''return_dict_in_generate''': True,
    '''forced_bos_token_id''': 2,
    '''forced_eos_token_id''': 3,
    '''remove_invalid_values''': True,
    '''architectures''': ['''BertModel'''],
    '''finetuning_task''': '''translation''',
    '''id2label''': {0: '''label'''},
    '''label2id''': {'''label''': '''0'''},
    '''tokenizer_class''': '''BertTokenizerFast''',
    '''prefix''': '''prefix''',
    '''bos_token_id''': 6,
    '''pad_token_id''': 7,
    '''eos_token_id''': 8,
    '''sep_token_id''': 9,
    '''decoder_start_token_id''': 1_0,
    '''exponential_decay_length_penalty''': (5, 1.01),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}




@is_staging_test
class a_    (    unittest.TestCase							):







   @classmethod
   def     SCREAMING_SNAKE_CASE__					(cls)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= TOKEN
      HfFolder.save_token(__A)







   @classmethod
   def     SCREAMING_SNAKE_CASE__					(cls)			->      List[str]:
      """simple docstring"""






      try:
         delete_repo(token=cls._token					,   repo_id='test-config')
      except HTTPError:
         pass

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

      try:
         delete_repo(token=cls._token					,   repo_id='test-dynamic-config')
      except HTTPError:
         pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= BertConfig(
          vocab_size=9_9					,   hidden_size=3_2					,   num_hidden_layers=5					,   num_attention_heads=4					,   intermediate_size=3_7)
      config.push_to_hub('test-config'					,   use_auth_token=self._token)

      __snake_case				:      Dict     							= BertConfig.from_pretrained(F"""{USER}/test-config""")
      for k, v in config.to_dict().items():
         if k != "transformers_version":
            self.assertEqual(__A					,   getattr(__A					,   __A))

        # Reset repo
      delete_repo(token=self._token					,   repo_id='test-config')

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
         config.save_pretrained(__A					,   repo_id='test-config'					,   push_to_hub=__A					,   use_auth_token=self._token)

      __snake_case				:      List[str]     							= BertConfig.from_pretrained(F"""{USER}/test-config""")
      for k, v in config.to_dict().items():
         if k != "transformers_version":
            self.assertEqual(__A					,   getattr(__A					,   __A))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= BertConfig(
          vocab_size=9_9					,   hidden_size=3_2					,   num_hidden_layers=5					,   num_attention_heads=4					,   intermediate_size=3_7)
      config.push_to_hub('valid_org/test-config-org'					,   use_auth_token=self._token)

      __snake_case				:      Any     							= BertConfig.from_pretrained('valid_org/test-config-org')
      for k, v in config.to_dict().items():
         if k != "transformers_version":
            self.assertEqual(__A					,   getattr(__A					,   __A))

        # Reset repo
      delete_repo(token=self._token					,   repo_id='valid_org/test-config-org')

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
         config.save_pretrained(
             __A					,   repo_id='valid_org/test-config-org'					,   push_to_hub=__A					,   use_auth_token=self._token)

      __snake_case				:      Tuple     							= BertConfig.from_pretrained('valid_org/test-config-org')
      for k, v in config.to_dict().items():
         if k != "transformers_version":
            self.assertEqual(__A					,   getattr(__A					,   __A))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      CustomConfig.register_for_auto_class()
      __snake_case				:      Any     							= CustomConfig(attribute=4_2)

      config.push_to_hub('test-dynamic-config'					,   use_auth_token=self._token)

      # This has added the proper auto_map field to the config
      self.assertDictEqual(config.auto_map					,   {'AutoConfig': 'custom_configuration.CustomConfig'})

      __snake_case				:      Optional[Any]     							= AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config"""					,   trust_remote_code=__A)
      # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
      self.assertEqual(new_config.__class__.__name__					,   'CustomConfig')
      self.assertEqual(new_config.attribute					,   4_2)




class a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= GPTaConfig()

      # attempt to modify each of int/float/bool/str config records and verify they were updated
      __snake_case				:      Tuple     							= c.n_embd + 1  # int
      __snake_case				:      List[Any]     							= c.resid_pdrop + 1.0  # float
      __snake_case				:      Any     							= not c.scale_attn_weights  # bool
      __snake_case				:      Dict     							= c.summary_type + 'foo'  # str
      c.update_from_string(
          F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}""")
      self.assertEqual(__A					,   c.n_embd					,   'mismatch for key: n_embd')
      self.assertEqual(__A					,   c.resid_pdrop					,   'mismatch for key: resid_pdrop')
      self.assertEqual(__A					,   c.scale_attn_weights					,   'mismatch for key: scale_attn_weights')
      self.assertEqual(__A					,   c.summary_type					,   'mismatch for key: summary_type')







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      List[str]     							= PretrainedConfig()
      __snake_case				:      Optional[int]     							= [key for key in base_config.__dict__ if key not in config_common_kwargs]
      # If this part of the test fails, you have arguments to addin config_common_kwargs above.
      self.assertListEqual(
          __A					,   ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'])
      __snake_case				:      Tuple     							= [key for key, value in config_common_kwargs.items() if value == getattr(__A					,   __A)]
      if len(__A) > 0:
         raise ValueError(
             'The following keys are set with the default values in'
             ' `test_configuration_common.config_common_kwargs` pick another value for them:'
             F""" {", ".join(__A)}.""")







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      with self.assertRaises(__A):
         # config is in subfolder, the following should not work without specifying the subfolder
         __snake_case				:      List[Any]     							= BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder')

      __snake_case				:      Tuple     							= BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'					,   subfolder='bert')

      self.assertIsNotNone(__A)







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= mock.Mock()
      __snake_case				:      Dict     							= 5_0_0
      __snake_case				:      Tuple     							= {}
      __snake_case				:      int     							= HTTPError
      __snake_case				:      str     							= {}

      # Download this model to make sure it's in the cache.
      __snake_case				:      int     							= BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert')

      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch('requests.Session.request'					,   return_value=__A) as mock_head:
         __snake_case				:      Union[str, Any]     							= BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert')
         # This check we did call the fake head request
         mock_head.assert_called()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= BertConfig.from_pretrained(
          'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json')







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= AutoConfig.from_pretrained('bert-base-cased')
      __snake_case				:      Union[str, Any]     							= ['config.4.0.0.json']

      with tempfile.TemporaryDirectory() as tmp_dir:
         configuration.save_pretrained(__A)
         __snake_case				:      List[str]     							= 2
         json.dump(configuration.to_dict()					,   open(os.path.join(__A					,   'config.4.0.0.json')					,   'w'))

         # This should pick the new configuration file as the version of Transformers is > 4.0.0
         __snake_case				:      Tuple     							= AutoConfig.from_pretrained(__A)
         self.assertEqual(new_configuration.hidden_size					,   2)

         # Will need to be adjusted if we reach v42 and this test is still here.
         # Should pick the old configuration file as the version of Transformers is < 4.42.0
         __snake_case				:      Optional[int]     							= ['config.42.0.0.json']
         __snake_case				:      Union[str, Any]     							= 7_6_8
         configuration.save_pretrained(__A)
         shutil.move(os.path.join(__A					,   'config.4.0.0.json')					,   os.path.join(__A					,   'config.42.0.0.json'))
         __snake_case				:      Union[str, Any]     							= AutoConfig.from_pretrained(__A)
         self.assertEqual(new_configuration.hidden_size					,   7_6_8)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= 'hf-internal-testing/test-two-configs'

      import transformers as new_transformers

      __snake_case				:      Dict     							= 'v4.0.0'
      __snake_case							,__snake_case				:      List[Any]     							= new_transformers.models.auto.AutoConfig.from_pretrained(
          __A					,   return_unused_kwargs=__A)
      self.assertEqual(new_configuration.hidden_size					,   2)
      # This checks `_configuration_file` ia not kept in the kwargs by mistake.
      self.assertDictEqual(__A					,   {})

      # Testing an older version by monkey-patching the version in the module it's used.
      import transformers as old_transformers

      __snake_case				:      int     							= 'v3.0.0'
      __snake_case				:      str     							= old_transformers.models.auto.AutoConfig.from_pretrained(__A)
      self.assertEqual(old_configuration.hidden_size					,   7_6_8)







'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            __snake_case				:      Optional[int]     							= timm.create_model('levit_128s'  ,		pretrained=A  )
         else:
            __snake_case				:      Tuple     							= timm.create_model('levit_128'  ,		pretrained=A  )
      if hidden_sizes == 1_92:
         __snake_case				:      int     							= timm.create_model('levit_192'  ,		pretrained=A  )
      if hidden_sizes == 2_56:
         __snake_case				:      List[Any]     							= timm.create_model('levit_256'  ,		pretrained=A  )
      if hidden_sizes == 3_84:
         __snake_case				:      int     							= timm.create_model('levit_384'  ,		pretrained=A  )

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

   assert torch.allclose(A  ,		A  ), "The model logits don't match the original one."

   __snake_case				:      int     							= name
   print(A  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      __snake_case				:      int     							= LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(F"""Pushed {checkpoint_name}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

   __snake_case				:      List[str]     							= 'huggingface/label-files'
   __snake_case				:      Any     							= num_labels
   __snake_case				:      str     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
   __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}

   __snake_case				:      int     							= idalabel
   __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

   __snake_case				:      Dict     							= {
       'levit-128S': 1_28,
       'levit-128': 1_28,
       'levit-192': 1_92,
       'levit-256': 2_56,
       'levit-384': 3_84,
   }

   __snake_case				:      Union[str, Any]     							= {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[2, 3, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 8, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]  ,		num_attention_heads=[3, 5, 6]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]  ,		num_attention_heads=[6, 9, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0.1  ,		),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]  ,		A  ,		names_to_config[model_name]  ,		A  ,		A  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,		A  ,		A  ,		A  ,		A  )
   return config, expected_shape


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''levit-dump-folder/''',
          type=Path,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      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''',
      )

      __A    	=   parser.parse_args()
      __A    	=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)