



"""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()
UpperCAmelCase    :		Optional[Any]      =			logging.get_logger()


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase = True				)			->    Union[str, Any]:







  '''simple docstring'''


  print(F'''Converting {name}...'''				)

  with torch.no_grad():
    if hidden_sizes == 1_28:
      if name[-1] == "S":
        lowercase_             =			timm.create_model("""levit_128s"""			,	pretrained=snake_case__				)
      else:
        lowercase_             =			timm.create_model("""levit_128"""			,	pretrained=snake_case__				)
    if hidden_sizes == 1_92:
      lowercase_             =			timm.create_model("""levit_192"""			,	pretrained=snake_case__				)
    if hidden_sizes == 2_56:
      lowercase_             =			timm.create_model("""levit_256"""			,	pretrained=snake_case__				)
    if hidden_sizes == 3_84:
      lowercase_             =			timm.create_model("""levit_384"""			,	pretrained=snake_case__				)

    from_model.eval()
    lowercase_             =			LevitForImageClassificationWithTeacher(snake_case__				).eval()
    lowercase_             =			OrderedDict()

    lowercase_             =			from_model.state_dict()
    lowercase_             =			list(from_model.state_dict().keys()				)
    lowercase_             =			list(our_model.state_dict().keys()				)
    print(len(snake_case__				)			,	len(snake_case__				)				)
    for i in range(len(snake_case__				)				):
      lowercase_             =			weights[og_keys[i]]
    our_model.load_state_dict(snake_case__				)

    lowercase_             =			torch.randn((2, 3, 2_24, 2_24)				)
    lowercase_             =			from_model(snake_case__				)
    lowercase_             =			our_model(snake_case__				).logits

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

  lowercase_             =			name
  print(snake_case__				)

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

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


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase			,	__lowerCAmelCase = None			,	__lowerCAmelCase = True				)			->    Dict:







  '''simple docstring'''


  lowercase_             =			'''imagenet-1k-id2label.json'''
  lowercase_             =			10_00
  lowercase_             =			(1, num_labels)

  lowercase_             =			'''huggingface/label-files'''
  lowercase_             =			num_labels
  lowercase_             =			json.load(open(hf_hub_download(snake_case__			,	snake_case__			,	repo_type="""dataset"""				)			,	"""r"""				)				)
  lowercase_             =			{int(snake_case__				): v for k, v in idalabel.items()}

  lowercase_             =			idalabel
  lowercase_             =			{v: k for k, v in idalabel.items()}

  lowercase_             =			partial(snake_case__			,	num_labels=snake_case__			,	idalabel=snake_case__			,	labelaid=snake_case__				)

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

  lowercase_             =			{
      '''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]			,	snake_case__			,	names_to_config[model_name]			,	snake_case__			,	snake_case__				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__				)
  return config, expected_shape


if __name__ == "__main__":
       UpperCAmelCase    :		List[str]      =			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",
       )

       UpperCAmelCase    :		List[str]      =			parser.parse_args()
       UpperCAmelCase    :		Path      =			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)



'''simple docstring'''




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     )					->							int:






         """simple docstring"""






         A			:  str   =		parent
         A			:  Optional[int]   =		13
         A			:  Union[str, Any]   =		7
         A			:  Optional[Any]   =		30
         A			:  Optional[int]   =		self.seq_length + self.mem_len
         A			:  int   =		15
         A			:  Any   =		True
         A			:  Union[str, Any]   =		True
         A			:  Optional[int]   =		99
         A			:  Any   =		[10, 50, 80]
         A			:  Any   =		32
         A			:  Any   =		32
         A			:  Any   =		4
         A			:  Any   =		8
         A			:  Optional[int]   =		128
         A			:  Union[str, Any]   =		2
         A			:  List[str]   =		2
         A			:  Optional[Any]   =		None
         A			:  int   =		1
         A			:  str   =		0
         A			:  Optional[int]   =		3
         A			:  Union[str, Any]   =		self.vocab_size - 1
         A			:  str   =		0.01





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )
         A			:  str   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         A			:  List[str]   =		None
         if self.use_labels:
             A			:  Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         A			:  Optional[Any]   =		TransfoXLConfig(
             vocab_size=self.vocab_size		,     mem_len=self.mem_len		,     clamp_len=self.clamp_len		,     cutoffs=self.cutoffs		,     d_model=self.hidden_size		,     d_embed=self.d_embed		,     n_head=self.num_attention_heads		,     d_head=self.d_head		,     d_inner=self.d_inner		,     div_val=self.div_val		,     n_layer=self.num_hidden_layers		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.vocab_size - 1		,     init_range=self.init_range		,     num_labels=self.num_labels		,     )

         return (config, input_ids_a, input_ids_a, lm_labels)





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         random.seed(self.seed      )
         tf.random.set_seed(self.seed      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		TFTransfoXLModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  int   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

         A,    A			:  Any   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape		,     (self.batch_size, self.seq_length, self.hidden_size)      )
         self.parent.assertEqual(hidden_states_a.shape		,     (self.batch_size, self.seq_length, self.hidden_size)      )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Tuple   =		TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Tuple   =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         A,    A			:  Dict   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A,    A			:  Optional[Any]   =		model([input_ids_a, mems_a]      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

         A,    A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape		,     (self.batch_size, self.seq_length, self.vocab_size)      )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )

         self.parent.assertEqual(lm_logits_a.shape		,     (self.batch_size, self.seq_length, self.vocab_size)      )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  List[Any]   =		TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  List[Any]   =		self.prepare_config_and_inputs()
         ((A),    (A),    (A),    (A))			:  Dict   =		config_and_inputs
         A			:  List[str]   =		{'''input_ids''': input_ids_a}
         return config, inputs_dict





@require_tf
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     __magic_name__          =						() if is_tf_available() else ()
     __magic_name__          =						(
         {
             '''feature-extraction''': TFTransfoXLModel,
             '''text-classification''': TFTransfoXLForSequenceClassification,
             '''text-generation''': TFTransfoXLLMHeadModel,
             '''zero-shot''': TFTransfoXLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

         return False





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  str   =		TFTransfoXLModelTester(self      )
         A			:  Optional[int]   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     d_embed=37      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Tuple   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  int   =		[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
             A			:  int   =		model_class(SCREAMING_SNAKE_CASE      )
             assert isinstance(model.get_input_embeddings()		,     tf.keras.layers.Layer      )
             if model_class in list_other_models_with_output_ebd:
                 A			:  List[Any]   =		model.get_output_embeddings()
                 assert isinstance(SCREAMING_SNAKE_CASE		,     tf.keras.layers.Layer      )
                 A			:  List[Any]   =		model.get_bias()
                 assert name is None
             else:
                 A			:  int   =		model.get_output_embeddings()
                 assert x is None
                 A			:  List[Any]   =		model.get_bias()
                 assert name is None





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         pass





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  Union[str, Any]   =		TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





     @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





@require_tf
class 					A							(				unittest.TestCase		):





     @unittest.skip('''Skip test until #12651 is resolved.'''      )
     @slow
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[str]   =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
         # fmt: off
         A			:  List[str]   =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]		,     dtype=tf.intaa      )  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         A			:  str   =		[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         A			:  Optional[Any]   =		model.generate(SCREAMING_SNAKE_CASE		,     max_length=200		,     do_sample=SCREAMING_SNAKE_CASE      )
         self.assertListEqual(output_ids[0].numpy().tolist()		,     SCREAMING_SNAKE_CASE      )




