"""simple docstring"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
  _lowerCAmelCase		: int						    =  True if '''large''' in model_name or '''huge''' in model_name else False
  _lowerCAmelCase		: Dict						    =  True if '''large''' in model_name or '''huge''' in model_name else False
  _lowerCAmelCase		: List[str]						    =  True if '''large''' in model_name or '''huge''' in model_name else False

  if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
    if "fl3" in model_name:
      _lowerCAmelCase		: int						    =  [3, 3, 3, 3]
      _lowerCAmelCase		: str						    =  [5, 5, 5, 5]
    elif "fl4" in model_name:
      _lowerCAmelCase		: List[str]						    =  [4, 4, 4, 4]
      _lowerCAmelCase		: Union[str, Any]						    =  [3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    _lowerCAmelCase		: Any						    =  [3, 3, 3, 3]
    if "lrf" in model_name:
      _lowerCAmelCase		: Optional[Any]						    =  [3, 3, 3, 3]
    else:
      _lowerCAmelCase		: Any						    =  [2, 2, 2, 2]

  if "tiny" in model_name:
    _lowerCAmelCase		: Dict						    =  96
  elif "small" in model_name:
    _lowerCAmelCase		: Any						    =  96
  elif "base" in model_name:
    _lowerCAmelCase		: Union[str, Any]						    =  128
  elif "large" in model_name:
    _lowerCAmelCase		: List[Any]						    =  192
  elif "xlarge" in model_name:
    _lowerCAmelCase		: List[Any]						    =  256
  elif "huge" in model_name:
    _lowerCAmelCase		: Optional[Any]						    =  352

  # set label information
  _lowerCAmelCase		: Dict						    =  '''huggingface/label-files'''
  if "large" in model_name or "huge" in model_name:
    _lowerCAmelCase		: Optional[Any]						    =  '''imagenet-22k-id2label.json'''
  else:
    _lowerCAmelCase		: Union[str, Any]						    =  '''imagenet-1k-id2label.json'''

  _lowerCAmelCase		: Tuple						    =  json.load(open(hf_hub_download(a_    ,a_    ,repo_type="""dataset"""			)    ,"""r"""			)			)
  _lowerCAmelCase		: Dict						    =  {int(a_			): v for k, v in idalabel.items()}
  _lowerCAmelCase		: Optional[Any]						    =  {v: k for k, v in idalabel.items()}

  _lowerCAmelCase		: Any						    =  FocalNetConfig(
      embed_dim=a_    ,depths=a_    ,focal_levels=a_    ,focal_windows=a_    ,use_conv_embed=a_    ,idalabel=a_    ,labelaid=a_    ,use_post_layernorm=a_    ,use_layerscale=a_    ,)

  return config





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]			) ->   int:
  if "patch_embed.proj" in name:
    _lowerCAmelCase		: Dict						    =  name.replace("""patch_embed.proj"""    ,"""embeddings.patch_embeddings.projection"""			)
  if "patch_embed.norm" in name:
    _lowerCAmelCase		: Dict						    =  name.replace("""patch_embed.norm"""    ,"""embeddings.norm"""			)
  if "layers" in name:
    _lowerCAmelCase		: Any						    =  '''encoder.''' + name
  if "encoder.layers" in name:
    _lowerCAmelCase		: Any						    =  name.replace("""encoder.layers"""    ,"""encoder.stages"""			)
  if "downsample.proj" in name:
    _lowerCAmelCase		: Optional[int]						    =  name.replace("""downsample.proj"""    ,"""downsample.projection"""			)
  if "blocks" in name:
    _lowerCAmelCase		: int						    =  name.replace("""blocks"""    ,"""layers"""			)
  if "modulation.f.weight" in name or "modulation.f.bias" in name:
    _lowerCAmelCase		: int						    =  name.replace("""modulation.f"""    ,"""modulation.projection_in"""			)
  if "modulation.h.weight" in name or "modulation.h.bias" in name:
    _lowerCAmelCase		: Union[str, Any]						    =  name.replace("""modulation.h"""    ,"""modulation.projection_context"""			)
  if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
    _lowerCAmelCase		: Tuple						    =  name.replace("""modulation.proj"""    ,"""modulation.projection_out"""			)

  if name == "norm.weight":
    _lowerCAmelCase		: Tuple						    =  '''layernorm.weight'''
  if name == "norm.bias":
    _lowerCAmelCase		: List[Any]						    =  '''layernorm.bias'''

  if "head" in name:
    _lowerCAmelCase		: List[Any]						    =  name.replace("""head"""    ,"""classifier"""			)
  else:
    _lowerCAmelCase		: Tuple						    =  '''focalnet.''' + name

  return name





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Union[str, Any]=False			) ->   Union[str, Any]:
  # fmt: off
  _lowerCAmelCase		: Optional[Any]						    =  {
      '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
      '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
      '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
      '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
      '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
      '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
      '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
      '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
      '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
      '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
  }
  # fmt: on

  _lowerCAmelCase		: Optional[int]						    =  model_name_to_url[model_name]
  print("""Checkpoint URL: """    ,a_			)
  _lowerCAmelCase		: List[Any]						    =  torch.hub.load_state_dict_from_url(a_    ,map_location="""cpu"""			)['''model''']

  # rename keys
  for key in state_dict.copy().keys():
    _lowerCAmelCase		: Union[str, Any]						    =  state_dict.pop(a_			)
    _lowerCAmelCase		: Union[str, Any]						    =  val

  _lowerCAmelCase		: Optional[int]						    =  get_focalnet_config(a_			)
  _lowerCAmelCase		: Union[str, Any]						    =  FocalNetForImageClassification(a_			)
  model.eval()

  # load state dict
  model.load_state_dict(a_			)

  # verify conversion
  _lowerCAmelCase		: Dict						    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

  _lowerCAmelCase		: Tuple						    =  BitImageProcessor(
      do_resize=a_    ,size={"""shortest_edge""": 256}    ,resample=PILImageResampling.BILINEAR    ,do_center_crop=a_    ,crop_size=224    ,do_normalize=a_    ,image_mean=a_    ,image_std=a_    ,)
  _lowerCAmelCase		: Dict						    =  Image.open(requests.get(a_    ,stream=a_			).raw			)
  _lowerCAmelCase		: Union[str, Any]						    =  processor(images=a_    ,return_tensors="""pt"""			)

  _lowerCAmelCase		: Dict						    =  transforms.Compose(
      [
          transforms.Resize(256			),
          transforms.CenterCrop(224			),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]    ,std=[0.2_29, 0.2_24, 0.2_25]			),
      ]			)

  _lowerCAmelCase		: List[str]						    =  image_transforms(a_			).unsqueeze(0			)

  # verify pixel_values
  assert torch.allclose(inputs.pixel_values    ,a_    ,atol=1e-4			)

  _lowerCAmelCase		: Optional[Any]						    =  model(**a_			)

  _lowerCAmelCase		: int						    =  outputs.logits.argmax(-1			).item()
  print("""Predicted class:"""    ,model.config.idalabel[predicted_class_idx]			)

  print("""First values of logits:"""    ,outputs.logits[0, :3]			)

  if model_name == "focalnet-tiny":
    _lowerCAmelCase		: Optional[Any]						    =  torch.tensor([0.21_66, -0.43_68, 0.21_91]			)
  elif model_name == "focalnet-tiny-lrf":
    _lowerCAmelCase		: Dict						    =  torch.tensor([1.16_69, 0.01_25, -0.16_95]			)
  elif model_name == "focalnet-small":
    _lowerCAmelCase		: Any						    =  torch.tensor([0.49_17, -0.04_30, 0.13_41]			)
  elif model_name == "focalnet-small-lrf":
    _lowerCAmelCase		: List[str]						    =  torch.tensor([-0.25_88, -0.53_42, -0.23_31]			)
  elif model_name == "focalnet-base":
    _lowerCAmelCase		: Any						    =  torch.tensor([-0.16_55, -0.40_90, -0.17_30]			)
  elif model_name == "focalnet-base-lrf":
    _lowerCAmelCase		: Dict						    =  torch.tensor([0.53_06, -0.04_83, -0.39_28]			)
  assert torch.allclose(outputs.logits[0, :3]    ,a_    ,atol=1e-4			)
  print("""Looks ok!"""			)

  if pytorch_dump_folder_path is not None:
    print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(a_			)
    processor.save_pretrained(a_			)

  if push_to_hub:
    print(f"Pushing model and processor of {model_name} to the hub..."			)
    model.push_to_hub(f"{model_name}"			)
    processor.push_to_hub(f"{model_name}"			)


if __name__ == "__main__":
     _a   :	Any 					=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='focalnet-tiny',
         type=str,
         help='Name of the FocalNet model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether to push the model and processor to the hub.',
     )

     _a   :	List[str] 					=     parser.parse_args()
     convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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       ,  a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Tuple						    =  13
       _lowerCAmelCase		: Tuple						    =  7
       _lowerCAmelCase		: Any						    =  30
       _lowerCAmelCase		: Optional[int]						    =  self.seq_length + self.mem_len
       _lowerCAmelCase		: Dict						    =  15
       _lowerCAmelCase		: List[Any]						    =  True
       _lowerCAmelCase		: Any						    =  True
       _lowerCAmelCase		: List[str]						    =  99
       _lowerCAmelCase		: List[Any]						    =  [10, 50, 80]
       _lowerCAmelCase		: Tuple						    =  32
       _lowerCAmelCase		: int						    =  32
       _lowerCAmelCase		: Dict						    =  4
       _lowerCAmelCase		: List[str]						    =  8
       _lowerCAmelCase		: Tuple						    =  128
       _lowerCAmelCase		: Any						    =  2
       _lowerCAmelCase		: List[Any]						    =  2
       _lowerCAmelCase		: List[Any]						    =  None
       _lowerCAmelCase		: Optional[Any]						    =  1
       _lowerCAmelCase		: Tuple						    =  0
       _lowerCAmelCase		: List[Any]						    =  3
       _lowerCAmelCase		: Optional[int]						    =  self.vocab_size - 1
       _lowerCAmelCase		: Dict						    =  0.0_1






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)
       _lowerCAmelCase		: Optional[Any]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

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

       _lowerCAmelCase		: Union[str, 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 					__A						(	self						):
       random.seed(self.seed						)
       tf.random.set_seed(self.seed						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  TFTransfoXLModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Optional[Any]						    =  {"""input_ids""": input_ids_a, """mems""": mems_a}

       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model(a__						).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 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  TFTransfoXLLMHeadModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Dict						    =  {"""input_ids""": input_ids_a, """labels""": lm_labels}
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

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

       _lowerCAmelCase		: Any						    =  {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).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 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLForSequenceClassification(a__						)
       _lowerCAmelCase		: int						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.prepare_config_and_inputs()
       ((_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase))		: Dict						    =  config_and_inputs
       _lowerCAmelCase		: List[Any]						    =  {"""input_ids""": input_ids_a}
       return config, inputs_dict







@require_tf
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _UpperCamelCase							:    Tuple	      =	() if is_tf_available() else ()
     _UpperCamelCase							:    Any	      =	(
         {
             "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
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    Tuple	      =	False
     _UpperCamelCase							:    Any	      =	False






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       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 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLModelTester(self						)
       _lowerCAmelCase		: List[Any]						    =  ConfigTester(self       ,  config_class=a__       ,  d_embed=37						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*a__						)






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: List[Any]						    =  [TFTransfoXLForSequenceClassification]

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






     def 					__A						(	self						):
       # TODO JP: Make TransfoXL XLA compliant
       pass






     @slow
     def 					__A						(	self						):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Union[str, Any]						    =  TFTransfoXLModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)






     @unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""						)
     def 					__A						(	self						):
       pass







@require_tf
class 		__A							(				unittest.TestCase	):






     @unittest.skip("""Skip test until #12651 is resolved."""						)
     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
       # fmt: off
       _lowerCAmelCase		: 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
       _lowerCAmelCase		: List[Any]						    =  [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>

       _lowerCAmelCase		: Tuple						    =  model.generate(a__       ,  max_length=200       ,  do_sample=a__						)
       self.assertListEqual(output_ids[0].numpy().tolist()       ,  a__						)




