code: 
  '''simple docstring'''
  import argparse
  import json
  import os
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase__						=			logging.get_logger(__name__)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Union[str, Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					str     =	ConvNextVaConfig()
  
  		if "atto" in checkpoint_url:
  				snake_case     :					Union[str, Any]     =	[2, 2, 6, 2]
  				snake_case     :					Tuple     =	[40, 80, 160, 320]
  		if "femto" in checkpoint_url:
  				snake_case     :					List[str]     =	[2, 2, 6, 2]
  				snake_case     :					Dict     =	[48, 96, 192, 384]
  		if "pico" in checkpoint_url:
  				snake_case     :					int     =	[2, 2, 6, 2]
  				snake_case     :					int     =	[64, 128, 256, 512]
  		if "nano" in checkpoint_url:
  				snake_case     :					Union[str, Any]     =	[2, 2, 8, 2]
  				snake_case     :					Dict     =	[80, 160, 320, 640]
  		if "tiny" in checkpoint_url:
  				snake_case     :					Optional[Any]     =	[3, 3, 9, 3]
  				snake_case     :					List[Any]     =	[96, 192, 384, 768]
  		if "base" in checkpoint_url:
  				snake_case     :					str     =	[3, 3, 27, 3]
  				snake_case     :					int     =	[128, 256, 512, 1024]
  		if "large" in checkpoint_url:
  				snake_case     :					Optional[Any]     =	[3, 3, 27, 3]
  				snake_case     :					Union[str, Any]     =	[192, 384, 768, 1536]
  		if "huge" in checkpoint_url:
  				snake_case     :					Optional[int]     =	[3, 3, 27, 3]
  				snake_case     :					int     =	[352, 704, 1408, 2816]
  
  		snake_case     :					List[Any]     =	1000
  		snake_case     :					str     =	'''imagenet-1k-id2label.json'''
  		snake_case     :					Optional[Any]     =	(1, 1000)
  
  		snake_case     :					Optional[int]     =	'''huggingface/label-files'''
  		snake_case     :					Any     =	num_labels
  		snake_case     :					Dict     =	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					repo_type='''dataset''' )   ,					'''r''' ) )
  		snake_case     :					Dict     =	{int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
  
  		snake_case     :					Optional[int]     =	idalabel
  		snake_case     :					Optional[Any]     =	{v: k for k, v in idalabel.items()}
  		snake_case     :					Dict     =	hidden_sizes
  		snake_case     :					Optional[int]     =	depths
  
  		return config, expected_shape
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       str:
  
  
  		'''simple docstring'''
  
  
  		if "downsample_layers.0.0" in name:
  				snake_case     :					Any     =	name.replace('''downsample_layers.0.0'''   ,					'''embeddings.patch_embeddings''' )
  		if "downsample_layers.0.1" in name:
  				snake_case     :					List[Any]     =	name.replace('''downsample_layers.0.1'''   ,					'''embeddings.norm''' )  # we rename to layernorm later on
  		if "downsample_layers.1.0" in name:
  				snake_case     :					str     =	name.replace('''downsample_layers.1.0'''   ,					'''stages.1.downsampling_layer.0''' )
  		if "downsample_layers.1.1" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''downsample_layers.1.1'''   ,					'''stages.1.downsampling_layer.1''' )
  		if "downsample_layers.2.0" in name:
  				snake_case     :					List[str]     =	name.replace('''downsample_layers.2.0'''   ,					'''stages.2.downsampling_layer.0''' )
  		if "downsample_layers.2.1" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''downsample_layers.2.1'''   ,					'''stages.2.downsampling_layer.1''' )
  		if "downsample_layers.3.0" in name:
  				snake_case     :					Any     =	name.replace('''downsample_layers.3.0'''   ,					'''stages.3.downsampling_layer.0''' )
  		if "downsample_layers.3.1" in name:
  				snake_case     :					Any     =	name.replace('''downsample_layers.3.1'''   ,					'''stages.3.downsampling_layer.1''' )
  		if "stages" in name and "downsampling_layer" not in name:
  				# stages.0.0. for instance should be renamed to stages.0.layers.0.
  				snake_case     :					List[Any]     =	name[: len('''stages.0''' )] + '''.layers''' + name[len('''stages.0''' ) :]
  		if "gamma" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''gamma'''   ,					'''weight''' )
  		if "beta" in name:
  				snake_case     :					Optional[int]     =	name.replace('''beta'''   ,					'''bias''' )
  		if "stages" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''stages'''   ,					'''encoder.stages''' )
  		if "norm" in name:
  				snake_case     :					List[str]     =	name.replace('''norm'''   ,					'''layernorm''' )
  		if "head" in name:
  				snake_case     :					List[Any]     =	name.replace('''head'''   ,					'''classifier''' )
  
  		return name
  
  
  
  
  def       _UpperCamelCase	(					)     ->       Optional[int]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Any     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		snake_case     :					Union[str, Any]     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
  		return im
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Tuple:
  
  
  		'''simple docstring'''
  
  
  		if "224" in checkpoint_url:
  				snake_case     :					Optional[int]     =	224
  				snake_case     :					List[str]     =	224 / 256
  		elif "384" in checkpoint_url:
  				snake_case     :					int     =	384
  				snake_case     :					Tuple     =	None
  		else:
  				snake_case     :					Tuple     =	512
  				snake_case     :					Tuple     =	None
  
  		return ConvNextImageProcessor(
  		    size=SCREAMING_SNAKE_CASE__   ,					crop_pct=SCREAMING_SNAKE_CASE__   ,					image_mean=[0.485, 0.456, 0.406]   ,					image_std=[0.229, 0.224, 0.225]   ,					resample=PILImageResampling.BICUBIC   ,					)
  
  
  
  
  @torch.no_grad()
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Union[str, Any]:
  
  
  		'''simple docstring'''
  
  
  		print('''Downloading original model from checkpoint...''' )
  		# define ConvNeXTV2 configuration based on URL
  		snake_case ,snake_case     :					Dict     =	get_convnextva_config(SCREAMING_SNAKE_CASE__ )
  		# load original state_dict from URL
  		snake_case     :					Optional[Any]     =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ )['''model''']
  
  		print('''Converting model parameters...''' )
  		# rename keys
  		for key in state_dict.copy().keys():
  				snake_case     :					int     =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
  				snake_case     :					str     =	val
  		# add prefix to all keys expect classifier head
  		for key in state_dict.copy().keys():
  				snake_case     :					int     =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
  				if not key.startswith('''classifier''' ):
  						snake_case     :					Tuple     =	'''convnextv2.''' + key
  				snake_case     :					List[Any]     =	val
  
  		# load HuggingFace model
  		snake_case     :					Any     =	ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE__ )
  		model.load_state_dict(SCREAMING_SNAKE_CASE__ )
  		model.eval()
  
  		# Check outputs on an image, prepared by ConvNextImageProcessor
  		snake_case     :					Union[str, Any]     =	convert_preprocessor(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Any     =	preprocessor(images=prepare_img()   ,					return_tensors='''pt''' )
  		snake_case     :					Dict     =	model(**SCREAMING_SNAKE_CASE__ ).logits
  
  		# note: the logits below were obtained without center cropping
  		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
  				snake_case     :					Tuple     =	torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  				snake_case     :					Dict     =	torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  				snake_case     :					Optional[Any]     =	torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  				snake_case     :					Dict     =	torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  				snake_case     :					List[str]     =	torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  				snake_case     :					Tuple     =	torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  				snake_case     :					Tuple     =	torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  				snake_case     :					Union[str, Any]     =	torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  				snake_case     :					Dict     =	torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  				snake_case     :					Optional[Any]     =	torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  				snake_case     :					Any     =	torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  				snake_case     :					Dict     =	torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  				snake_case     :					Union[str, Any]     =	torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  				snake_case     :					List[str]     =	torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  				snake_case     :					Optional[Any]     =	torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  				snake_case     :					Any     =	torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  				snake_case     :					Any     =	torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  				snake_case     :					List[str]     =	torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826] )
  		else:
  				raise ValueError(F'Unknown URL: {checkpoint_url}' )
  
  		assert torch.allclose(logits[0, :5]   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 )
  		assert logits.shape == expected_shape
  		print('''Model outputs match the original results!''' )
  
  		if save_model:
  				print('''Saving model to local...''' )
  				# Create folder to save model
  				if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
  						os.mkdir(SCREAMING_SNAKE_CASE__ )
  
  				model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  				preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					Optional[Any]     =	'''convnextv2'''
  		if "atto" in checkpoint_url:
  				model_name += "-atto"
  		if "femto" in checkpoint_url:
  				model_name += "-femto"
  		if "pico" in checkpoint_url:
  				model_name += "-pico"
  		if "nano" in checkpoint_url:
  				model_name += "-nano"
  		elif "tiny" in checkpoint_url:
  				model_name += "-tiny"
  		elif "base" in checkpoint_url:
  				model_name += "-base"
  		elif "large" in checkpoint_url:
  				model_name += "-large"
  		elif "huge" in checkpoint_url:
  				model_name += "-huge"
  		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  				model_name += "-22k"
  		elif "22k" in checkpoint_url and "1k" in checkpoint_url:
  				model_name += "-22k-1k"
  		elif "1k" in checkpoint_url:
  				model_name += "-1k"
  		if "224" in checkpoint_url:
  				model_name += "-224"
  		elif "384" in checkpoint_url:
  				model_name += "-384"
  		elif "512" in checkpoint_url:
  				model_name += "-512"
  
  		if push_to_hub:
  				print(F'Pushing {model_name} to the hub...' )
  				model.push_to_hub(SCREAMING_SNAKE_CASE__ )
  				preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--checkpoint_url",
  						    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
  						    type=str,
  						    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
  						)
  						parser.add_argument(
  						    "--pytorch_dump_folder_path",
  						    default="model",
  						    type=str,
  						    help="Path to the output PyTorch model directory.",
  						)
  						parser.add_argument("--save_model", action="store_true", help="Save model to local")
  						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")
  
  						lowercase__						=			parser.parse_args()
  						convert_convnextva_checkpoint(
  						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  						)
  
  
  
style_context: 
  '''simple docstring'''
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, 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, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  						import tensorflow as tf
  
  						from transformers.models.ctrl.modeling_tf_ctrl import (
  						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFCTRLForSequenceClassification,
  						    TFCTRLLMHeadModel,
  						    TFCTRLModel,
  						)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]     ,  )	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	parent
  				snake_case     :					str     =	13
  				snake_case     :					Any     =	7
  				snake_case     :					str     =	True
  				snake_case     :					int     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					str     =	True
  				snake_case     :					Any     =	99
  				snake_case     :					int     =	32
  				snake_case     :					str     =	2
  				snake_case     :					Optional[Any]     =	4
  				snake_case     :					str     =	37
  				snake_case     :					Optional[int]     =	'''gelu'''
  				snake_case     :					List[Any]     =	0.1
  				snake_case     :					Dict     =	0.1
  				snake_case     :					str     =	512
  				snake_case     :					List[str]     =	16
  				snake_case     :					Tuple     =	2
  				snake_case     :					List[str]     =	0.02
  				snake_case     :					Optional[int]     =	3
  				snake_case     :					Optional[int]     =	4
  				snake_case     :					List[str]     =	None
  				snake_case     :					List[str]     =	self.vocab_size - 1
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					Optional[Any]     =	None
  				if self.use_input_mask:
  						snake_case     :					List[str]     =	random_attention_mask([self.batch_size, self.seq_length]			)
  
  				snake_case     :					str     =	None
  				if self.use_token_type_ids:
  						snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size			)
  
  				snake_case     :					Optional[int]     =	None
  				if self.use_mc_token_ids:
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size, self.num_choices]     ,  self.seq_length			)
  
  				snake_case     :					Dict     =	None
  				snake_case     :					Any     =	None
  				snake_case     :					str     =	None
  				if self.use_labels:
  						snake_case     :					Optional[Any]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  						snake_case     :					Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels			)
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size]     ,  self.num_choices			)
  
  				snake_case     :					List[Any]     =	CTRLConfig(
  				    vocab_size=self.vocab_size     ,  n_embd=self.hidden_size     ,  n_layer=self.num_hidden_layers     ,  n_head=self.num_attention_heads     ,  n_positions=self.max_position_embeddings     ,  pad_token_id=self.pad_token_id     ,  )
  
  				snake_case     :					List[Any]     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,  2			)
  
  				return (
  				    config,
  				    input_ids,
  				    input_mask,
  				    head_mask,
  				    token_type_ids,
  				    mc_token_ids,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	TFCTRLModel(config=UpperCamelCase__			)
  				snake_case     :					Union[str, Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Optional[int]     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	[input_ids, None, input_mask]  # None is the input for 'past'
  				snake_case     :					str     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)
  
  				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: List[str]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
  				snake_case     :					int     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Any     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  *UpperCamelCase__							: Tuple			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self.num_labels
  				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  				snake_case     :					Any     =	{
  				    '''input_ids''': input_ids,
  				    '''token_type_ids''': token_type_ids,
  				    '''labels''': sequence_labels,
  				}
  				snake_case     :					Dict     =	TFCTRLForSequenceClassification(UpperCamelCase__			)
  				snake_case     :					int     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.prepare_config_and_inputs()
  
  				(
  				    (
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,
  				)     :					str     =	config_and_inputs
  
  				snake_case     :					Any     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
  		lowerCamelCase           =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
  		lowerCamelCase           =	(
  		    {
  		        """feature-extraction""": TFCTRLModel,
  		        """text-classification""": TFCTRLForSequenceClassification,
  		        """text-generation""": TFCTRLLMHeadModel,
  		        """zero-shot""": TFCTRLForSequenceClassification,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Dict			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
  						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
  						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
  						# config could not be created.
  						return True
  
  				return False
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	TFCTRLModelTester(self			)
  				snake_case     :					Dict     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  n_embd=37			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case ,snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
  				snake_case     :					int     =	[TFCTRLLMHeadModel]
  				snake_case     :					Tuple     =	[TFCTRLForSequenceClassification]
  
  				for model_class in self.all_model_classes:
  						snake_case     :					List[Any]     =	model_class(UpperCamelCase__			)
  						model.build()  # may be needed for the get_bias() call below
  						assert isinstance(model.get_input_embeddings()     ,  tf.keras.layers.Layer			)
  
  						if model_class in list_lm_models:
  								snake_case     :					Optional[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					Any     =	model.get_bias()
  								assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
  								for k, v in name.items():
  										assert isinstance(UpperCamelCase__     ,  tf.Variable			)
  						elif model_class in list_other_models_with_output_ebd:
  								snake_case     :					List[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					int     =	model.get_bias()
  								assert name is None
  						else:
  								snake_case     :					Tuple     =	model.get_output_embeddings()
  								assert x is None
  								snake_case     :					Dict     =	model.get_bias()
  								assert name is None
  
  
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						snake_case     :					str     =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
  						self.assertIsNotNone(UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	TFCTRLLMHeadModel.from_pretrained('''ctrl'''			)
  				snake_case     :					Optional[Any]     =	tf.convert_to_tensor([[1_1859, 0, 1611, 8]]     ,  dtype=tf.intaa			)  # Legal the president is
  				snake_case     :					Dict     =	[
  				    1_1859,
  				    0,
  				    1611,
  				    8,
  				    5,
  				    150,
  				    2_6449,
  				    2,
  				    19,
  				    348,
  				    469,
  				    3,
  				    2595,
  				    48,
  				    2_0740,
  				    24_6533,
  				    24_6533,
  				    19,
  				    30,
  				    5,
  				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
  				snake_case     :					int     =	model.generate(UpperCamelCase__     ,  do_sample=UpperCamelCase__			)
  				self.assertListEqual(output_ids[0].numpy().tolist()     ,  UpperCamelCase__			)
  
  
  