code: 
  '''simple docstring'''
  import argparse
  import os
  
  import align
  import numpy as np
  import requests
  import tensorflow as tf
  import torch
  from PIL import Image
  from tokenizer import Tokenizer
  
  from transformers import (
      AlignConfig,
      AlignModel,
      AlignProcessor,
      BertConfig,
      BertTokenizer,
      EfficientNetConfig,
      EfficientNetImageProcessor,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase__						=			logging.get_logger(__name__)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	tf.image.resize(SCREAMING_SNAKE_CASE__   ,					(346, 346) )
  		snake_case     :					List[str]     =	tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE__   ,					(346 - 289) // 2   ,					(346 - 289) // 2   ,					289   ,					289 )
  		return image
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					List[str]     =	EfficientNetConfig.from_pretrained('''google/efficientnet-b7''' )
  		snake_case     :					Tuple     =	289
  		snake_case     :					Optional[int]     =	640
  		snake_case     :					Optional[Any]     =	{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
  		snake_case     :					Any     =	{'''LABEL_0''': 0, '''LABEL_1''': 1}
  		snake_case     :					List[str]     =	[]
  
  		snake_case     :					Any     =	BertConfig()
  		snake_case     :					Optional[int]     =	AlignConfig.from_text_vision_configs(
  		    text_config=SCREAMING_SNAKE_CASE__   ,					vision_config=SCREAMING_SNAKE_CASE__   ,					projection_dim=640 )
  		return config
  
  
  
  
  def       _UpperCamelCase	(					)     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		snake_case     :					Any     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
  		return im
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	EfficientNetImageProcessor(
  		    do_center_crop=SCREAMING_SNAKE_CASE__   ,					rescale_factor=1 / 127.5   ,					rescale_offset=SCREAMING_SNAKE_CASE__   ,					do_normalize=SCREAMING_SNAKE_CASE__   ,					include_top=SCREAMING_SNAKE_CASE__   ,					resample=Image.BILINEAR   ,					)
  		snake_case     :					Any     =	BertTokenizer.from_pretrained('''bert-base-uncased''' )
  		snake_case     :					Union[str, Any]     =	64
  		snake_case     :					Tuple     =	AlignProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,					tokenizer=SCREAMING_SNAKE_CASE__ )
  		return processor
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	[v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
  		snake_case     :					Any     =	list(set(SCREAMING_SNAKE_CASE__ ) )
  		snake_case     :					List[Any]     =	sorted(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[Any]     =	len(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[Any]     =	{b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__   ,					range(SCREAMING_SNAKE_CASE__ ) )}
  
  		snake_case     :					Optional[Any]     =	[]
  		rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
  		rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
  		rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
  		rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
  		rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
  
  		for b in block_names:
  				snake_case     :					int     =	block_name_mapping[b]
  				rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
  				rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
  				rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
  				rename_keys.append(
  				    (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
  				rename_keys.append(
  				    (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
  				rename_keys.append(
  				    (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
  				rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
  				rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
  				rename_keys.append(
  				    (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
  				rename_keys.append(
  				    (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )
  
  				rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
  				rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
  				rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
  				rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
  				rename_keys.append(
  				    (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight') )
  				rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight') )
  				rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias') )
  				rename_keys.append(
  				    (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
  				rename_keys.append(
  				    (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var') )
  
  		snake_case     :					List[str]     =	{}
  		for item in rename_keys:
  				if item[0] in original_param_names:
  						snake_case     :					Any     =	'''vision_model.''' + item[1]
  
      # BERT text encoder
  		snake_case     :					Union[str, Any]     =	[]
  		snake_case     :					int     =	'''tf_bert_model/bert'''
  		snake_case     :					Optional[int]     =	'''text_model'''
  		for i in range(12 ):
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
  				        F'{new}.encoder.layer.{i}.attention.self.query.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
  				        F'{new}.encoder.layer.{i}.attention.self.query.bias',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
  				        F'{new}.encoder.layer.{i}.attention.self.key.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
  				        F'{new}.encoder.layer.{i}.attention.self.key.bias',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
  				        F'{new}.encoder.layer.{i}.attention.self.value.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
  				        F'{new}.encoder.layer.{i}.attention.self.value.bias',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
  				        F'{new}.encoder.layer.{i}.attention.output.dense.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
  				        F'{new}.encoder.layer.{i}.attention.output.dense.bias',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
  				        F'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
  				        F'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
  				        F'{new}.encoder.layer.{i}.intermediate.dense.weight',
  				    ) )
  				rename_keys.append(
  				    (
  				        F'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
  				        F'{new}.encoder.layer.{i}.intermediate.dense.bias',
  				    ) )
  				rename_keys.append(
  				    (F'{old}/encoder/layer_._{i}/output/dense/kernel:0', F'{new}.encoder.layer.{i}.output.dense.weight') )
  				rename_keys.append(
  				    (F'{old}/encoder/layer_._{i}/output/dense/bias:0', F'{new}.encoder.layer.{i}.output.dense.bias') )
  				rename_keys.append(
  				    (F'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', F'{new}.encoder.layer.{i}.output.LayerNorm.weight') )
  				rename_keys.append(
  				    (F'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', F'{new}.encoder.layer.{i}.output.LayerNorm.bias') )
  
  		rename_keys.append((F'{old}/embeddings/word_embeddings/weight:0', F'{new}.embeddings.word_embeddings.weight') )
  		rename_keys.append(
  		    (F'{old}/embeddings/position_embeddings/embeddings:0', F'{new}.embeddings.position_embeddings.weight') )
  		rename_keys.append(
  		    (F'{old}/embeddings/token_type_embeddings/embeddings:0', F'{new}.embeddings.token_type_embeddings.weight') )
  		rename_keys.append((F'{old}/embeddings/LayerNorm/gamma:0', F'{new}.embeddings.LayerNorm.weight') )
  		rename_keys.append((F'{old}/embeddings/LayerNorm/beta:0', F'{new}.embeddings.LayerNorm.bias') )
  
  		rename_keys.append((F'{old}/pooler/dense/kernel:0', F'{new}.pooler.dense.weight') )
  		rename_keys.append((F'{old}/pooler/dense/bias:0', F'{new}.pooler.dense.bias') )
  		rename_keys.append(('''dense/kernel:0''', '''text_projection.weight''') )
  		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''') )
  		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''') )
  		rename_keys.append(('''temperature:0''', '''temperature''') )
  
  		for item in rename_keys:
  				if item[0] in original_param_names:
  						snake_case     :					List[Any]     =	item[1]
  		return key_mapping
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:
  
  
  		'''simple docstring'''
  
  
  		list(hf_params.keys() )
  
  		for key, value in tf_params.items():
  				if key not in key_mapping:
  						continue
  
  				snake_case     :					List[str]     =	key_mapping[key]
  				if "_conv" in key and "kernel" in key:
  						snake_case     :					List[str]     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3   ,					2   ,					0   ,					1 )
  				elif "embeddings" in key:
  						snake_case     :					Any     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ )
  				elif "depthwise_kernel" in key:
  						snake_case     :					Dict     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2   ,					3   ,					0   ,					1 )
  				elif "kernel" in key:
  						snake_case     :					Tuple     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
  				elif "temperature" in key:
  						snake_case     :					List[str]     =	value
  				elif "bn/gamma" or "bn/beta" in key:
  						snake_case     :					int     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) ).squeeze()
  				else:
  						snake_case     :					Dict     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ )
  
  				# Replace HF parameters with original TF model parameters
  				hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__ )
  
  
  
  
  @torch.no_grad()
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	64
  		snake_case     :					int     =	Tokenizer(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[Any]     =	align.Align('''efficientnet-b7'''   ,					'''bert-base'''   ,					640   ,					SCREAMING_SNAKE_CASE__   ,					tok.get_vocab_size() )
  		original_model.compile()
  		original_model.load_weights(SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					List[str]     =	original_model.trainable_variables
  		snake_case     :					str     =	original_model.non_trainable_variables
  		snake_case     :					Dict     =	{param.name: param.numpy() for param in tf_params}
  		for param in tf_non_train_params:
  				snake_case     :					Optional[int]     =	param.numpy()
  		snake_case     :					List[str]     =	list(tf_params.keys() )
  
  		# Load HuggingFace model
  		snake_case     :					str     =	get_align_config()
  		snake_case     :					str     =	AlignModel(SCREAMING_SNAKE_CASE__ ).eval()
  		snake_case     :					Union[str, Any]     =	hf_model.state_dict()
  
  		# Create src-to-dst parameter name mapping dictionary
  		print('''Converting parameters...''' )
  		snake_case     :					Tuple     =	rename_keys(SCREAMING_SNAKE_CASE__ )
  		replace_params(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Initialize processor
  		snake_case     :					Union[str, Any]     =	get_processor()
  		snake_case     :					List[Any]     =	processor(
  		    images=prepare_img()   ,					text='''A picture of a cat'''   ,					padding='''max_length'''   ,					max_length=64   ,					return_tensors='''pt''' )
  
  		# HF model inference
  		hf_model.eval()
  		with torch.no_grad():
  				snake_case     :					Tuple     =	hf_model(**SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					List[Any]     =	outputs.image_embeds.detach().numpy()
  		snake_case     :					Dict     =	outputs.text_embeds.detach().numpy()
  
  		# Original model inference
  		snake_case     :					Dict     =	False
  		snake_case     :					Optional[Any]     =	EfficientNetImageProcessor(
  		    do_center_crop=SCREAMING_SNAKE_CASE__   ,					do_rescale=SCREAMING_SNAKE_CASE__   ,					do_normalize=SCREAMING_SNAKE_CASE__   ,					include_top=SCREAMING_SNAKE_CASE__   ,					resample=Image.BILINEAR   ,					)
  		snake_case     :					str     =	tf_image_processor(images=prepare_img()   ,					return_tensors='''tf'''   ,					data_format='''channels_last''' )['''pixel_values''']
  		snake_case     :					Tuple     =	tok(tf.constant(['''A picture of a cat'''] ) )
  
  		snake_case     :					Any     =	original_model.image_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )
  		snake_case     :					str     =	original_model.text_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					Union[str, Any]     =	tf.nn.la_normalize(SCREAMING_SNAKE_CASE__   ,					axis=-1 )
  		snake_case     :					List[Any]     =	tf.nn.la_normalize(SCREAMING_SNAKE_CASE__   ,					axis=-1 )
  
  		# Check whether original and HF model outputs match  -> np.allclose
  		if not np.allclose(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 ):
  				raise ValueError('''The predicted image features are not the same.''' )
  		if not np.allclose(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 ):
  				raise ValueError('''The predicted text features are not the same.''' )
  		print('''Model outputs match!''' )
  
  		if save_model:
  				# Create folder to save model
  				if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
  						os.mkdir(SCREAMING_SNAKE_CASE__ )
  				# Save converted model and image processor
  				hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  				processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  		if push_to_hub:
  				# Push model and image processor to hub
  				print('''Pushing converted ALIGN to the hub...''' )
  				processor.push_to_hub('''align-base''' )
  				hf_model.push_to_hub('''align-base''' )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--checkpoint_path",
  						    default="./weights/model-weights",
  						    type=str,
  						    help="Path to the pretrained TF ALIGN checkpoint.",
  						)
  						parser.add_argument(
  						    "--pytorch_dump_folder_path",
  						    default="hf_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 processor to the hub")
  
  						lowercase__						=			parser.parse_args()
  						convert_align_checkpoint(args.checkpoint_path, 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__			)
  
  
  