
'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class   snake_case__					(			UpperCAmelCase__				):







		"""simple docstring"""

		def __init__(			self							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[Any]     ,  )	-> Tuple:






				"""simple docstring"""







				super().__init__()

				self.register_modules(vae=lowerCamelCase__     ,  text_encoder=lowerCamelCase__     ,  tokenizer=lowerCamelCase__     ,  unet=lowerCamelCase__     ,  scheduler=lowerCamelCase__			)



		def 			lowerCAmelCase							(			self							: List[str]     ,  UpperCamelCase__							: str			)	-> List[str]:






				"""simple docstring"""







				with torch.no_grad():
						snake_case     :					Union[str, Any]     =	self.vae.encode(tfms.ToTensor()(lowerCamelCase__			).unsqueeze(0			).to(self.device			) * 2 - 1			)
						snake_case     :					Union[str, Any]     =	0.18_215 * latent.latent_dist.sample()
				return latent



		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: int			)	-> Optional[int]:






				"""simple docstring"""







				snake_case     :					Tuple     =	(1 / 0.18_215) * latent
				with torch.no_grad():
						snake_case     :					int     =	self.vae.decode(lowerCamelCase__			).sample
				snake_case     :					List[str]     =	(img / 2 + 0.5).clamp(0     ,  1			)
				snake_case     :					Optional[Any]     =	img.detach().cpu().permute(0     ,  2     ,  3     ,  1			).numpy()
				snake_case     :					List[Any]     =	(img * 255).round().astype('''uint8'''			)
				return Image.fromarray(img[0]			)



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: Dict			)	-> Dict:






				"""simple docstring"""







				snake_case     :					List[str]     =	self.tokenizer(
				    lowerCamelCase__     ,  padding='''max_length'''     ,  max_length=self.tokenizer.model_max_length     ,  truncation=lowerCamelCase__     ,  return_tensors='''pt'''     ,  )

				snake_case     :					int     =	self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

				snake_case     :					Any     =	self.tokenizer(
				    ''''''     ,  padding='''max_length'''     ,  max_length=self.tokenizer.model_max_length     ,  truncation=lowerCamelCase__     ,  return_tensors='''pt'''     ,  )

				snake_case     :					Dict     =	self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

				return torch.cat([uncond_embedding, text_embedding]			)



		def __call__(			self							: Optional[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Optional[Any] = 0.3     ,  UpperCamelCase__							: Union[str, Any] = 0.6     ,  UpperCamelCase__							: Union[str, Any] = 0.5     ,  UpperCamelCase__							: List[Any] = 42     ,  UpperCamelCase__							: int = 50     ,  UpperCamelCase__							: List[str] = 7.5     ,  )	-> Union[str, Any]:






				"""simple docstring"""







				snake_case     :					Dict     =	steps - int(kmin * steps			)
				snake_case     :					Optional[int]     =	steps - int(kmax * steps			)

				snake_case     :					List[Any]     =	self.prep_text(lowerCamelCase__			)

				self.scheduler.set_timesteps(lowerCamelCase__			)

				snake_case     :					Union[str, Any]     =	img.size
				snake_case     :					str     =	self.encode(lowerCamelCase__			)

				torch.manual_seed(lowerCamelCase__			)
				snake_case     :					Any     =	torch.randn(
				    (1, self.unet.config.in_channels, height // 8, width // 8)     ,  ).to(self.device			)

				snake_case     :					Optional[Any]     =	self.scheduler.add_noise(
				    lowerCamelCase__     ,  lowerCamelCase__     ,  timesteps=self.scheduler.timesteps[tmax]     ,  )

				snake_case     :					Dict     =	torch.cat([latents] * 2			)

				snake_case     :					Any     =	self.scheduler.scale_model_input(lowerCamelCase__     ,  self.scheduler.timesteps[tmax]			)

				with torch.no_grad():
						snake_case     :					Tuple     =	self.unet(
						    lowerCamelCase__     ,  self.scheduler.timesteps[tmax]     ,  encoder_hidden_states=lowerCamelCase__     ,  ).sample

				snake_case     :					Optional[int]     =	pred.chunk(2			)
				snake_case     :					str     =	pred_uncond + guidance_scale * (pred_text - pred_uncond)

				snake_case     :					Any     =	self.scheduler.step(lowerCamelCase__     ,  self.scheduler.timesteps[tmax]     ,  lowerCamelCase__			).prev_sample

				for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
						if i > tmax:
								if i < tmin:  # layout generation phase
										snake_case     :					Tuple     =	self.scheduler.add_noise(
										    lowerCamelCase__     ,  lowerCamelCase__     ,  timesteps=lowerCamelCase__     ,  )

										snake_case     :					Any     =	(mix_factor * latents) + (
										    1 - mix_factor
										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
										snake_case     :					Dict     =	torch.cat([input] * 2			)

								else:  # content generation phase
										snake_case     :					Union[str, Any]     =	torch.cat([latents] * 2			)

								snake_case     :					Any     =	self.scheduler.scale_model_input(lowerCamelCase__     ,  lowerCamelCase__			)

								with torch.no_grad():
										snake_case     :					int     =	self.unet(
										    lowerCamelCase__     ,  lowerCamelCase__     ,  encoder_hidden_states=lowerCamelCase__     ,  ).sample

								snake_case     :					Tuple     =	pred.chunk(2			)
								snake_case     :					Dict     =	pred_uncond + guidance_scale * (pred_text - pred_uncond)

								snake_case     :					Optional[Any]     =	self.scheduler.step(lowerCamelCase__     ,  lowerCamelCase__     ,  lowerCamelCase__			).prev_sample

				return self.decode(lowerCamelCase__			)





'''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__			)


