






"""simple docstring"""


from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class  snake_case		(							_lowerCAmelCase						):


			'''simple docstring'''

			def __init__(     self			:					List[Any],	_lowerCamelCase			:					AutoencoderKL,	_lowerCamelCase			:					CLIPTextModel,	_lowerCamelCase			:					CLIPTokenizer,	_lowerCamelCase			:					UNetaDConditionModel,	_lowerCamelCase			:					DDIMScheduler,	_lowerCamelCase			:					float = 0.93,	_lowerCamelCase			:					bool = True,	):

					'''simple docstring'''
					__A						      =			mixing_coeff
					__A						      =			leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=_lowerCamelCase,	text_encoder=_lowerCamelCase,	tokenizer=_lowerCamelCase,	unet=_lowerCamelCase,	scheduler=_lowerCamelCase,	)

					__A						      =			2 ** (len(self.vae.config.block_out_channels  ) - 1)
					__A						      =			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor  )


			def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					str,	_lowerCamelCase			:					Optional[str] = None,	_lowerCamelCase			:					bool = False  ):

					'''simple docstring'''
					__A						      =			self.tokenizer(
					    _lowerCamelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=_lowerCamelCase,	return_tensors='''pt''',	)

					__A						      =			self.text_encoder(text_inputs.input_ids.to(self.device  )  ).last_hidden_state

					__A						      =			prompt_embeds.to(dtype=self.text_encoder.dtype,	device=self.device  )

					if do_classifier_free_guidance:
							__A						      =			'''''' if negative_prompt is None else negative_prompt

							__A						      =			self.tokenizer(
							    _lowerCamelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=_lowerCamelCase,	return_tensors='''pt''',	)

							__A						      =			self.text_encoder(uncond_input.input_ids.to(self.device  )  ).last_hidden_state

							__A						      =			torch.cat([negative_prompt_embeds, prompt_embeds]  )

					return prompt_embeds


			def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor  ):

					'''simple docstring'''
					__A						      =			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					__A						      =			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]


			def     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor  ):

					'''simple docstring'''
					__A						      =			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					__A						      =			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]


			def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					torch.Tensor  ):

					'''simple docstring'''
					__A						      =			int(_lowerCamelCase  )

					__A						      =			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod


			def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	):

					'''simple docstring'''
					__A						      =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					__A      ,   __A						      =			self._get_alpha_and_beta(_lowerCamelCase  )
					__A      ,   __A						      =			self._get_alpha_and_beta(_lowerCamelCase  )

					__A						      =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
					__A						      =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					__A						      =			(base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype  )


			def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	):

					'''simple docstring'''
					__A						      =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					__A      ,   __A						      =			self._get_alpha_and_beta(_lowerCamelCase  )
					__A      ,   __A						      =			self._get_alpha_and_beta(_lowerCamelCase  )

					__A						      =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
					__A						      =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					__A						      =			a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype  )


			@torch.no_grad()
			def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					torch.Tensor  ):

					'''simple docstring'''
					__A						      =			1 / self.vae.config.scaling_factor * latents
					__A						      =			self.vae.decode(_lowerCamelCase  ).sample
					__A						      =			(image / 2 + 0.5).clamp(0,	1  )
					return image


			@torch.no_grad()
			def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Image.Image,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					float,	_lowerCamelCase			:					Optional[torch.Generator] = None,	):

					'''simple docstring'''
					__A						      =			guidance_scale > 1.0

					__A						      =			image.to(device=self.device,	dtype=text_embeds.dtype  )
					__A						      =			self.vae.encode(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )

					__A						      =			self.vae.config.scaling_factor * latent

					__A						      =			[latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(_lowerCamelCase  ),	total=len(_lowerCamelCase  )  ):
							__A						      =			self.noise_mixing_layer(x=coupled_latents[0],	y=coupled_latents[1]  )

							# j - model_input index, k - base index
							for j in range(2  ):
									__A						      =			j ^ 1

									if self.leapfrog_steps:
											if i % 2 == 0:
													__A      ,   __A						      =			j, k

									__A						      =			coupled_latents[j]
									__A						      =			coupled_latents[k]

									__A						      =			torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

									__A						      =			self.unet(_lowerCamelCase,	_lowerCamelCase,	encoder_hidden_states=_lowerCamelCase  ).sample

									if do_classifier_free_guidance:
											__A      ,   __A						      =			noise_pred.chunk(2  )
											__A						      =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									__A      ,   __A						      =			self.noise_step(
									    base=_lowerCamelCase,	model_input=_lowerCamelCase,	model_output=_lowerCamelCase,	timestep=_lowerCamelCase,	)

									__A						      =			model_input

					return coupled_latents






			@torch.no_grad()
			def __call__(     self			:					Union[str, Any],	_lowerCamelCase			:					str,	_lowerCamelCase			:					str,	_lowerCamelCase			:					Image.Image,	_lowerCamelCase			:					float = 3.0,	_lowerCamelCase			:					int = 50,	_lowerCamelCase			:					float = 0.8,	_lowerCamelCase			:					Optional[str] = None,	_lowerCamelCase			:					Optional[torch.Generator] = None,	_lowerCamelCase			:					Optional[str] = "pil",	):

					'''simple docstring'''
					__A						      =			guidance_scale > 1.0

					__A						      =			self.image_processor.preprocess(_lowerCamelCase  )

					__A						      =			self._encode_prompt(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )
					__A						      =			self._encode_prompt(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )

					self.scheduler.set_timesteps(_lowerCamelCase,	self.device  )

					__A						      =			num_inference_steps - int(num_inference_steps * strength  )
					__A						      =			self.scheduler.timesteps[t_limit:]
					__A						      =			fwd_timesteps.flip(0  )

					__A						      =			self.prepare_latents(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )

					for i, t in tqdm(enumerate(_lowerCamelCase  ),	total=len(_lowerCamelCase  )  ):
							# j - model_input index, k - base index
							for k in range(2  ):
									__A						      =			k ^ 1

									if self.leapfrog_steps:
											if i % 2 == 1:
													__A      ,   __A						      =			j, k

									__A						      =			coupled_latents[j]
									__A						      =			coupled_latents[k]

									__A						      =			torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

									__A						      =			self.unet(_lowerCamelCase,	_lowerCamelCase,	encoder_hidden_states=_lowerCamelCase  ).sample

									if do_classifier_free_guidance:
											__A      ,   __A						      =			noise_pred.chunk(2  )
											__A						      =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									__A      ,   __A						      =			self.denoise_step(
									    base=_lowerCamelCase,	model_input=_lowerCamelCase,	model_output=_lowerCamelCase,	timestep=_lowerCamelCase,	)

									__A						      =			model_input

							__A						      =			self.denoise_mixing_layer(x=coupled_latents[0],	y=coupled_latents[1]  )

					# either one is fine
					__A						      =			coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
							__A						      =			(
							    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
							    '''`pil`, `np`, `pt`, `latent`'''
							)
							deprecate('''Unsupported output_type''',	'''1.0.0''',	_lowerCamelCase,	standard_warn=_lowerCamelCase  )
							__A						      =			'''np'''

					if output_type == "latent":
							__A						      =			final_latent
					else:
							__A						      =			self.decode_latents(_lowerCamelCase  )
							__A						      =			self.image_processor.postprocess(_lowerCamelCase,	output_type=_lowerCamelCase  )

					return image












"""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		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(     self			:					Optional[int],	_lowerCamelCase			:					Any,	):

     '''simple docstring'''
     __A						      =			parent
     __A						      =			13
     __A						      =			7
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			99
     __A						      =			32
     __A						      =			2
     __A						      =			4
     __A						      =			37
     __A						      =			'''gelu'''
     __A						      =			0.1
     __A						      =			0.1
     __A						      =			5_12
     __A						      =			16
     __A						      =			2
     __A						      =			0.02
     __A						      =			3
     __A						      =			4
     __A						      =			None
     __A						      =			self.vocab_size - 1


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.vocab_size  )

     __A						      =			None
     if self.use_input_mask:
       __A						      =			random_attention_mask([self.batch_size, self.seq_length]  )

     __A						      =			None
     if self.use_token_type_ids:
       __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.type_vocab_size  )

     __A						      =			None
     if self.use_mc_token_ids:
       __A						      =			ids_tensor([self.batch_size, self.num_choices],	self.seq_length  )

     __A						      =			None
     __A						      =			None
     __A						      =			None
     if self.use_labels:
       __A						      =			ids_tensor([self.batch_size],	self.type_sequence_label_size  )
       __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.num_labels  )
       __A						      =			ids_tensor([self.batch_size],	self.num_choices  )

     __A						      =			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,	)

     __A						      =			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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					int,	_lowerCamelCase			:					List[Any],	*_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			TFCTRLModel(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
     __A						      =			model(_lowerCamelCase  )

     __A						      =			[input_ids, None, input_mask]  # None is the input for 'past'
     __A						      =			model(_lowerCamelCase  )

     __A						      =			model(_lowerCamelCase  )

     self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Optional[Any],	*_lowerCamelCase			:					Tuple  ):

     '''simple docstring'''
     __A						      =			TFCTRLLMHeadModel(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.seq_length, self.vocab_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					List[str],	*_lowerCamelCase			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.num_labels
     __A						      =			ids_tensor([self.batch_size],	self.type_sequence_label_size  )
     __A						      =			{
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''labels''': sequence_labels,
     }
     __A						      =			TFCTRLForSequenceClassification(_lowerCamelCase  )
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.prepare_config_and_inputs()

     (
         (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   
     )						      =			config_and_inputs

     __A						      =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict





@require_tf
class  snake_case		(							_lowerCAmelCase       ,     _lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     Optional[int]									=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A_						:     str									=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
   A_						:     int									=			(
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_						:     Union[str, Any]									=			False
   A_						:     Dict									=			False


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					int,	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					str,	_lowerCamelCase			:					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     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			TFCTRLModelTester(self  )
     __A						      =			ConfigTester(self,	config_class=_lowerCamelCase,	n_embd=37  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()
     __A						      =			[TFCTRLLMHeadModel]
     __A						      =			[TFCTRLForSequenceClassification]

     for model_class in self.all_model_classes:
       __A						      =			model_class(_lowerCamelCase  )
       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:
         __A						      =			model.get_output_embeddings()
         assert isinstance(_lowerCamelCase,	tf.keras.layers.Layer  )
         __A						      =			model.get_bias()
         assert isinstance(_lowerCamelCase,	_lowerCamelCase  )
         for k, v in name.items():
           assert isinstance(_lowerCamelCase,	tf.Variable  )
       elif model_class in list_other_models_with_output_ebd:
         __A						      =			model.get_output_embeddings()
         assert isinstance(_lowerCamelCase,	tf.keras.layers.Layer  )
         __A						      =			model.get_bias()
         assert name is None
       else:
         __A						      =			model.get_output_embeddings()
         assert x is None
         __A						      =			model.get_bias()
         assert name is None






   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A						      =			TFCTRLModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_tf
class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			TFCTRLLMHeadModel.from_pretrained('''ctrl'''  )
     __A						      =			tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]],	dtype=tf.intaa  )  # Legal the president is
     __A						      =			[
         1_18_59,
         0,
         16_11,
         8,
         5,
         1_50,
         2_64_49,
         2,
         19,
         3_48,
         4_69,
         3,
         25_95,
         48,
         2_07_40,
         24_65_33,
         24_65_33,
         19,
         30,
         5,
     ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

     __A						      =			model.generate(_lowerCamelCase,	do_sample=_lowerCamelCase  )
     self.assertListEqual(output_ids[0].numpy().tolist(),	_lowerCamelCase  )




