






'''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 				_UpperCAmelCase  (						__lowerCAmelCase	):




    def __init__(	self     :				Dict     ,   a     :				Optional[int]     ,   ):






       '''simple docstring'''






       lowercase_		:			Optional[Any]							=   parent
       lowercase_		:			Optional[int]							=   1_3
       lowercase_		:			Tuple							=   7
       lowercase_		:			Optional[int]							=   True
       lowercase_		:			Optional[Any]							=   True
       lowercase_		:			Any							=   True
       lowercase_		:			List[str]							=   True
       lowercase_		:			Dict							=   True
       lowercase_		:			int							=   9_9
       lowercase_		:			int							=   3_2
       lowercase_		:			List[Any]							=   2
       lowercase_		:			Any							=   4
       lowercase_		:			int							=   3_7
       lowercase_		:			Optional[Any]							=   "gelu"
       lowercase_		:			str							=   0.1
       lowercase_		:			Dict							=   0.1
       lowercase_		:			Optional[int]							=   5_1_2
       lowercase_		:			Optional[int]							=   1_6
       lowercase_		:			int							=   2
       lowercase_		:			Optional[int]							=   0.02
       lowercase_		:			List[str]							=   3
       lowercase_		:			List[str]							=   4
       lowercase_		:			Optional[Any]							=   None
       lowercase_		:			Tuple							=   self.vocab_size - 1




    def  lowerCAmelCase__ (	self     :				List[str]	):






       '''simple docstring'''






       lowercase_		:			Tuple							=   ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size	)

       lowercase_		:			List[Any]							=   None
       if self.use_input_mask:
          lowercase_		:			Dict							=   random_attention_mask([self.batch_size, self.seq_length]	)

       lowercase_		:			Union[str, Any]							=   None
       if self.use_token_type_ids:
          lowercase_		:			str							=   ids_tensor([self.batch_size, self.seq_length]     ,   self.type_vocab_size	)

       lowercase_		:			Optional[Any]							=   None
       if self.use_mc_token_ids:
          lowercase_		:			List[Any]							=   ids_tensor([self.batch_size, self.num_choices]     ,   self.seq_length	)

       lowercase_		:			Union[str, Any]							=   None
       lowercase_		:			str							=   None
       lowercase_		:			Dict							=   None
       if self.use_labels:
          lowercase_		:			Dict							=   ids_tensor([self.batch_size]     ,   self.type_sequence_label_size	)
          lowercase_		:			Dict							=   ids_tensor([self.batch_size, self.seq_length]     ,   self.num_labels	)
          lowercase_		:			List[str]							=   ids_tensor([self.batch_size]     ,   self.num_choices	)

       lowercase_		:			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     ,   )

       lowercase_		:			Optional[int]							=   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     :				List[str]     ,   a     :				List[str]     ,   a     :				Any     ,   a     :				Dict     ,   a     :				str     ,   a     :				int     ,   *a     :				Dict	):






       '''simple docstring'''






       lowercase_		:			Dict							=   TFCTRLModel(config=_lowerCamelCase	)
       lowercase_		:			List[str]							=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       lowercase_		:			Optional[Any]							=   model(_lowerCamelCase	)

       lowercase_		:			Tuple							=   [input_ids, None, input_mask]  # None is the input for 'past'
       lowercase_		:			str							=   model(_lowerCamelCase	)

       lowercase_		:			Optional[Any]							=   model(_lowerCamelCase	)

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




    def  lowerCAmelCase__ (	self     :				Optional[Any]     ,   a     :				int     ,   a     :				Optional[Any]     ,   a     :				Dict     ,   a     :				Optional[int]     ,   a     :				Optional[Any]     ,   *a     :				int	):






       '''simple docstring'''






       lowercase_		:			Dict							=   TFCTRLLMHeadModel(config=_lowerCamelCase	)
       lowercase_		:			Tuple							=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       lowercase_		:			List[Any]							=   model(_lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)	)




    def  lowerCAmelCase__ (	self     :				Dict     ,   a     :				Tuple     ,   a     :				List[str]     ,   a     :				List[str]     ,   a     :				Dict     ,   a     :				Optional[int]     ,   *a     :				Dict	):






       '''simple docstring'''






       lowercase_		:			str							=   self.num_labels
       lowercase_		:			str							=   ids_tensor([self.batch_size]     ,   self.type_sequence_label_size	)
       lowercase_		:			int							=   {
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       lowercase_		:			int							=   TFCTRLForSequenceClassification(_lowerCamelCase	)
       lowercase_		:			int							=   model(_lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)	)





    def  lowerCAmelCase__ (	self     :				int	):






       '''simple docstring'''






       lowercase_		:			Any							=   self.prepare_config_and_inputs()

       (
           (
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						(
           lowercase_      
       )			,						      
       )		:			Tuple							=   config_and_inputs

       lowercase_		:			Any							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict


@require_tf
class 				_UpperCAmelCase  (						__lowerCAmelCase ,    __lowerCAmelCase ,    unittest.TestCase	):
    __lowerCamelCase:      Union[str, Any]										=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
    __lowerCamelCase:      Any										=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
    __lowerCamelCase:      Any										=			(
        {
            '''feature-extraction''': TFCTRLModel,
            '''text-classification''': TFCTRLForSequenceClassification,
            '''text-generation''': TFCTRLLMHeadModel,
            '''zero-shot''': TFCTRLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase:      Any										=			False
    __lowerCamelCase:      Union[str, Any]										=			False




    def  lowerCAmelCase__ (	self     :				Any     ,   a     :				Union[str, Any]     ,   a     :				Dict     ,   a     :				Optional[int]     ,   a     :				List[Any]     ,   a     :				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[int]	):






       '''simple docstring'''






       lowercase_		:			Tuple							=   TFCTRLModelTester(self	)
       lowercase_		:			Tuple							=   ConfigTester(self     ,   config_class=_lowerCamelCase     ,   n_embd=3_7	)




    def  lowerCAmelCase__ (	self     :				List[str]	):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    def  lowerCAmelCase__ (	self     :				str	):






       '''simple docstring'''






       lowercase_		:			Dict							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase	)




    def  lowerCAmelCase__ (	self     :				Union[str, Any]	):






       '''simple docstring'''






       lowercase_		:			str							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase	)




    def  lowerCAmelCase__ (	self     :				List[str]	):






       '''simple docstring'''






       lowercase_		:			Optional[Any]							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase	)




    def  lowerCAmelCase__ (	self     :				Tuple	):






       '''simple docstring'''






       lowercase_			,						lowercase_		:			Tuple							=   self.model_tester.prepare_config_and_inputs_for_common()
       lowercase_		:			Dict							=   [TFCTRLLMHeadModel]
       lowercase_		:			int							=   [TFCTRLForSequenceClassification]

       for model_class in self.all_model_classes:
          lowercase_		:			Any							=   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:
             lowercase_		:			int							=   model.get_output_embeddings()
             assert isinstance(_lowerCamelCase     ,   tf.keras.layers.Layer	)
             lowercase_		:			Any							=   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:
             lowercase_		:			int							=   model.get_output_embeddings()
             assert isinstance(_lowerCamelCase     ,   tf.keras.layers.Layer	)
             lowercase_		:			Dict							=   model.get_bias()
             assert name is None
          else:
             lowercase_		:			str							=   model.get_output_embeddings()
             assert x is None
             lowercase_		:			int							=   model.get_bias()
             assert name is None





    @slow
    def  lowerCAmelCase__ (	self     :				Tuple	):






       '''simple docstring'''






       for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_		:			Union[str, Any]							=   TFCTRLModel.from_pretrained(_lowerCamelCase	)
          self.assertIsNotNone(_lowerCamelCase	)


@require_tf
class 				_UpperCAmelCase  (						unittest.TestCase	):




    @slow
    def  lowerCAmelCase__ (	self     :				Dict	):






       '''simple docstring'''






       lowercase_		:			Any							=   TFCTRLLMHeadModel.from_pretrained("ctrl"	)
       lowercase_		:			Union[str, Any]							=   tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]     ,   dtype=tf.intaa	)  # Legal the president is
       lowercase_		:			Any							=   [
           1_1_8_5_9,
           0,
           1_6_1_1,
           8,
           5,
           1_5_0,
           2_6_4_4_9,
           2,
           1_9,
           3_4_8,
           4_6_9,
           3,
           2_5_9_5,
           4_8,
           2_0_7_4_0,
           2_4_6_5_3_3,
           2_4_6_5_3_3,
           1_9,
           3_0,
           5,
       ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

       lowercase_		:			Optional[Any]							=   model.generate(_lowerCamelCase     ,   do_sample=_lowerCamelCase	)
       self.assertListEqual(output_ids[0].numpy().tolist()     ,   _lowerCamelCase	)





"""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    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

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

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

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

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

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

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=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
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

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

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

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

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


