






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


A_	:	Tuple				=				getLogger(__name__)


A_	:	Optional[Any]				=				4
A_	:	Optional[Any]				=				7

A_	:	Tuple				=				{
    'float16': torch.floataa,
    'float32': torch.floataa,
}

def 						snake_case      (UpperCAmelCase__						) ->       List[str]:
  if isinstance(UpperCAmelCase__			,					torch.Tensor						):
    return image
  elif isinstance(UpperCAmelCase__			,					PIL.Image.Image						):
    UpperCamelCase_:      List[str]					   =       [image]

  if isinstance(image[0]			,					PIL.Image.Image						):
    UpperCamelCase_   ,UpperCamelCase_:      Any					   =       image[0].size
    UpperCamelCase_   ,UpperCamelCase_:      Optional[int]					   =       (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

    UpperCamelCase_:      List[str]					   =       [np.array(i.resize((w, h)						)						)[None, :] for i in image]
    UpperCamelCase_:      int					   =       np.concatenate(UpperCAmelCase__			,					axis=0						)
    UpperCamelCase_:      Any					   =       np.array(UpperCAmelCase__						).astype(np.floataa						) / 255.0
    UpperCamelCase_:      int					   =       image.transpose(0			,					3			,					1			,					2						)
    UpperCamelCase_:      Union[str, Any]					   =       2.0 * image - 1.0
    UpperCamelCase_:      Dict					   =       torch.from_numpy(UpperCAmelCase__						)
  elif isinstance(image[0]			,					torch.Tensor						):
    UpperCamelCase_:      Union[str, Any]					   =       torch.cat(UpperCAmelCase__			,					dim=0						)

  return image




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = 3_5_0						,    ):
         super().__init__(
             vae=_lowerCamelCase						,    text_encoder=_lowerCamelCase						,    tokenizer=_lowerCamelCase						,    unet=_lowerCamelCase						,    low_res_scheduler=_lowerCamelCase						,    scheduler=_lowerCamelCase						,    safety_checker=_lowerCamelCase						,    feature_extractor=_lowerCamelCase						,    watermarker=_lowerCamelCase						,    max_noise_level=_lowerCamelCase						,    )





       def __call__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = 7_5						,    _lowerCamelCase = 9.0						,    _lowerCamelCase = 2_0						,    _lowerCamelCase = None						,    _lowerCamelCase = 1						,    _lowerCamelCase = 0.0						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = "pil"						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    _lowerCamelCase = 1						,    ):
         self.check_inputs(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

         # 2. Define call parameters
         if prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      List[Any]					   =       1
         elif prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      Optional[Any]					   =       len(_lowerCamelCase							)
         else:
           UpperCamelCase_:      Optional[Any]					   =       prompt_embeds.shape[0]

         UpperCamelCase_:      Any					   =       self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCamelCase_:      List[Any]					   =       guidance_scale > 1.0

         # 3. Encode input prompt
         UpperCamelCase_:      Dict					   =       self._encode_prompt(
             _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    prompt_embeds=_lowerCamelCase						,    negative_prompt_embeds=_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

         # 4. Preprocess image
         UpperCamelCase_:      Optional[Any]					   =       preprocess(_lowerCamelCase							)
         UpperCamelCase_:      int					   =       image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(_lowerCamelCase						,    device=_lowerCamelCase							)
         UpperCamelCase_:      Dict					   =       self.scheduler.timesteps

         # 5. Add noise to image
         UpperCamelCase_:      Union[str, Any]					   =       torch.tensor([noise_level]						,    dtype=torch.long						,    device=_lowerCamelCase							)
         UpperCamelCase_:      List[str]					   =       torch.randn(image.shape						,    generator=_lowerCamelCase						,    device=_lowerCamelCase						,    dtype=_lowerCamelCase							)
         UpperCamelCase_:      str					   =       self.low_res_scheduler.add_noise(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

         UpperCamelCase_:      Dict					   =       2 if do_classifier_free_guidance else 1
         UpperCamelCase_:      List[Any]					   =       np.concatenate([image] * batch_multiplier * num_images_per_prompt							)
         UpperCamelCase_:      str					   =       np.concatenate([noise_level] * image.shape[0]							)

         # 6. Prepare latent variables
         UpperCamelCase_   ,UpperCamelCase_:      Optional[int]					   =       image.shape[2:]
         UpperCamelCase_:      Dict					   =       self.prepare_latents(
             batch_size * num_images_per_prompt						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    )

         # 7. Check that sizes of image and latents match
         UpperCamelCase_:      Any					   =       image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
           raise ValueError(
               'Incorrect configuration settings! The config of `pipeline.unet` expects'
               f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
               f''' `num_channels_image`: {num_channels_image} '''
               f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
               ' `pipeline.unet` or your `image` input.'							)

         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         UpperCamelCase_:      str					   =       self.prepare_extra_step_kwargs(_lowerCamelCase						,    _lowerCamelCase							)

         UpperCamelCase_:      List[Any]					   =       next(
             (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')						,    'tensor(float)'							)
         UpperCamelCase_:      List[Any]					   =       ORT_TO_NP_TYPE[timestep_dtype]

         # 9. Denoising loop
         UpperCamelCase_:      Optional[int]					   =       len(_lowerCamelCase							) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=_lowerCamelCase							) as progress_bar:
           for i, t in enumerate(_lowerCamelCase							):
             # expand the latents if we are doing classifier free guidance
             UpperCamelCase_:      Optional[int]					   =       np.concatenate([latents] * 2							) if do_classifier_free_guidance else latents

             # concat latents, mask, masked_image_latents in the channel dimension
             UpperCamelCase_:      Tuple					   =       self.scheduler.scale_model_input(_lowerCamelCase						,    _lowerCamelCase							)
             UpperCamelCase_:      Optional[int]					   =       np.concatenate([latent_model_input, image]						,    axis=1							)

             # timestep to tensor
             UpperCamelCase_:      Any					   =       np.array([t]						,    dtype=_lowerCamelCase							)

             # predict the noise residual
             UpperCamelCase_:      int					   =       self.unet(
                 sample=_lowerCamelCase						,    timestep=_lowerCamelCase						,    encoder_hidden_states=_lowerCamelCase						,    class_labels=noise_level.astype(np.intaa							)						,    )[0]

             # perform guidance
             if do_classifier_free_guidance:
               UpperCamelCase_   ,UpperCamelCase_:      Optional[int]					   =       np.split(_lowerCamelCase						,    2							)
             UpperCamelCase_:      int					   =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase_:      Optional[Any]					   =       self.scheduler.step(
                 torch.from_numpy(_lowerCamelCase							)						,    _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							).prev_sample

             # call the callback, if provided
             if i == len(_lowerCamelCase							) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
               progress_bar.update()
               if callback is not None and i % callback_steps == 0:
                 callback(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

        # 10. Post-processing
         UpperCamelCase_:      Any					   =       self.decode_latents(latents.float()							)

         # 11. Convert to PIL
         if output_type == "pil":
           UpperCamelCase_:      Union[str, Any]					   =       self.numpy_to_pil(_lowerCamelCase							)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Dict					   =       1 / 0.0_8_3_3_3 * latents
         UpperCamelCase_:      Tuple					   =       self.vae(latent_sample=_lowerCamelCase							)[0]
         UpperCamelCase_:      List[str]					   =       np.clip(image / 2 + 0.5						,    0						,    1							)
         UpperCamelCase_:      Optional[int]					   =       image.transpose((0, 2, 3, 1)							)
         return image





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    ):
         if prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      str					   =       1
         elif prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      Optional[int]					   =       len(_lowerCamelCase							)
         else:
           UpperCamelCase_:      Dict					   =       prompt_embeds.shape[0]

         if prompt_embeds is None:
           UpperCamelCase_:      str					   =       self.tokenizer(
               _lowerCamelCase						,    padding='max_length'						,    max_length=self.tokenizer.model_max_length						,    truncation=_lowerCamelCase						,    return_tensors='pt'						,    )
           UpperCamelCase_:      Tuple					   =       text_inputs.input_ids
           UpperCamelCase_:      List[str]					   =       self.tokenizer(_lowerCamelCase						,    padding='longest'						,    return_tensors='pt'							).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
               _lowerCamelCase						,    _lowerCamelCase							):
             UpperCamelCase_:      List[str]					   =       self.tokenizer.batch_decode(
                 untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
             logger.warning(
                 'The following part of your input was truncated because CLIP can only handle sequences up to'
                 f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)

           # no positional arguments to text_encoder
           UpperCamelCase_:      Tuple					   =       self.text_encoder(
               input_ids=text_input_ids.int().to(_lowerCamelCase							)						,    )
           UpperCamelCase_:      Tuple					   =       prompt_embeds[0]

         UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Dict					   =       prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase_:      Optional[Any]					   =       prompt_embeds.repeat(1						,    _lowerCamelCase							)
         UpperCamelCase_:      List[Any]					   =       prompt_embeds.reshape(bs_embed * num_images_per_prompt						,    _lowerCamelCase						,    -1							)

         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
           UpperCamelCase_:      List[str]
           if negative_prompt is None:
             UpperCamelCase_:      Dict					   =       [''] * batch_size
           elif type(_lowerCamelCase							) is not type(_lowerCamelCase							):
             raise TypeError(
                 f'''`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCamelCase							)} !='''
                 f''' {type(_lowerCamelCase							)}.'''							)
           elif isinstance(_lowerCamelCase						,    _lowerCamelCase							):
             UpperCamelCase_:      Optional[int]					   =       [negative_prompt]
           elif batch_size != len(_lowerCamelCase							):
             raise ValueError(
                 f'''`negative_prompt`: {negative_prompt} has batch size {len(_lowerCamelCase							)}, but `prompt`:'''
                 f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                 ' the batch size of `prompt`.'							)
           else:
             UpperCamelCase_:      Union[str, Any]					   =       negative_prompt

           UpperCamelCase_:      Dict					   =       text_input_ids.shape[-1]
           UpperCamelCase_:      Dict					   =       self.tokenizer(
               _lowerCamelCase						,    padding='max_length'						,    max_length=_lowerCamelCase						,    truncation=_lowerCamelCase						,    return_tensors='pt'						,    )

           # if hasattr(uncond_input, "attention_mask"):
           #     attention_mask = uncond_input.attention_mask.to(device)
           # else:
           #     attention_mask = None

           UpperCamelCase_:      Tuple					   =       self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(_lowerCamelCase							)						,    )
           UpperCamelCase_:      Optional[int]					   =       uncond_embeddings[0]

         if do_classifier_free_guidance:
           UpperCamelCase_:      List[str]					   =       uncond_embeddings.shape[1]
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase_:      str					   =       uncond_embeddings.repeat(1						,    _lowerCamelCase							)
           UpperCamelCase_:      int					   =       uncond_embeddings.reshape(batch_size * num_images_per_prompt						,    _lowerCamelCase						,    -1							)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           UpperCamelCase_:      Optional[int]					   =       np.concatenate([uncond_embeddings, prompt_embeds]							)

         return prompt_embeds







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        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    ):
         UpperCamelCase_:      Tuple					   =       parent
         UpperCamelCase_:      Optional[Any]					   =       1_3
         UpperCamelCase_:      Any					   =       7
         UpperCamelCase_:      Any					   =       True
         UpperCamelCase_:      List[Any]					   =       True
         UpperCamelCase_:      Dict					   =       True
         UpperCamelCase_:      Optional[Any]					   =       True
         UpperCamelCase_:      Tuple					   =       True
         UpperCamelCase_:      Any					   =       9_9
         UpperCamelCase_:      Dict					   =       3_2
         UpperCamelCase_:      Dict					   =       2
         UpperCamelCase_:      List[Any]					   =       4
         UpperCamelCase_:      int					   =       3_7
         UpperCamelCase_:      List[str]					   =       'gelu'
         UpperCamelCase_:      Union[str, Any]					   =       0.1
         UpperCamelCase_:      int					   =       0.1
         UpperCamelCase_:      int					   =       5_1_2
         UpperCamelCase_:      str					   =       1_6
         UpperCamelCase_:      List[str]					   =       2
         UpperCamelCase_:      Optional[int]					   =       0.0_2
         UpperCamelCase_:      int					   =       3
         UpperCamelCase_:      Tuple					   =       4
         UpperCamelCase_:      List[Any]					   =       None
         UpperCamelCase_:      Union[str, Any]					   =       self.vocab_size - 1





       def     _a					(      self							):
         UpperCamelCase_:      Optional[Any]					   =       ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size							)

         UpperCamelCase_:      List[str]					   =       None
         if self.use_input_mask:
           UpperCamelCase_:      str					   =       random_attention_mask([self.batch_size, self.seq_length]							)

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

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

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

         UpperCamelCase_:      List[str]					   =       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						,    )

         UpperCamelCase_:      Tuple					   =       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     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    *_lowerCamelCase							):
         UpperCamelCase_:      Dict					   =       TFCTRLModel(config=_lowerCamelCase							)
         UpperCamelCase_:      int					   =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         UpperCamelCase_:      Optional[int]					   =       model(_lowerCamelCase							)

         UpperCamelCase_:      Optional[int]					   =       [input_ids, None, input_mask]  # None is the input for 'past'
         UpperCamelCase_:      Dict					   =       model(_lowerCamelCase							)

         UpperCamelCase_:      List[str]					   =       model(_lowerCamelCase							)

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





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    *_lowerCamelCase							):
         UpperCamelCase_:      int					   =       TFCTRLLMHeadModel(config=_lowerCamelCase							)
         UpperCamelCase_:      Optional[int]					   =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         UpperCamelCase_:      List[Any]					   =       model(_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    *_lowerCamelCase							):
         UpperCamelCase_:      List[str]					   =       self.num_labels
         UpperCamelCase_:      Dict					   =       ids_tensor([self.batch_size]						,    self.type_sequence_label_size							)
         UpperCamelCase_:      List[Any]					   =       {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'labels': sequence_labels,
         }
         UpperCamelCase_:      List[str]					   =       TFCTRLForSequenceClassification(_lowerCamelCase							)
         UpperCamelCase_:      Tuple					   =       model(_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)							)





       def     _a					(      self							):
         UpperCamelCase_:      int					   =       self.prepare_config_and_inputs()

         (
             (
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,
         ):      str					   =       config_and_inputs

         UpperCamelCase_:      List[str]					   =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict







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





       """simple docstring"""







       a       :	Tuple								=(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       :	Tuple								=False
       a       :	str								=False





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         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     _a					(      self							):
         UpperCamelCase_:      List[str]					   =       TFCTRLModelTester(self							)
         UpperCamelCase_:      List[Any]					   =       ConfigTester(self						,    config_class=_lowerCamelCase						,    n_embd=3_7							)





       def     _a					(      self							):
         self.config_tester.run_common_tests()





       def     _a					(      self							):
         UpperCamelCase_:      Dict					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Optional[int]					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Union[str, Any]					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      Union[str, Any]					   =       self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_:      Optional[Any]					   =       [TFCTRLLMHeadModel]
         UpperCamelCase_:      Optional[Any]					   =       [TFCTRLForSequenceClassification]

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





       @slow
       def     _a					(      self							):
         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_:      Optional[Any]					   =       TFCTRLModel.from_pretrained(_lowerCamelCase							)
           self.assertIsNotNone(_lowerCamelCase							)







@require_tf
class        _lowerCAmelCase( unittest.TestCase      ):





       """simple docstring"""







       @slow
       def     _a					(      self							):
         UpperCamelCase_:      Optional[int]					   =       TFCTRLLMHeadModel.from_pretrained('ctrl'							)
         UpperCamelCase_:      Any					   =       tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]						,    dtype=tf.intaa							)  # Legal the president is
         UpperCamelCase_:      Dict					   =       [
             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

         UpperCamelCase_:      List[str]					   =       model.generate(_lowerCamelCase						,    do_sample=_lowerCamelCase							)
         self.assertListEqual(output_ids[0].numpy().tolist()						,    _lowerCamelCase							)