






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu





class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







 @property
 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  1
      UpperCamelCase__      :       Tuple	    =  3
      UpperCamelCase__      :       Union[str, Any]	    =  (32, 32)

      UpperCamelCase__      :       List[str]	    =  floats_tensor((batch_size, num_channels) + sizes							,       rng=random.Random(0						)						).to(SCREAMING_SNAKE_CASE						)
      return image







 @property
 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__      :       Union[str, Any]	    =  UNetaDConditionModel(
          block_out_channels=(32, 64)							,       layers_per_block=2							,       sample_size=32							,       in_channels=4							,       out_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,       cross_attention_dim=32							,       )
      return model







 @property
 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  AutoencoderKL(
          block_out_channels=[32, 64]							,       in_channels=3							,       out_channels=3							,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,       latent_channels=4							,       )
      return model







 @property
 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__      :       Optional[Any]	    =  CLIPTextConfig(
          bos_token_id=0							,       eos_token_id=2							,       hidden_size=32							,       intermediate_size=37							,       layer_norm_eps=1e-0_5							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=10_00							,       )
      return CLIPTextModel(SCREAMING_SNAKE_CASE						)







 @property
 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      def extract(*SCREAMING_SNAKE_CASE :       Optional[int]							,       **SCREAMING_SNAKE_CASE :       Tuple						):





           class       __a  :







            def __init__(		self :       List[str]						):


                 '''simple docstring'''






                 UpperCamelCase__      :       Tuple	    =  torch.ones([0]						)







            def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       int						):


                 '''simple docstring'''






                 self.pixel_values.to(SCREAMING_SNAKE_CASE						)
                 return self







           return Out()

      return extract







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  "cpu"  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase__      :       List[Any]	    =  self.dummy_cond_unet
      UpperCamelCase__      :       str	    =  DDIMScheduler(
          beta_start=0.0_0_0_8_5							,       beta_end=0.0_1_2							,       beta_schedule="scaled_linear"							,       clip_sample=SCREAMING_SNAKE_CASE							,       set_alpha_to_one=SCREAMING_SNAKE_CASE							,       )

      UpperCamelCase__      :       str	    =  self.dummy_vae
      UpperCamelCase__      :       str	    =  self.dummy_text_encoder
      UpperCamelCase__      :       Optional[Any]	    =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

      # make sure here that pndm scheduler skips prk
      UpperCamelCase__      :       Tuple	    =  StableDiffusionPipeline(
          unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE							,       vae=SCREAMING_SNAKE_CASE							,       text_encoder=SCREAMING_SNAKE_CASE							,       tokenizer=SCREAMING_SNAKE_CASE							,       safety_checker=SCREAMING_SNAKE_CASE							,       feature_extractor=self.dummy_extractor							,       )
      UpperCamelCase__      :       List[Any]	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Union[str, Any]	    =  "A painting of a squirrel eating a burger"

      UpperCamelCase__      :       str	    =  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(0						)
      UpperCamelCase__      :       Any	    =  sd_pipe([prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=6.0							,       num_inference_steps=2							,       output_type="np"						)
      UpperCamelCase__      :       List[str]	    =  output.images

      UpperCamelCase__      :       List[Any]	    =  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=6.0							,       num_inference_steps=2							,       output_type="np"							,       return_dict=SCREAMING_SNAKE_CASE							,       )[0]

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Dict	    =  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      UpperCamelCase__      :       Any	    =  np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  "cpu"  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase__      :       Optional[Any]	    =  self.dummy_cond_unet
      UpperCamelCase__      :       int	    =  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       str	    =  self.dummy_vae
      UpperCamelCase__      :       Tuple	    =  self.dummy_text_encoder
      UpperCamelCase__      :       Tuple	    =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

      # make sure here that pndm scheduler skips prk
      UpperCamelCase__      :       Optional[Any]	    =  StableDiffusionPipeline(
          unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE							,       vae=SCREAMING_SNAKE_CASE							,       text_encoder=SCREAMING_SNAKE_CASE							,       tokenizer=SCREAMING_SNAKE_CASE							,       safety_checker=SCREAMING_SNAKE_CASE							,       feature_extractor=self.dummy_extractor							,       )
      UpperCamelCase__      :       str	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  "A painting of a squirrel eating a burger"
      UpperCamelCase__      :       List[str]	    =  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(0						)
      UpperCamelCase__      :       List[Any]	    =  sd_pipe([prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=6.0							,       num_inference_steps=2							,       output_type="np"						)

      UpperCamelCase__      :       Any	    =  output.images

      UpperCamelCase__      :       Optional[int]	    =  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(0						)
      UpperCamelCase__      :       Union[str, Any]	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=6.0							,       num_inference_steps=2							,       output_type="np"							,       return_dict=SCREAMING_SNAKE_CASE							,       )[0]

      UpperCamelCase__      :       List[Any]	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Dict	    =  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      UpperCamelCase__      :       Tuple	    =  np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  StableDiffusionPipeline.from_pretrained(
          "hf-internal-testing/tiny-stable-diffusion-lms-pipe"							,       safety_checker=SCREAMING_SNAKE_CASE						)
      assert isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
      assert isinstance(pipe.scheduler							,       SCREAMING_SNAKE_CASE						)
      assert pipe.safety_checker is None

      UpperCamelCase__      :       Tuple	    =  pipe("example prompt"							,       num_inference_steps=2						).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Tuple	    =  StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE						)

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      UpperCamelCase__      :       List[Any]	    =  pipe("example prompt"							,       num_inference_steps=2						).images[0]
      assert image is not None







 @unittest.skipIf(torch_device != "cuda"							,       "This test requires a GPU"						)
 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  self.dummy_cond_unet
      UpperCamelCase__      :       Tuple	    =  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  self.dummy_vae
      UpperCamelCase__      :       Tuple	    =  self.dummy_text_encoder
      UpperCamelCase__      :       Any	    =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

      # put models in fp16
      UpperCamelCase__      :       Optional[int]	    =  unet.half()
      UpperCamelCase__      :       Optional[Any]	    =  vae.half()
      UpperCamelCase__      :       Union[str, Any]	    =  bert.half()

      # make sure here that pndm scheduler skips prk
      UpperCamelCase__      :       Tuple	    =  StableDiffusionPipeline(
          unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE							,       vae=SCREAMING_SNAKE_CASE							,       text_encoder=SCREAMING_SNAKE_CASE							,       tokenizer=SCREAMING_SNAKE_CASE							,       safety_checker=SCREAMING_SNAKE_CASE							,       feature_extractor=self.dummy_extractor							,       )
      UpperCamelCase__      :       Dict	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Union[str, Any]	    =  "A painting of a squirrel eating a burger"
      UpperCamelCase__      :       Union[str, Any]	    =  sd_pipe([prompt]							,       num_inference_steps=2							,       output_type="np"						).images

      assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,       safety_checker=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       str	    =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
      UpperCamelCase__      :       int	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[str]	    =  (
          "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
          " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
          " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
          " children from bahnhof zoo, detailed "
      )
      UpperCamelCase__      :       Union[str, Any]	    =  40_03_66_03_46
      UpperCamelCase__      :       List[str]	    =  7

      # without safety guidance (sld_guidance_scale = 0)
      UpperCamelCase__      :       Tuple	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=0							,       )

      UpperCamelCase__      :       Union[str, Any]	    =  output.images
      UpperCamelCase__      :       Dict	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Union[str, Any]	    =  [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

      # without safety guidance (strong configuration)
      UpperCamelCase__      :       int	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=20_00							,       sld_warmup_steps=7							,       sld_threshold=0.0_2_5							,       sld_momentum_scale=0.5							,       sld_mom_beta=0.7							,       )

      UpperCamelCase__      :       Optional[Any]	    =  output.images
      UpperCamelCase__      :       Dict	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Any	    =  [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2







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


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,       safety_checker=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
      UpperCamelCase__      :       str	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[str]	    =  "padme amidala taking a bath artwork, safe for work, no nudity"
      UpperCamelCase__      :       int	    =  27_34_97_17_55
      UpperCamelCase__      :       Tuple	    =  7

      UpperCamelCase__      :       Union[str, Any]	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=0							,       )

      UpperCamelCase__      :       List[Any]	    =  output.images
      UpperCamelCase__      :       str	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       List[str]	    =  [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

      UpperCamelCase__      :       str	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[int]	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=20_00							,       sld_warmup_steps=7							,       sld_threshold=0.0_2_5							,       sld_momentum_scale=0.5							,       sld_mom_beta=0.7							,       )

      UpperCamelCase__      :       Optional[int]	    =  output.images
      UpperCamelCase__      :       Dict	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Optional[int]	    =  [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2







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


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
      UpperCamelCase__      :       Tuple	    =  sd_pipe.to(SCREAMING_SNAKE_CASE						)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  (
          "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
          " leyendecker"
      )
      UpperCamelCase__      :       Dict	    =  10_44_35_52_34
      UpperCamelCase__      :       str	    =  12

      UpperCamelCase__      :       int	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=0							,       )

      UpperCamelCase__      :       Tuple	    =  output.images
      UpperCamelCase__      :       int	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Any	    =  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]						)

      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-7

      UpperCamelCase__      :       Optional[Any]	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[int]	    =  sd_pipe(
          [prompt]							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=SCREAMING_SNAKE_CASE							,       num_inference_steps=50							,       output_type="np"							,       width=5_12							,       height=5_12							,       sld_guidance_scale=20_00							,       sld_warmup_steps=7							,       sld_threshold=0.0_2_5							,       sld_momentum_scale=0.5							,       sld_mom_beta=0.7							,       )

      UpperCamelCase__      :       Dict	    =  output.images
      UpperCamelCase__      :       List[Any]	    =  image[0, -3:, -3:, -1]
      UpperCamelCase__      :       Any	    =  np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]						)
      assert image.shape == (1, 5_12, 5_12, 3)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Optional[int]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''decision_transformer'''
 _lowerCAmelCase	:	Optional[Any]				=			['''past_key_values''']
 _lowerCAmelCase	:	Optional[int]				=			{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }







 def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]=17							,       SCREAMING_SNAKE_CASE :       Optional[int]=4							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       SCREAMING_SNAKE_CASE :       Dict="relu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=True							,       SCREAMING_SNAKE_CASE :       int=5_02_56							,       SCREAMING_SNAKE_CASE :       List[Any]=5_02_56							,       SCREAMING_SNAKE_CASE :       Any=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  state_dim
      UpperCamelCase__      :       Optional[int]	    =  act_dim
      UpperCamelCase__      :       Dict	    =  hidden_size
      UpperCamelCase__      :       Any	    =  max_ep_len
      UpperCamelCase__      :       Optional[Any]	    =  action_tanh
      UpperCamelCase__      :       str	    =  vocab_size
      UpperCamelCase__      :       List[str]	    =  n_positions
      UpperCamelCase__      :       Tuple	    =  n_layer
      UpperCamelCase__      :       Union[str, Any]	    =  n_head
      UpperCamelCase__      :       Dict	    =  n_inner
      UpperCamelCase__      :       int	    =  activation_function
      UpperCamelCase__      :       List[str]	    =  resid_pdrop
      UpperCamelCase__      :       Optional[int]	    =  embd_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  attn_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  layer_norm_epsilon
      UpperCamelCase__      :       int	    =  initializer_range
      UpperCamelCase__      :       Dict	    =  scale_attn_weights
      UpperCamelCase__      :       Tuple	    =  use_cache
      UpperCamelCase__      :       List[str]	    =  scale_attn_by_inverse_layer_idx
      UpperCamelCase__      :       Union[str, Any]	    =  reorder_and_upcast_attn

      UpperCamelCase__      :       Optional[Any]	    =  bos_token_id
      UpperCamelCase__      :       Optional[int]	    =  eos_token_id

      super().__init__(bos_token_id=SCREAMING_SNAKE_CASE							,       eos_token_id=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)