





"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a__       :  Optional[int]       		=					logging.get_logger(__name__)  # pylint: disable=invalid-name

a__       :  List[str]       		=					"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase=8						)     ->	Optional[int]:

 '''simple docstring'''






 snake_case__     				=					height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 snake_case__     				=					width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase=5_1_2						,  _UpperCAmelCase=5_1_2						)     ->	int:

 '''simple docstring'''






 snake_case__     				=					pil_image.resize((w, h)						,  resample=Image.BICUBIC						,  reducing_gap=1						)
 snake_case__     				=					np.array(pil_image.convert("RGB"						)						)
 snake_case__     				=					arr.astype(np.floataa						) / 127.5 - 1
 snake_case__     				=					np.transpose(_UpperCAmelCase						,  [2, 0, 1]						)
 snake_case__     				=					torch.from_numpy(_UpperCAmelCase						).unsqueeze(0						)
 return image



class   lowercase_					(  __lowerCAmelCase				):

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(
            unet=__SCREAMING_SNAKE_CASE ,       scheduler=__SCREAMING_SNAKE_CASE ,       movq=__SCREAMING_SNAKE_CASE ,       )
        snake_case__     				=					2 ** (len(self.movq.config.block_out_channels  ) - 1)

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					min(int(num_inference_steps * strength  ) ,       __SCREAMING_SNAKE_CASE  )

        snake_case__     				=					max(num_inference_steps - init_timestep ,       0  )
        snake_case__     				=					self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        if not isinstance(__SCREAMING_SNAKE_CASE ,       (torch.Tensor, PIL.Image.Image, list)  ):
         raise ValueError(
             F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE  )}"  )

        snake_case__     				=					image.to(device=__SCREAMING_SNAKE_CASE ,       dtype=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					batch_size * num_images_per_prompt

        if image.shape[1] == 4:
         snake_case__     				=					image

        else:
         if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) and len(__SCREAMING_SNAKE_CASE  ) != batch_size:
          raise ValueError(
              F"You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE  )}, but requested an effective batch"
              F" size of {batch_size}. Make sure the batch size matches the length of the generators."  )

         elif isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
          snake_case__     				=					[
              self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(__SCREAMING_SNAKE_CASE  )
          ]
          snake_case__     				=					torch.cat(__SCREAMING_SNAKE_CASE ,       dim=0  )
         else:
          snake_case__     				=					self.movq.encode(__SCREAMING_SNAKE_CASE  ).latent_dist.sample(__SCREAMING_SNAKE_CASE  )

         snake_case__     				=					self.movq.config.scaling_factor * init_latents

        snake_case__     				=					torch.cat([init_latents] ,       dim=0  )

        snake_case__     				=					init_latents.shape
        snake_case__     				=					randn_tensor(__SCREAMING_SNAKE_CASE ,       generator=__SCREAMING_SNAKE_CASE ,       device=__SCREAMING_SNAKE_CASE ,       dtype=__SCREAMING_SNAKE_CASE  )

        # get latents
        snake_case__     				=					self.scheduler.add_noise(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

        snake_case__     				=					init_latents

        return latents

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE=0  ):






        '''simple docstring'''

        if is_accelerate_available():
         from accelerate import cpu_offload
        else:
         raise ImportError("Please install accelerate via `pip install accelerate`"  )

        snake_case__     				=					torch.device(F"cuda:{gpu_id}"  )

        snake_case__     				=					[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
          cpu_offload(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE=0  ):






        '''simple docstring'''

        if is_accelerate_available() and is_accelerate_version(">=" ,       "0.17.0.dev0"  ):
         from accelerate import cpu_offload_with_hook
        else:
         raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."  )

        snake_case__     				=					torch.device(F"cuda:{gpu_id}"  )

        if self.device.type != "cpu":
         self.to("cpu" ,       silence_dtype_warnings=__SCREAMING_SNAKE_CASE  )
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        snake_case__     				=					None
        for cpu_offloaded_model in [self.unet, self.movq]:
         snake_case__				, snake_case__     				=					cpu_offload_with_hook(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       prev_module_hook=__SCREAMING_SNAKE_CASE  )

        # We'll offload the last model manually.
        snake_case__     				=					hook

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        if not hasattr(self.unet ,       "_hf_hook"  ):
         return self.device
        for module in self.unet.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE ,       "_hf_hook"  )
             and hasattr(module._hf_hook ,       "execution_device"  )
             and module._hf_hook.execution_device is not None
         ):
          return torch.device(module._hf_hook.execution_device  )
        return self.device

       @torch.no_grad()
       @replace_example_docstring(__SCREAMING_SNAKE_CASE  )
       def __call__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = 512 ,       __SCREAMING_SNAKE_CASE = 512 ,       __SCREAMING_SNAKE_CASE = 100 ,       __SCREAMING_SNAKE_CASE = 4.0 ,       __SCREAMING_SNAKE_CASE = 0.3 ,       __SCREAMING_SNAKE_CASE = 1 ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "pil" ,       __SCREAMING_SNAKE_CASE = True ,       ):






        '''simple docstring'''

        snake_case__     				=					self._execution_device

        snake_case__     				=					guidance_scale > 1.0

        if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					torch.cat(__SCREAMING_SNAKE_CASE ,       dim=0  )
        if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					torch.cat(__SCREAMING_SNAKE_CASE ,       dim=0  )
        if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					torch.cat(__SCREAMING_SNAKE_CASE ,       dim=0  )

        snake_case__     				=					image_embeds.shape[0]

        if do_classifier_free_guidance:
         snake_case__     				=					image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE ,       dim=0  )
         snake_case__     				=					negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE ,       dim=0  )
         snake_case__     				=					hint.repeat_interleave(__SCREAMING_SNAKE_CASE ,       dim=0  )

        snake_case__     				=					torch.cat([negative_image_embeds, image_embeds] ,       dim=0  ).to(dtype=self.unet.dtype ,       device=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					torch.cat([hint, hint] ,       dim=0  ).to(dtype=self.unet.dtype ,       device=__SCREAMING_SNAKE_CASE  )

        if not isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					[image]
        if not all(isinstance(__SCREAMING_SNAKE_CASE ,       (PIL.Image.Image, torch.Tensor)  ) for i in image  ):
         raise ValueError(
             F"Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"  )

        snake_case__     				=					torch.cat([prepare_image(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for i in image] ,       dim=0  )
        snake_case__     				=					image.to(dtype=image_embeds.dtype ,       device=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					self.movq.encode(__SCREAMING_SNAKE_CASE  )["latents"]
        snake_case__     				=					latents.repeat_interleave(__SCREAMING_SNAKE_CASE ,       dim=0  )
        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE ,       device=__SCREAMING_SNAKE_CASE  )
        snake_case__				, snake_case__     				=					self.get_timesteps(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )
        snake_case__     				=					timesteps[:1].repeat(batch_size * num_images_per_prompt  )
        snake_case__				, snake_case__     				=					downscale_height_and_width(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       self.movq_scale_factor  )
        snake_case__     				=					self.prepare_latents(
            __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       image_embeds.dtype ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )
        for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE  )  ):
         # expand the latents if we are doing classifier free guidance
         snake_case__     				=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

         snake_case__     				=					{"image_embeds": image_embeds, "hint": hint}
         snake_case__     				=					self.unet(
             sample=__SCREAMING_SNAKE_CASE ,       timestep=__SCREAMING_SNAKE_CASE ,       encoder_hidden_states=__SCREAMING_SNAKE_CASE ,       added_cond_kwargs=__SCREAMING_SNAKE_CASE ,       return_dict=__SCREAMING_SNAKE_CASE ,       )[0]

         if do_classifier_free_guidance:
          snake_case__				, snake_case__     				=					noise_pred.split(latents.shape[1] ,       dim=1  )
          snake_case__				, snake_case__     				=					noise_pred.chunk(2  )
          snake_case__				, snake_case__     				=					variance_pred.chunk(2  )
          snake_case__     				=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
          snake_case__     				=					torch.cat([noise_pred, variance_pred_text] ,       dim=1  )

         if not (
             hasattr(self.scheduler.config ,       "variance_type"  )
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
          snake_case__				, snake_case__     				=					noise_pred.split(latents.shape[1] ,       dim=1  )

         # compute the previous noisy sample x_t -> x_t-1

         snake_case__     				=					self.scheduler.step(
             __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       generator=__SCREAMING_SNAKE_CASE ,       )[0]

        # post-processing
        snake_case__     				=					self.movq.decode(__SCREAMING_SNAKE_CASE ,       force_not_quantize=__SCREAMING_SNAKE_CASE  )["sample"]

        if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"  )

        if output_type in ["np", "pil"]:
         snake_case__     				=					image * 0.5 + 0.5
         snake_case__     				=					image.clamp(0 ,       1  )
         snake_case__     				=					image.cpu().permute(0 ,       2 ,       3 ,       1  ).float().numpy()

        if output_type == "pil":
         snake_case__     				=					self.numpy_to_pil(__SCREAMING_SNAKE_CASE  )

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )








"""simple docstring"""


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
       SpeechaTextaDecoder,
       SpeechaTextaForCausalLM,
   )



@require_torch
class   lowercase_					:

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=99 ,       __SCREAMING_SNAKE_CASE=13 ,       __SCREAMING_SNAKE_CASE=16 ,       __SCREAMING_SNAKE_CASE=7 ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=2 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=4 ,       __SCREAMING_SNAKE_CASE=4 ,       __SCREAMING_SNAKE_CASE=30 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=2 ,       __SCREAMING_SNAKE_CASE=None ,       ):






        '''simple docstring'''

        snake_case__     				=					parent
        snake_case__     				=					batch_size
        snake_case__     				=					decoder_seq_length
        # For common tests
        snake_case__     				=					self.decoder_seq_length
        snake_case__     				=					is_training
        snake_case__     				=					use_attention_mask
        snake_case__     				=					use_labels

        snake_case__     				=					vocab_size
        snake_case__     				=					d_model
        snake_case__     				=					d_model
        snake_case__     				=					decoder_layers
        snake_case__     				=					decoder_layers
        snake_case__     				=					decoder_ffn_dim
        snake_case__     				=					decoder_attention_heads
        snake_case__     				=					decoder_attention_heads
        snake_case__     				=					eos_token_id
        snake_case__     				=					bos_token_id
        snake_case__     				=					pad_token_id
        snake_case__     				=					decoder_start_token_id
        snake_case__     				=					use_cache
        snake_case__     				=					max_position_embeddings

        snake_case__     				=					None
        snake_case__     				=					decoder_seq_length
        snake_case__     				=					2
        snake_case__     				=					1

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					ids_tensor([self.batch_size, self.decoder_seq_length] ,       self.vocab_size  )

        snake_case__     				=					None
        if self.use_attention_mask:
         snake_case__     				=					ids_tensor([self.batch_size, self.decoder_seq_length] ,       vocab_size=2  )

        snake_case__     				=					None
        if self.use_labels:
         snake_case__     				=					ids_tensor([self.batch_size, self.decoder_seq_length] ,       self.vocab_size  )

        snake_case__     				=					SpeechaTextaConfig(
            vocab_size=self.vocab_size ,       d_model=self.d_model ,       decoder_layers=self.decoder_layers ,       decoder_ffn_dim=self.decoder_ffn_dim ,       decoder_attention_heads=self.decoder_attention_heads ,       eos_token_id=self.eos_token_id ,       bos_token_id=self.bos_token_id ,       use_cache=self.use_cache ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.decoder_start_token_id ,       max_position_embeddings=self.max_position_embeddings ,       )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					True
        snake_case__     				=					SpeechaTextaDecoder(config=__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  ).eval()
        snake_case__     				=					input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        snake_case__     				=					model(__SCREAMING_SNAKE_CASE ,       use_cache=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					model(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					model(__SCREAMING_SNAKE_CASE ,       use_cache=__SCREAMING_SNAKE_CASE  )

        self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE  ) == len(__SCREAMING_SNAKE_CASE  )  )
        self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE  ) == len(__SCREAMING_SNAKE_CASE  ) + 1  )

        snake_case__     				=					outputs["past_key_values"]

        # create hypothetical next token and extent to next_input_ids
        snake_case__     				=					ids_tensor((2, 1) ,       config.vocab_size - 1  ) + 1

        # append to next input_ids and
        snake_case__     				=					torch.cat([input_ids, next_tokens] ,       dim=-1  )

        print(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					model(__SCREAMING_SNAKE_CASE  )["last_hidden_state"]
        snake_case__     				=					model(__SCREAMING_SNAKE_CASE ,       past_key_values=__SCREAMING_SNAKE_CASE  )["last_hidden_state"]

        # select random slice
        snake_case__     				=					ids_tensor((1,) ,       output_from_past.shape[-1]  ).item()
        snake_case__     				=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        snake_case__     				=					output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       atol=1e-3  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.prepare_config_and_inputs()
        (
            (
            snake_case__
        )				, (
            snake_case__
        )				, (
            snake_case__
        )				, (
            snake_case__
        )				, 
        )     				=					config_and_inputs

        snake_case__     				=					{
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict



@require_torch
class   lowercase_					(  __lowerCAmelCase					,__lowerCAmelCase					,__lowerCAmelCase					,unittest.TestCase				):
       snake_case__					:				Tuple									=  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       snake_case__					:				Optional[Any]									=  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       snake_case__					:				List[Any]									=  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
       snake_case__					:				Dict									=  True
       snake_case__					:				Dict									=  False

       def 		_SCREAMING_SNAKE_CASE(      self ,       ):






        '''simple docstring'''

        snake_case__     				=					SpeechaTextaStandaloneDecoderModelTester(self ,       is_training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					ConfigTester(self ,       config_class=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return

