





"""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,
)


__magic_name__       :					List[str]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__       :					List[Any]						=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=8     ):
						UpperCamelCase				:		Union[str, Any]				   =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase				:		Optional[int]				   =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=512							,     SCREAMING_SNAKE_CASE=512     ):
						UpperCamelCase				:		Optional[Any]				   =			pil_image.resize((w, h)							,     resample=Image.BICUBIC							,     reducing_gap=1     )
						UpperCamelCase				:		Any				   =			np.array(pil_image.convert("""RGB"""     )     )
						UpperCamelCase				:		List[Any]				   =			arr.astype(np.floataa     ) / 1_27.5 - 1
						UpperCamelCase				:		Union[str, Any]				   =			np.transpose(SCREAMING_SNAKE_CASE							,     [2, 0, 1]     )
						UpperCamelCase				:		List[Any]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )
						return image






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=_A						,   scheduler=_A						,   movq=_A						,   )
									UpperCamelCase				:		List[str]				   =			2 ** (len(self.movq.config.block_out_channels    ) - 1)







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			min(int(num_inference_steps * strength    )						,   _A    )

									UpperCamelCase				:		List[str]				   =			max(num_inference_steps - init_timestep						,   0    )
									UpperCamelCase				:		Optional[Any]				   =			self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A=None    ):






									'''simple docstring'''







									if not isinstance(_A						,   (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(_A    )}"""    )

									UpperCamelCase				:		Optional[int]				   =			image.to(device=_A						,   dtype=_A    )

									UpperCamelCase				:		int				   =			batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															UpperCamelCase				:		List[str]				   =			image

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

															elif isinstance(_A						,   _A    ):
																					UpperCamelCase				:		str				   =			[
																					    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_A    )
																					]
																					UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A						,   dim=0    )
															else:
																					UpperCamelCase				:		List[str]				   =			self.movq.encode(_A    ).latent_dist.sample(_A    )

															UpperCamelCase				:		Dict				   =			self.movq.config.scaling_factor * init_latents

									UpperCamelCase				:		Tuple				   =			torch.cat([init_latents]						,   dim=0    )

									UpperCamelCase				:		List[str]				   =			init_latents.shape
									UpperCamelCase				:		int				   =			randn_tensor(_A						,   generator=_A						,   device=_A						,   dtype=_A    )

									# get latents
									UpperCamelCase				:		int				   =			self.scheduler.add_noise(_A						,   _A						,   _A    )

									UpperCamelCase				:		Optional[Any]				   =			init_latents

									return latents







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







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

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

									UpperCamelCase				:		Optional[int]				   =			[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_A						,   _A    )







			def   _a      (      self						,   _A=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."""    )

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

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

									UpperCamelCase				:		Dict				   =			None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			cpu_offload_with_hook(_A						,   _A						,   prev_module_hook=_A    )

									# We'll offload the last model manually.
									UpperCamelCase				:		Optional[Any]				   =			hook







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






									'''simple docstring'''







									if not hasattr(self.unet						,   """_hf_hook"""    ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(_A						,   """_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(_A    )
			def __call__(      self						,   _A						,   _A						,   _A						,   _A = 5_1_2						,   _A = 5_1_2						,   _A = 1_0_0						,   _A = 4.0						,   _A = 0.3						,   _A = 1						,   _A = None						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self._execution_device

									UpperCamelCase				:		List[Any]				   =			guidance_scale > 1.0

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Dict				   =			torch.cat(_A						,   dim=0    )
									UpperCamelCase				:		Tuple				   =			image_embeds.shape[0]
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		int				   =			torch.cat(_A						,   dim=0    )

									if do_classifier_free_guidance:
															UpperCamelCase				:		str				   =			image_embeds.repeat_interleave(_A						,   dim=0    )
															UpperCamelCase				:		Optional[Any]				   =			negative_image_embeds.repeat_interleave(_A						,   dim=0    )

									UpperCamelCase				:		Dict				   =			torch.cat([negative_image_embeds, image_embeds]						,   dim=0    ).to(dtype=self.unet.dtype						,   device=_A    )

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

									UpperCamelCase				:		Any				   =			torch.cat([prepare_image(_A						,   _A						,   _A    ) for i in image]						,   dim=0    )
									UpperCamelCase				:		Union[str, Any]				   =			image.to(dtype=image_embeds.dtype						,   device=_A    )

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

															UpperCamelCase				:		Dict				   =			{"""image_embeds""": image_embeds}
															UpperCamelCase				:		int				   =			self.unet(
															    sample=_A						,   timestep=_A						,   encoder_hidden_states=_A						,   added_cond_kwargs=_A						,   return_dict=_A						,   )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			noise_pred.split(latents.shape[1]						,   dim=1    )
																					UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			noise_pred.chunk(2    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[Any]				   =			variance_pred.chunk(2    )
																					UpperCamelCase				:		Tuple				   =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					UpperCamelCase				:		Union[str, Any]				   =			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"]
															):
																					UpperCamelCase   ,						UpperCamelCase				:		int				   =			noise_pred.split(latents.shape[1]						,   dim=1    )

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase				:		List[Any]				   =			self.scheduler.step(
															    _A						,   _A						,   _A						,   generator=_A						,   )[0]

									# post-processing
									UpperCamelCase				:		Union[str, Any]				   =			self.movq.decode(_A						,   force_not_quantize=_A    )["""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"]:
															UpperCamelCase				:		Optional[int]				   =			image * 0.5 + 0.5
															UpperCamelCase				:		Optional[Any]				   =			image.clamp(0						,   1    )
															UpperCamelCase				:		str				   =			image.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Optional[Any]				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )









from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)







@require_tf
class 				lowerCAmelCase_       :
				__a     :       List[Any]						=     XGLMConfig
				__a     :       List[Any]						=     {}
				__a     :       List[str]						=     "gelu"






				def __init__(			self      ,snake_case__      ,snake_case__=14      ,snake_case__=7      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=4      ,snake_case__=37      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=0.02      ,):
							SCREAMING_SNAKE_CASE_   : int					  =      parent
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      seq_length
							SCREAMING_SNAKE_CASE_   : List[str]					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_input_mask
							SCREAMING_SNAKE_CASE_   : Tuple					  =      use_labels
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : int					  =      d_model
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ffn_dim
							SCREAMING_SNAKE_CASE_   : Any					  =      activation_function
							SCREAMING_SNAKE_CASE_   : Dict					  =      activation_dropout
							SCREAMING_SNAKE_CASE_   : int					  =      attention_dropout
							SCREAMING_SNAKE_CASE_   : str					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Any					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : int					  =      None
							SCREAMING_SNAKE_CASE_   : str					  =      0
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      2
							SCREAMING_SNAKE_CASE_   : Any					  =      1






				def 		snake_case	(			self ):
							return XGLMConfig.from_pretrained('facebook/xglm-564M' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )      ,clip_value_min=0      ,clip_value_max=3 )

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_config()

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,2 )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)






				def 		snake_case	(			self ):
							return XGLMConfig(
							    vocab_size=self.vocab_size      ,d_model=self.hidden_size      ,num_layers=self.num_hidden_layers      ,attention_heads=self.num_attention_heads      ,ffn_dim=self.ffn_dim      ,activation_function=self.activation_function      ,activation_dropout=self.activation_dropout      ,attention_dropout=self.attention_dropout      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,use_cache=snake_case__      ,bos_token_id=self.bos_token_id      ,eos_token_id=self.eos_token_id      ,pad_token_id=self.pad_token_id      ,return_dict=snake_case__      ,)






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.prepare_config_and_inputs()

							(
							    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    
							)   : List[Any]					  =      config_and_inputs

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Dict						=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				__a     :       Dict						=     (TFXGLMForCausalLM,) if is_tf_available() else ()
				__a     :       Union[str, Any]						=     (
				    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				__a     :       Dict						=     False
				__a     :       Union[str, Any]						=     False
				__a     :       List[str]						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      TFXGLMModelTester(self )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConfigTester(self      ,config_class=snake_case__      ,n_embd=37 )






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






				@slow
				def 		snake_case	(			self ):
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMModel.from_pretrained(snake_case__ )
										self.assertIsNotNone(snake_case__ )






				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
				def 		snake_case	(			self ):
							super().test_resize_token_embeddings()







@require_tf
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Any					  =      tf.convert_to_tensor([[2, 268, 9865]]      ,dtype=tf.intaa )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							SCREAMING_SNAKE_CASE_   : Tuple					  =      [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
							# fmt: on
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,num_beams=1 )
							if verify_outputs:
										self.assertListEqual(output_ids[0].numpy().tolist()      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

							tf.random.set_seed(0 )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer('Today is a nice day and'      ,return_tensors='tf' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(':/CPU:0' ):
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,seed=[7, 0] )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.decode(output_ids[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      (
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(snake_case__      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'left'

							# use different length sentences to test batching
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer(snake_case__      ,return_tensors='tf'      ,padding=snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      inputs['input_ids']

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.generate(input_ids=snake_case__      ,attention_mask=inputs['attention_mask']      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[0]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[1]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.batch_decode(snake_case__      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.decode(output_non_padded[0]      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer.decode(output_padded[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(snake_case__      ,snake_case__ )
							self.assertListEqual(snake_case__      ,[non_padded_sentence, padded_sentence] )



