



"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def  __snake_case							(	__A      :						str     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Dict							=     SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							Tuple							=     4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							Any							=     4
								SCREAMING_SNAKE_CASE						:							Dict							=     48
								SCREAMING_SNAKE_CASE						:							int							=     'pixelshuffle_aux'
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							Dict							=     [6, 6, 6, 6]
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     60
								SCREAMING_SNAKE_CASE						:							List[str]							=     [6, 6, 6, 6]
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'pixelshuffledirect'
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     4
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'nearest+conv'
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							List[str]							=     1
								SCREAMING_SNAKE_CASE						:							Any							=     1
								SCREAMING_SNAKE_CASE						:							Tuple							=     126
								SCREAMING_SNAKE_CASE						:							Any							=     7
								SCREAMING_SNAKE_CASE						:							List[str]							=     2_5_5.0
								SCREAMING_SNAKE_CASE						:							List[Any]							=     ''

				return config
def  __snake_case							(	__A      :						Optional[Any]							,		__A      :						Optional[Any]     )     ->       Any:






				'''simple docstring'''
				if "patch_embed.proj" in name and "layers" not in name:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     name.replace('patch_embed.proj'							,		'embeddings.patch_embeddings.projection'     )
				if "patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE						:							Any							=     name.replace('patch_embed.norm'							,		'embeddings.patch_embeddings.layernorm'     )
				if "layers" in name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     name.replace('layers'							,		'encoder.stages'     )
				if "residual_group.blocks" in name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     name.replace('residual_group.blocks'							,		'layers'     )
				if "attn.proj" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('attn.proj'							,		'attention.output.dense'     )
				if "attn" in name:
								SCREAMING_SNAKE_CASE						:							Dict							=     name.replace('attn'							,		'attention.self'     )
				if "norm1" in name:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     name.replace('norm1'							,		'layernorm_before'     )
				if "norm2" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('norm2'							,		'layernorm_after'     )
				if "mlp.fc1" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('mlp.fc1'							,		'intermediate.dense'     )
				if "mlp.fc2" in name:
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('mlp.fc2'							,		'output.dense'     )
				if "q_bias" in name:
								SCREAMING_SNAKE_CASE						:							Dict							=     name.replace('q_bias'							,		'query.bias'     )
				if "k_bias" in name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     name.replace('k_bias'							,		'key.bias'     )
				if "v_bias" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('v_bias'							,		'value.bias'     )
				if "cpb_mlp" in name:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     name.replace('cpb_mlp'							,		'continuous_position_bias_mlp'     )
				if "patch_embed.proj" in name:
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     name.replace('patch_embed.proj'							,		'patch_embed.projection'     )

				if name == "norm.weight":
								SCREAMING_SNAKE_CASE						:							List[str]							=     'layernorm.weight'
				if name == "norm.bias":
								SCREAMING_SNAKE_CASE						:							Any							=     'layernorm.bias'

				if "conv_first" in name:
								SCREAMING_SNAKE_CASE						:							Dict							=     name.replace('conv_first'							,		'first_convolution'     )

				if (
				    "upsample" in name
				    or "conv_before_upsample" in name
				    or "conv_bicubic" in name
				    or "conv_up" in name
				    or "conv_hr" in name
				    or "conv_last" in name
				    or "aux" in name
				):
								# heads
								if "conv_last" in name:
												SCREAMING_SNAKE_CASE						:							List[str]							=     name.replace('conv_last'							,		'final_convolution'     )
								if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
												if "conv_before_upsample.0" in name:
																SCREAMING_SNAKE_CASE						:							Dict							=     name.replace('conv_before_upsample.0'							,		'conv_before_upsample'     )
												if "upsample.0" in name:
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     name.replace('upsample.0'							,		'upsample.convolution_0'     )
												if "upsample.2" in name:
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     name.replace('upsample.2'							,		'upsample.convolution_1'     )
												SCREAMING_SNAKE_CASE						:							List[Any]							=     'upsample.' + name
								elif config.upsampler == "pixelshuffledirect":
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     name.replace('upsample.0.weight'							,		'upsample.conv.weight'     )
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     name.replace('upsample.0.bias'							,		'upsample.conv.bias'     )
								else:
												pass
				else:
								SCREAMING_SNAKE_CASE						:							Tuple							=     'swin2sr.' + name

				return name
def  __snake_case							(	__A      :						Tuple							,		__A      :						List[Any]     )     ->       List[str]:






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     orig_state_dict.pop(__A     )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE						:							List[str]							=     key.split('.'     )
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     int(key_split[1]     )
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     int(key_split[4]     )
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     config.embed_dim

												if "weight" in key:
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     val[:dim, :]
																SCREAMING_SNAKE_CASE						:							Any							=     val[dim : dim * 2, :]
																SCREAMING_SNAKE_CASE						:							List[Any]							=     val[-dim:, :]
												else:
																SCREAMING_SNAKE_CASE						:							List[str]							=     val[:dim]
																SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[dim : dim * 2]
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     val[-dim:]
												pass
								else:
												SCREAMING_SNAKE_CASE						:							Tuple							=     val

				return orig_state_dict
def  __snake_case							(	__A      :						Tuple							,		__A      :						Optional[Any]							,		__A      :						Optional[int]     )     ->       Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Any							=     get_config(__A     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     SwinaSRForImageSuperResolution(__A     )
				model.eval()

				SCREAMING_SNAKE_CASE						:							List[str]							=     torch.hub.load_state_dict_from_url(__A							,		map_location='cpu'     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     convert_state_dict(__A							,		__A     )
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     model.load_state_dict(__A							,		strict=__A     )

				if len(__A     ) > 0:
								raise ValueError('Missing keys when converting: {}'.format(__A     )     )
				for key in unexpected_keys:
								if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
												raise ValueError(F"""Unexpected key {key} in state_dict"""     )

    # verify values
				SCREAMING_SNAKE_CASE						:							str							=     'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
				SCREAMING_SNAKE_CASE						:							List[Any]							=     Image.open(requests.get(__A							,		stream=__A     ).raw     ).convert('RGB'     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     126 if 'Jpeg' in checkpoint_url else 256
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     Compose(
				    [
				        Resize((image_size, image_size)     ),
				        ToTensor(),
				        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]							,		std=[0.2_2_9, 0.2_2_4, 0.2_2_5]     ),
				    ]     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     transforms(__A     ).unsqueeze(0     )

				if config.num_channels == 1:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pixel_values[:, 0, :, :].unsqueeze(1     )

				SCREAMING_SNAKE_CASE						:							Tuple							=     model(__A     )

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.Size([1, 3, 512, 512]     )
								SCREAMING_SNAKE_CASE						:							str							=     torch.tensor(
								    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]     )
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							str							=     torch.Size([1, 3, 1024, 1024]     )
								SCREAMING_SNAKE_CASE						:							Tuple							=     torch.tensor(
								    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]     )
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								SCREAMING_SNAKE_CASE						:							Any							=     torch.Size([1, 3, 1024, 1024]     )
								SCREAMING_SNAKE_CASE						:							Dict							=     torch.tensor(
								    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]     )
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							int							=     torch.Size([1, 3, 512, 512]     )
								SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor(
								    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]     )
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							Any							=     torch.Size([1, 3, 1024, 1024]     )
								SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.tensor(
								    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]     )

				assert (
				    outputs.reconstruction.shape == expected_shape
				), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
				assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]							,		__A							,		atol=1E-3     )
				print('Looks ok!'     )

				SCREAMING_SNAKE_CASE						:							List[str]							=     {
				    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
				        'swin2SR-classical-sr-x2-64'
				    ),
				    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
				        'swin2SR-classical-sr-x4-64'
				    ),
				    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
				        'swin2SR-compressed-sr-x4-48'
				    ),
				    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
				        'swin2SR-lightweight-x2-64'
				    ),
				    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
				        'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
				    ),
				}
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     url_to_name[checkpoint_url]

				if pytorch_dump_folder_path is not None:
								print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
								model.save_pretrained(__A     )
								print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
								processor.save_pretrained(__A     )

				if push_to_hub:
								model.push_to_hub(F"""caidas/{model_name}"""     )
								processor.push_to_hub(F"""caidas/{model_name}"""     )


if __name__ == "__main__":
							A_						:    Tuple				=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
							    type=str,
							    help='URL of the original Swin2SR checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

							A_						:    Tuple				=      parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""

from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





	@register_to_config
	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 50_257     ,       _SCREAMING_SNAKE_CASE				:       int = 1_024     ,       _SCREAMING_SNAKE_CASE				:       int = 768     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       str = "gelu_new"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 1E-5     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
									    f""" `n_embd`: {n_embd} are not equal."""    )

					SCREAMING_SNAKE_CASE						:							int							=     prefix_inner_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     prefix_hidden_dim

					SCREAMING_SNAKE_CASE						:							int							=     (
					    nn.Linear(self.prefix_inner_dim     ,       self.prefix_hidden_dim    )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    nn.Linear(self.prefix_hidden_dim     ,       _SCREAMING_SNAKE_CASE    ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     GPTaConfig(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       n_positions=_SCREAMING_SNAKE_CASE     ,       n_embd=_SCREAMING_SNAKE_CASE     ,       n_layer=_SCREAMING_SNAKE_CASE     ,       n_head=_SCREAMING_SNAKE_CASE     ,       n_inner=_SCREAMING_SNAKE_CASE     ,       activation_function=_SCREAMING_SNAKE_CASE     ,       resid_pdrop=_SCREAMING_SNAKE_CASE     ,       embd_pdrop=_SCREAMING_SNAKE_CASE     ,       attn_pdrop=_SCREAMING_SNAKE_CASE     ,       layer_norm_epsilon=_SCREAMING_SNAKE_CASE     ,       initializer_range=_SCREAMING_SNAKE_CASE     ,       scale_attn_weights=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE     ,       scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE     ,       reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     GPTaLMHeadModel(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.encode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.decode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.cat((prefix_embeds, embedding_text)     ,       dim=1    )

					if labels is not None:
									SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_token(input_ids.shape[0]     ,       input_ids.device    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.cat((dummy_token, input_ids)     ,       dim=1    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       torch.device    )						->	torch.Tensor:





					"""simple docstring"""


					return torch.zeros(_SCREAMING_SNAKE_CASE     ,       self.prefix_length     ,       dtype=torch.intaa     ,       device=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.encode_prefix(_SCREAMING_SNAKE_CASE    )





	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.split(_SCREAMING_SNAKE_CASE     ,       1     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     []
					SCREAMING_SNAKE_CASE						:							str							=     []
					for feature in features:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE    )    )  # back to the clip feature
									# Only support beam search for now
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     self.generate_beam(
									    input_embeds=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE    )
									generated_tokens.append(output_tokens[0]    )
									generated_seq_lengths.append(seq_lengths[0]    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       int = 5     ,       _SCREAMING_SNAKE_CASE				:       int = 67     ,       _SCREAMING_SNAKE_CASE				:       float = 1.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Tuple							=     None
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.ones(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.int    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.bool    )

					if input_embeds is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_embeds
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     outputs.logits
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     logits.softmax(-1    ).log()

									if scores is None:
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     logits.topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     generated.expand(_SCREAMING_SNAKE_CASE     ,       *generated.shape[1:]    )
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens.permute(1     ,       0    ), scores.squeeze(0    )
													if tokens is None:
																	SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens
													else:
																	SCREAMING_SNAKE_CASE						:							List[Any]							=     tokens.expand(_SCREAMING_SNAKE_CASE     ,       *tokens.shape[1:]    )
																	SCREAMING_SNAKE_CASE						:							List[str]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
									else:
													SCREAMING_SNAKE_CASE						:							List[str]							=     -float(np.inf    )
													SCREAMING_SNAKE_CASE						:							int							=     0
													SCREAMING_SNAKE_CASE						:							Tuple							=     scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum / seq_lengths[:, None]
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     scores_sum_average.view(-1    ).topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens // scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							Dict							=     seq_lengths[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens % scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							int							=     next_tokens.unsqueeze(1    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tokens[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
													SCREAMING_SNAKE_CASE						:							Any							=     generated[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum_average * seq_lengths
													SCREAMING_SNAKE_CASE						:							Tuple							=     is_stopped[next_tokens_source]

									SCREAMING_SNAKE_CASE						:							str							=     self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]     ,       1     ,       -1    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     torch.cat((generated, next_token_embed)     ,       dim=1    )
									SCREAMING_SNAKE_CASE						:							str							=     is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE    ).squeeze()
									if is_stopped.all():
													break

					SCREAMING_SNAKE_CASE						:							Tuple							=     scores / seq_lengths
					SCREAMING_SNAKE_CASE						:							Tuple							=     scores.argsort(descending=_SCREAMING_SNAKE_CASE    )
					# tokens tensors are already padded to max_seq_length
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [tokens[i] for i in order]
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([seq_lengths[i] for i in order]     ,       dtype=seq_lengths.dtype    )
					return output_texts, seq_lengths





