






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= 42






class  UpperCamelCase_      (			snake_case_       ,					snake_case_				):



	'''simple docstring'''




	@register_to_config
	def __init__(       self       ,			a = 6_55_36       ,			a = None       ,			a = 2       ,			a = 2       ,			a = 0       ,			a = "fourier"       ,			a = True       ,			a = False       ,			a = 0.0       ,			a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,			a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,			a = "UNetMidBlock1D"       ,			a = None       ,			a = (32, 32, 64)       ,			a = None       ,			a = 8       ,			a = 1       ,			a = False       ,			)       ->    Union[str, Any]:
								super().__init__()
								snake_case_				=							sample_size

								# time
								if time_embedding_type == "fourier":
															snake_case_				=							GaussianFourierProjection(
															    embedding_size=8       ,			set_W_to_weight=a       ,			log=a       ,			flip_sin_to_cos=a					)
															snake_case_				=							2 * block_out_channels[0]
								elif time_embedding_type == "positional":
															snake_case_				=							Timesteps(
															    block_out_channels[0]       ,			flip_sin_to_cos=a       ,			downscale_freq_shift=a					)
															snake_case_				=							block_out_channels[0]

								if use_timestep_embedding:
															snake_case_				=							block_out_channels[0] * 4
															snake_case_				=							TimestepEmbedding(
															    in_channels=a       ,			time_embed_dim=a       ,			act_fn=a       ,			out_dim=block_out_channels[0]       ,			)

								snake_case_				=							nn.ModuleList([]					)
								snake_case_				=							None
								snake_case_				=							nn.ModuleList([]					)
								snake_case_				=							None

								# down
								snake_case_				=							in_channels
								for i, down_block_type in enumerate(a					):
															snake_case_				=							output_channel
															snake_case_				=							block_out_channels[i]

															if i == 0:
																						input_channel += extra_in_channels

															snake_case_				=							i == len(a					) - 1

															snake_case_				=							get_down_block(
															    a       ,			num_layers=a       ,			in_channels=a       ,			out_channels=a       ,			temb_channels=block_out_channels[0]       ,			add_downsample=not is_final_block or downsample_each_block       ,			)
															self.down_blocks.append(a					)

								# mid
								snake_case_				=							get_mid_block(
								    a       ,			in_channels=block_out_channels[-1]       ,			mid_channels=block_out_channels[-1]       ,			out_channels=block_out_channels[-1]       ,			embed_dim=block_out_channels[0]       ,			num_layers=a       ,			add_downsample=a       ,			)

								# up
								snake_case_				=							list(reversed(a					)					)
								snake_case_				=							reversed_block_out_channels[0]
								if out_block_type is None:
															snake_case_				=							out_channels
								else:
															snake_case_				=							block_out_channels[0]

								for i, up_block_type in enumerate(a					):
															snake_case_				=							output_channel
															snake_case_				=							(
															    reversed_block_out_channels[i + 1] if i < len(a					) - 1 else final_upsample_channels
															)

															snake_case_				=							i == len(a					) - 1

															snake_case_				=							get_up_block(
															    a       ,			num_layers=a       ,			in_channels=a       ,			out_channels=a       ,			temb_channels=block_out_channels[0]       ,			add_upsample=not is_final_block       ,			)
															self.up_blocks.append(a					)
															snake_case_				=							output_channel

								# out
								snake_case_				=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,			32					)
								snake_case_				=							get_out_block(
								    out_block_type=a       ,			num_groups_out=a       ,			embed_dim=block_out_channels[0]       ,			out_channels=a       ,			act_fn=a       ,			fc_dim=block_out_channels[-1] // 4       ,			)






	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a = True       ,			)       ->    Union[UNetaDOutput, Tuple]:
								snake_case_				=							timestep
								if not torch.is_tensor(a					):
															snake_case_				=							torch.tensor([timesteps]       ,			dtype=torch.long       ,			device=sample.device					)
								elif torch.is_tensor(a					) and len(timesteps.shape					) == 0:
															snake_case_				=							timesteps[None].to(sample.device					)

								snake_case_				=							self.time_proj(a					)
								if self.config.use_timestep_embedding:
															snake_case_				=							self.time_mlp(a					)
								else:
															snake_case_				=							timestep_embed[..., None]
															snake_case_				=							timestep_embed.repeat([1, 1, sample.shape[2]]					).to(sample.dtype					)
															snake_case_				=							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])					)

								# 2. down
								snake_case_				=							()
								for downsample_block in self.down_blocks:
															snake_case_			,		snake_case_				=							downsample_block(hidden_states=a       ,			temb=a					)
															down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
															snake_case_				=							self.mid_block(a       ,			a					)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks					):
															snake_case_				=							down_block_res_samples[-1:]
															snake_case_				=							down_block_res_samples[:-1]
															snake_case_				=							upsample_block(a       ,			res_hidden_states_tuple=a       ,			temb=a					)

								# 5. post-process
								if self.out_block:
															snake_case_				=							self.out_block(a       ,			a					)

								if not return_dict:
															return (sample,)

								return UNetaDOutput(sample=a					)













import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase  							=					{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_=None):
							# Initialise PyTorch model
							snake_case_				=							XLNetConfig.from_json_file(a_)

							snake_case_				=							finetuning_task.lower() if finetuning_task is not None else ''
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''')
														snake_case_				=							finetuning_task
														snake_case_				=							GLUE_TASKS_NUM_LABELS[finetuning_task]
														snake_case_				=							XLNetForSequenceClassification(a_)
							elif "squad" in finetuning_task:
														snake_case_				=							finetuning_task
														snake_case_				=							XLNetForQuestionAnswering(a_)
							else:
														snake_case_				=							XLNetLMHeadModel(a_)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(a_							,     a_							,     a_)

							# Save pytorch-model
							snake_case_				=							os.path.join(a_							,     a_)
							snake_case_				=							os.path.join(a_							,     a_)
							print(f'''Save PyTorch model to {os.path.abspath(a_)}''')
							torch.save(model.state_dict()							,     a_)
							print(f'''Save configuration file to {os.path.abspath(a_)}''')
							with open(a_							,     'w'							,     encoding='utf-8') as f:
														f.write(config.to_json_string())


if __name__ == "__main__":
	lowercase  							=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
	)
	parser.add_argument(
	    "--xlnet_config_file",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        "The config json file corresponding to the pre-trained XLNet model. \n"
	        "This specifies the model architecture."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path to the folder to store the PyTorch model or dataset/vocab.",
	)
	parser.add_argument(
	    "--finetuning_task",
	    default=None,
	    type=str,
	    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
	)
	lowercase  							=					parser.parse_args()
	print(args)

	convert_xlnet_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
	)





