
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					torch.manual_seed(0					)
					lowerCamelCase_         =     UNetaDModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=3		,    out_channels=3		,    down_block_types=("DownBlock2D", "AttnDownBlock2D")		,    up_block_types=("AttnUpBlock2D", "UpBlock2D")		,    )
					return model



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     self.dummy_uncond_unet
					lowerCamelCase_         =     KarrasVeScheduler()

					lowerCamelCase_         =     KarrasVePipeline(unet=lowercase		,    scheduler=lowercase					)
					pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)

					lowerCamelCase_         =     torch.manual_seed(0					)
					lowerCamelCase_         =     pipe(num_inference_steps=2		,    generator=lowercase		,    output_type="numpy"					).images

					lowerCamelCase_         =     torch.manual_seed(0					)
					lowerCamelCase_         =     pipe(num_inference_steps=2		,    generator=lowercase		,    output_type="numpy"		,    return_dict=lowercase					)[0]

					lowerCamelCase_         =     image[0, -3:, -3:, -1]
					lowerCamelCase_         =     image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_         =     np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

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




@slow
@require_torch
class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     "google/ncsnpp-celebahq-256"
					lowerCamelCase_         =     UNetaDModel.from_pretrained(lowercase					)
					lowerCamelCase_         =     KarrasVeScheduler()

					lowerCamelCase_         =     KarrasVePipeline(unet=lowercase		,    scheduler=lowercase					)
					pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)

					lowerCamelCase_         =     torch.manual_seed(0					)
					lowerCamelCase_         =     pipe(num_inference_steps=20		,    generator=lowercase		,    output_type="numpy"					).images

					lowerCamelCase_         =     image[0, -3:, -3:, -1]
					assert image.shape == (1, 256, 256, 3)
					lowerCamelCase_         =     np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]					)

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




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


__A							={
    '''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      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     XLNetConfig.from_json_file(lowerCamelCase__       )

				lowerCamelCase_         =     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}'       )
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     GLUE_TASKS_NUM_LABELS[finetuning_task]
								lowerCamelCase_         =     XLNetForSequenceClassification(lowerCamelCase__       )
				elif "squad" in finetuning_task:
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     XLNetForQuestionAnswering(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     XLNetLMHeadModel(lowerCamelCase__       )

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

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


if __name__ == "__main__":
			__A							=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''',
			)
			__A							=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
			)


