


"""simple docstring"""



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


__snake_case         =					{
    """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		(		lowercase						:						Tuple		,							lowercase						:						Union[str, Any]		,							lowercase						:						str		,							lowercase						:						Optional[int]=None				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					XLNetConfig.from_json_file(lowercase				)

    snake_case					:   int         =					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					:   Optional[int]         =					finetuning_task
        snake_case					:   Optional[Any]         =					GLUE_TASKS_NUM_LABELS[finetuning_task]
        snake_case					:   List[str]         =					XLNetForSequenceClassification(lowercase				)
    elif "squad" in finetuning_task:
        snake_case					:   List[str]         =					finetuning_task
        snake_case					:   Any         =					XLNetForQuestionAnswering(lowercase				)
    else:
        snake_case					:   str         =					XLNetLMHeadModel(lowercase				)

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

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


if __name__ == "__main__":
   __snake_case         =					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""",
   )
   __snake_case         =					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
   )









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 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 _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

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





