import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       KandinskyVaaControlnetImgaImgPipeline
      _snake_case    		=       ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      _snake_case    		=       ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      _snake_case    		=       [
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _snake_case    		=       False



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          return 32



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          return 32



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


          """simple docstring"""


          return self.time_input_dim



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          return self.time_input_dim * 4



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->str:


          """simple docstring"""


          return 100



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->List[Any]:


          """simple docstring"""


          torch.manual_seed(0)

          _lowerCamelCase     :		Optional[Any] 						=						{
              """in_channels""": 8,
              # Out channels is double in channels because predicts mean and variance
              """out_channels""": 8,
              """addition_embed_type""": """image_hint""",
              """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
              """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
              """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
              """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
              """layers_per_block""": 1,
              """encoder_hid_dim""": self.text_embedder_hidden_size,
              """encoder_hid_dim_type""": """image_proj""",
              """cross_attention_dim""": self.cross_attention_dim,
              """attention_head_dim""": 4,
              """resnet_time_scale_shift""": """scale_shift""",
              """class_embed_type""": None,
          }

          _lowerCamelCase     :		List[Any] 						=						UNetaDConditionModel(**_UpperCamelCase)
          return model



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->List[Any]:


          """simple docstring"""


          return {
              "block_out_channels": [32, 32, 64, 64],
              "down_block_types": [
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "AttnDownEncoderBlock2D",
              ],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          torch.manual_seed(0)
          _lowerCamelCase     :		List[Any] 						=						VQModel(**self.dummy_movq_kwargs)
          return model



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.dummy_unet
          _lowerCamelCase     :		Dict 						=						self.dummy_movq

          _lowerCamelCase     :		List[str] 						=						{
              """num_train_timesteps""": 1000,
              """beta_schedule""": """linear""",
              """beta_start""": 0.0_0_0_8_5,
              """beta_end""": 0.0_1_2,
              """clip_sample""": False,
              """set_alpha_to_one""": False,
              """steps_offset""": 0,
              """prediction_type""": """epsilon""",
              """thresholding""": False,
          }

          _lowerCamelCase     :		Dict 						=						DDIMScheduler(**_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						{
              """unet""": unet,
              """scheduler""": scheduler,
              """movq""": movq,
          }

          return components



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Any=0)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
          _lowerCamelCase     :		str 						=						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(seed + 1)).to(
              _UpperCamelCase)
          # create init_image
          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						image.cpu().permute(0    ,  2    ,  3    ,  1)[0]
          _lowerCamelCase     :		int 						=						Image.fromarray(np.uinta(_UpperCamelCase)).convert("""RGB""").resize((256, 256))
          # create hint
          _lowerCamelCase     :		List[Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)

          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		Any 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		Any 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						{
              """image""": init_image,
              """image_embeds""": image_embeds,
              """negative_image_embeds""": negative_image_embeds,
              """hint""": hint,
              """generator""": generator,
              """height""": 64,
              """width""": 64,
              """num_inference_steps""": 10,
              """guidance_scale""": 7.0,
              """strength""": 0.2,
              """output_type""": """np""",
          }
          return inputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						"""cpu"""

          _lowerCamelCase     :		Tuple 						=						self.get_dummy_components()

          _lowerCamelCase     :		Union[str, Any] 						=						self.pipeline_class(**_UpperCamelCase)
          _lowerCamelCase     :		str 						=						pipe.to(_UpperCamelCase)

          pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						pipe(**self.get_dummy_inputs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						output.images

          _lowerCamelCase     :		Dict 						=						pipe(
              **self.get_dummy_inputs(_UpperCamelCase)    ,  return_dict=_UpperCamelCase    ,  )[0]

          _lowerCamelCase     :		Optional[int] 						=						image[0, -3:, -3:, -1]
          _lowerCamelCase     :		int 						=						image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          _lowerCamelCase     :		str 						=						np.array(
              [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6])
          assert (
              np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
          ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
          ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""")

          _lowerCamelCase     :		Any 						=						load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
          _lowerCamelCase     :		Optional[int] 						=						init_image.resize((512, 512))

          _lowerCamelCase     :		Dict 						=						load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinskyv22/hint_image_cat.png""")
          _lowerCamelCase     :		int 						=						torch.from_numpy(np.array(_UpperCamelCase)).float() / 2_5_5.0
          _lowerCamelCase     :		Dict 						=						hint.permute(2    ,  0    ,  1).unsqueeze(0)

          _lowerCamelCase     :		Tuple 						=						"""A robot, 4k photo"""

          _lowerCamelCase     :		Optional[int] 						=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-prior"""    ,  torch_dtype=torch.floataa)
          pipe_prior.to(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-controlnet-depth"""    ,  torch_dtype=torch.floataa)
          _lowerCamelCase     :		List[str] 						=						pipeline.to(_UpperCamelCase)

          pipeline.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase     :		str 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						pipe_prior(
              _UpperCamelCase    ,  image=_UpperCamelCase    ,  strength=0.8_5    ,  generator=_UpperCamelCase    ,  negative_prompt=""""""    ,  ).to_tuple()

          _lowerCamelCase     :		Optional[Any] 						=						pipeline(
              image=_UpperCamelCase    ,  image_embeds=_UpperCamelCase    ,  negative_image_embeds=_UpperCamelCase    ,  hint=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=100    ,  height=512    ,  width=512    ,  strength=0.5    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		List[Any] 						=						output.images[0]

          assert image.shape == (512, 512, 3)

          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)





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


lowerCAmelCase			:							int     ={
    "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 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						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     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

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

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						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__":
    lowerCAmelCase			:							Dict     =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",
    )
    lowerCAmelCase			:							Union[str, Any]     =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
    )




