"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  UnCLIPImageVariationPipeline
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  IMAGE_VARIATION_BATCH_PARAMS

				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  [
				    """generator""",
				    """return_dict""",
				    """decoder_num_inference_steps""",
				    """super_res_num_inference_steps""",
				]
				SCREAMING_SNAKE_CASE_       :				str       				=  False

				@property
				def 							lowercase_					(     self							:    List[str])->       str:



						'''simple docstring'''


						return 3_2

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



						'''simple docstring'''


						return 3_2

				@property
				def 							lowercase_					(     self							:    int)->       Any:



						'''simple docstring'''


						return self.time_input_dim

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



						'''simple docstring'''


						return self.time_input_dim * 4

				@property
				def 							lowercase_					(     self							:    int)->       Tuple:



						'''simple docstring'''


						return 1_0_0

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
						return tokenizer

				@property
				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	str   				=      CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=self.text_embedder_hidden_size					,  projection_dim=self.text_embedder_hidden_size					,  intermediate_size=3_7					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1_0_0_0					,  )
						return CLIPTextModelWithProjection(UpperCamelCase__)

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



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size					,  projection_dim=self.text_embedder_hidden_size					,  num_hidden_layers=5					,  num_attention_heads=4					,  image_size=3_2					,  intermediate_size=3_7					,  patch_size=1					,  )
						return CLIPVisionModelWithProjection(UpperCamelCase__)

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



						'''simple docstring'''


						torch.manual_seed(0)

						__lowerCAmelCase:	List[Any]   				=      {
						    "clip_embeddings_dim": self.text_embedder_hidden_size,
						    "time_embed_dim": self.time_embed_dim,
						    "cross_attention_dim": self.cross_attention_dim,
						}

						__lowerCAmelCase:	List[str]   				=      UnCLIPTextProjModel(**UpperCamelCase__)
						return model

				@property
				def 							lowercase_					(     self							:    Any)->       Dict:



						'''simple docstring'''


						torch.manual_seed(0)

						__lowerCAmelCase:	Union[str, Any]   				=      {
						    "sample_size": 3_2,
						    # RGB in channels
						    "in_channels": 3,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 6,
						    "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,
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": "identity",
						}

						__lowerCAmelCase:	int   				=      UNetaDConditionModel(**UpperCamelCase__)
						return model

				@property
				def 							lowercase_					(     self							:    List[str])->       Dict:



						'''simple docstring'''


						return {
						    "sample_size": 6_4,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}

				@property
				def 							lowercase_					(     self							:    Any)->       Tuple:



						'''simple docstring'''


						torch.manual_seed(0)

						__lowerCAmelCase:	Optional[Any]   				=      UNetaDModel(**self.dummy_super_res_kwargs)
						return model

				@property
				def 							lowercase_					(     self							:    str)->       Tuple:



						'''simple docstring'''


						torch.manual_seed(1)

						__lowerCAmelCase:	Union[str, Any]   				=      UNetaDModel(**self.dummy_super_res_kwargs)
						return model

				def 							lowercase_					(     self							:    Optional[Any])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.dummy_decoder
						__lowerCAmelCase:	str   				=      self.dummy_text_proj
						__lowerCAmelCase:	Tuple   				=      self.dummy_text_encoder
						__lowerCAmelCase:	List[str]   				=      self.dummy_tokenizer
						__lowerCAmelCase:	Dict   				=      self.dummy_super_res_first
						__lowerCAmelCase:	List[str]   				=      self.dummy_super_res_last

						__lowerCAmelCase:	str   				=      UnCLIPScheduler(
						    variance_type="learned_range"					,  prediction_type="epsilon"					,  num_train_timesteps=1_0_0_0					,  )

						__lowerCAmelCase:	Optional[Any]   				=      UnCLIPScheduler(
						    variance_type="fixed_small_log"					,  prediction_type="epsilon"					,  num_train_timesteps=1_0_0_0					,  )

						__lowerCAmelCase:	int   				=      CLIPImageProcessor(crop_size=3_2					,  size=3_2)

						__lowerCAmelCase:	Dict   				=      self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    int=0					,  UpperCamelCase__							:    Optional[int]=True)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      floats_tensor((1, 3, 3_2, 3_2)					,  rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
						if str(UpperCamelCase__).startswith("mps"):
								__lowerCAmelCase:	str   				=      torch.manual_seed(UpperCamelCase__)
						else:
								__lowerCAmelCase:	Optional[int]   				=      torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)

						if pil_image:
								__lowerCAmelCase:	List[str]   				=      input_image * 0.5 + 0.5
								__lowerCAmelCase:	str   				=      input_image.clamp(0					,  1)
								__lowerCAmelCase:	Optional[Any]   				=      input_image.cpu().permute(0					,  2					,  3					,  1).float().numpy()
								__lowerCAmelCase:	List[Any]   				=      DiffusionPipeline.numpy_to_pil(UpperCamelCase__)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}

				def 							lowercase_					(     self							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      "cpu"

						__lowerCAmelCase:	Optional[Any]   				=      self.get_dummy_components()

						__lowerCAmelCase:	Optional[int]   				=      self.pipeline_class(**UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      pipe.to(UpperCamelCase__)

						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	str   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      pipe(**UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      output.images

						__lowerCAmelCase:	int   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      pipe(
						    **UpperCamelCase__					,  return_dict=UpperCamelCase__					,  )[0]

						__lowerCAmelCase:	Dict   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	List[str]   				=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						__lowerCAmelCase:	Union[str, Any]   				=      np.array(
						    [
						        0.9997,
						        0.0002,
						        0.9997,
						        0.9997,
						        0.9969,
						        0.0023,
						        0.9997,
						        0.9969,
						        0.9970,
						    ])

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

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      "cpu"

						__lowerCAmelCase:	List[Any]   				=      self.get_dummy_components()

						__lowerCAmelCase:	Optional[Any]   				=      self.pipeline_class(**UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      pipe.to(UpperCamelCase__)

						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      pipe(**UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      output.images

						__lowerCAmelCase:	List[Any]   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      pipe(
						    **UpperCamelCase__					,  return_dict=UpperCamelCase__					,  )[0]

						__lowerCAmelCase:	Any   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	Optional[Any]   				=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						__lowerCAmelCase:	Any   				=      np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])

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

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      "cpu"

						__lowerCAmelCase:	Dict   				=      self.get_dummy_components()

						__lowerCAmelCase:	Optional[int]   				=      self.pipeline_class(**UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      pipe.to(UpperCamelCase__)

						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	str   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)
						__lowerCAmelCase:	int   				=      [
						    pipeline_inputs["image"],
						    pipeline_inputs["image"],
						]

						__lowerCAmelCase:	Dict   				=      pipe(**UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      output.images

						__lowerCAmelCase:	Optional[Any]   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      [
						    tuple_pipeline_inputs["image"],
						    tuple_pipeline_inputs["image"],
						]

						__lowerCAmelCase:	int   				=      pipe(
						    **UpperCamelCase__					,  return_dict=UpperCamelCase__					,  )[0]

						__lowerCAmelCase:	Union[str, Any]   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	Optional[int]   				=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (2, 6_4, 6_4, 3)

						__lowerCAmelCase:	Union[str, Any]   				=      np.array(
						    [
						        0.9997,
						        0.9989,
						        0.0008,
						        0.0021,
						        0.9960,
						        0.0018,
						        0.0014,
						        0.0002,
						        0.9933,
						    ])

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

				def 							lowercase_					(     self							:    Optional[Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      torch.device("cpu")





						class    snake_case     :
										SCREAMING_SNAKE_CASE_       :				str       				=  1


						__lowerCAmelCase:	str   				=      self.get_dummy_components()

						__lowerCAmelCase:	List[str]   				=      self.pipeline_class(**UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      pipe.to(UpperCamelCase__)

						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:	Dict   				=      pipe.decoder.dtype
						__lowerCAmelCase:	Any   				=      1

						__lowerCAmelCase:	Any   				=      (
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						__lowerCAmelCase:	str   				=      pipe.prepare_latents(
						    UpperCamelCase__					,  dtype=UpperCamelCase__					,  device=UpperCamelCase__					,  generator=UpperCamelCase__					,  latents=UpperCamelCase__					,  scheduler=DummyScheduler())

						__lowerCAmelCase:	Tuple   				=      (
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						__lowerCAmelCase:	List[str]   				=      pipe.prepare_latents(
						    UpperCamelCase__					,  dtype=UpperCamelCase__					,  device=UpperCamelCase__					,  generator=UpperCamelCase__					,  latents=UpperCamelCase__					,  scheduler=DummyScheduler())

						__lowerCAmelCase:	int   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)

						__lowerCAmelCase:	Union[str, Any]   				=      pipe(
						    **UpperCamelCase__					,  decoder_latents=UpperCamelCase__					,  super_res_latents=UpperCamelCase__).images

						__lowerCAmelCase:	Tuple   				=      self.get_dummy_inputs(UpperCamelCase__					,  pil_image=UpperCamelCase__)
						# Don't pass image, instead pass embedding
						__lowerCAmelCase:	int   				=      pipeline_inputs.pop("image")
						__lowerCAmelCase:	Optional[int]   				=      pipe.image_encoder(UpperCamelCase__).image_embeds

						__lowerCAmelCase:	List[Any]   				=      pipe(
						    **UpperCamelCase__					,  decoder_latents=UpperCamelCase__					,  super_res_latents=UpperCamelCase__					,  image_embeddings=UpperCamelCase__					,  ).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a).max() < 1e-4

				@skip_mps
				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      torch_device == "cpu"

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						__lowerCAmelCase:	List[str]   				=      1e-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=UpperCamelCase__					,  expected_max_diff=UpperCamelCase__)

				@skip_mps
				def 							lowercase_					(     self							:    Dict)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      torch_device == "cpu"
						__lowerCAmelCase:	int   				=      True
						__lowerCAmelCase:	Union[str, Any]   				=      [
						    "decoder_num_inference_steps",
						    "super_res_num_inference_steps",
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=UpperCamelCase__					,  relax_max_difference=UpperCamelCase__					,  additional_params_copy_to_batched_inputs=UpperCamelCase__					,  )

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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      [
						    "decoder_num_inference_steps",
						    "super_res_num_inference_steps",
						]

						if torch_device == "mps":
								# TODO: MPS errors with larger batch sizes
								__lowerCAmelCase:	Optional[Any]   				=      [2, 3]
								self._test_inference_batch_consistent(
								    batch_sizes=UpperCamelCase__					,  additional_params_copy_to_batched_inputs=UpperCamelCase__					,  )
						else:
								self._test_inference_batch_consistent(
								    additional_params_copy_to_batched_inputs=UpperCamelCase__)

				@skip_mps
				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def 							lowercase_					(     self							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


						return super().test_save_load_local()




				@skip_mps
				def 							lowercase_					(     self							:    Optional[Any])->       List[Any]:



						'''simple docstring'''


						return super().test_save_load_optional_components()





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

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


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




				def 							lowercase_					(     self							:    str)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png")
						__lowerCAmelCase:	int   				=      load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy")

						__lowerCAmelCase:	Dict   				=      UnCLIPImageVariationPipeline.from_pretrained(
						    "kakaobrain/karlo-v1-alpha-image-variations"					,  torch_dtype=torch.floataa)
						__lowerCAmelCase:	List[str]   				=      pipeline.to(UpperCamelCase__)
						pipeline.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      torch.Generator(device="cpu").manual_seed(0)
						__lowerCAmelCase:	int   				=      pipeline(
						    UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type="np"					,  )

						__lowerCAmelCase:	int   				=      output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						assert_mean_pixel_difference(UpperCamelCase__					,  UpperCamelCase__					,  1_5)





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


