import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					UnCLIPPipeline
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_PARAMS - {
	    'negative_prompt',
	    'height',
	    'width',
	    'negative_prompt_embeds',
	    'guidance_scale',
	    'prompt_embeds',
	    'cross_attention_kwargs',
	}
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_BATCH_PARAMS
	__SCREAMING_SNAKE_CASE              =					[
	    'generator',
	    'return_dict',
	    'prior_num_inference_steps',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	__SCREAMING_SNAKE_CASE              =					False


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim * 4


	@property
	def __lowerCAmelCase      (   self       ):
		return 100


	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
		return tokenizer


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )
		_lowercase   =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=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
		return CLIPTextModelWithProjection(lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "num_attention_heads": 2,
		    "attention_head_dim": 12,
		    "embedding_dim": self.text_embedder_hidden_size,
		    "num_layers": 1,
		}

		_lowercase   =PriorTransformer(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "clip_embeddings_dim": self.text_embedder_hidden_size,
		    "time_embed_dim": self.time_embed_dim,
		    "cross_attention_dim": self.cross_attention_dim,
		}

		_lowercase   =UnCLIPTextProjModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "sample_size": 32,
		    # 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",
		}

		_lowercase   =UNetaDConditionModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		return {
		    "sample_size": 64,
		    "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 __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		# seeded differently to get different unet than `self.dummy_super_res_first`
		torch.manual_seed(1       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dummy_prior
		_lowercase   =self.dummy_decoder
		_lowercase   =self.dummy_text_proj
		_lowercase   =self.dummy_text_encoder
		_lowercase   =self.dummy_tokenizer
		_lowercase   =self.dummy_super_res_first
		_lowercase   =self.dummy_super_res_last

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="sample"       ,  num_train_timesteps=1000       ,  clip_sample_range=5.0       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="learned_range"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   ={
		    "prior": prior,
		    "decoder": decoder,
		    "text_proj": text_proj,
		    "text_encoder": text_encoder,
		    "tokenizer": tokenizer,
		    "super_res_first": super_res_first,
		    "super_res_last": super_res_last,
		    "prior_scheduler": prior_scheduler,
		    "decoder_scheduler": decoder_scheduler,
		    "super_res_scheduler": super_res_scheduler,
		}

		return components


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):
		if str(lowerCAmelCase_       ).startswith("mps"       ):
			_lowercase   =torch.manual_seed(lowerCAmelCase_       )
		else:
			_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(lowerCAmelCase_       )
		_lowercase   ={
		    "prompt": "horse",
		    "generator": generator,
		    "prior_num_inference_steps": 2,
		    "decoder_num_inference_steps": 2,
		    "super_res_num_inference_steps": 2,
		    "output_type": "numpy",
		}
		return inputs


	def __lowerCAmelCase      (   self       ):
		_lowercase   ="cpu"

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =pipe(**self.get_dummy_inputs(lowerCAmelCase_       )       )
		_lowercase   =output.images

		_lowercase   =pipe(
		    **self.get_dummy_inputs(lowerCAmelCase_       )       ,  return_dict=lowerCAmelCase_       ,  )[0]

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

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

		_lowercase   =np.array(
		    [
		        0.9_9_9_7,
		        0.9_9_8_8,
		        0.0_0_2_8,
		        0.9_9_9_7,
		        0.9_9_8_4,
		        0.9_9_6_5,
		        0.0_0_2_9,
		        0.9_9_8_6,
		        0.0_0_2_5,
		    ]       )

		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 __lowerCAmelCase      (   self       ):
		_lowercase   =torch.device("cpu"       )
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					1

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		_lowercase   =components["prior"]
		_lowercase   =components["decoder"]
		_lowercase   =components["super_res_first"]
		_lowercase   =components["tokenizer"]
		_lowercase   =components["text_encoder"]

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =prior.dtype
		_lowercase   =1

		_lowercase   =(batch_size, prior.config.embedding_dim)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )
		_lowercase   =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		_lowercase   =(
		    batch_size,
		    super_res_first.config.in_channels // 2,
		    super_res_first.config.sample_size,
		    super_res_first.config.sample_size,
		)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   ="this is a prompt example"

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    [prompt]       ,  generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  output_type="np"       ,  )
		_lowercase   =output.images

		_lowercase   =tokenizer(
		    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=tokenizer.model_max_length       ,  return_tensors="pt"       ,  )
		_lowercase   =text_encoder(text_inputs.input_ids       )
		_lowercase   =text_inputs.attention_mask

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  text_model_output=lowerCAmelCase_       ,  text_attention_mask=lowerCAmelCase_       ,  output_type="np"       ,  )[0]

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


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"

		self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_       ,  expected_max_diff=0.0_1       )


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"
		_lowercase   =True
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

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


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_local()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_optional_components()

@nightly
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_cpu.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  num_images_per_prompt=1       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)
		assert np.abs(expected_image - image       ).max() < 1e-1

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



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_fp16.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipeline.to(lowerCAmelCase_       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.Generator(device="cpu"       ).manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)

		assert_mean_pixel_difference(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipe.to(lowerCAmelCase_       )
		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		_lowercase   =pipe(
		    "horse"       ,  num_images_per_prompt=1       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  output_type="np"       ,  )

		_lowercase   =torch.cuda.max_memory_allocated()
		# make sure that less than 7 GB is allocated
		assert mem_bytes < 7 * 10**9











"""simple docstring"""




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_attn_layer.in_proj_weight.chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       pt_attn_layer.in_proj_bias.chunk(3			,		dim=0  )

						_UpperCamelCase     :   int      =       pt_attn_layer.out_proj.weight
						_UpperCamelCase     :   List[Any]      =       pt_attn_layer.out_proj.bias

						_UpperCamelCase     :   Any      =       q_proj
						_UpperCamelCase     :   str      =       q_proj_bias

						_UpperCamelCase     :   str      =       k_proj
						_UpperCamelCase     :   List[str]      =       k_proj_bias

						_UpperCamelCase     :   Dict      =       v_proj
						_UpperCamelCase     :   Dict      =       v_proj_bias

						_UpperCamelCase     :   Dict      =       out_proj_weights
						_UpperCamelCase     :   Optional[Any]      =       out_proj_bias





def 			a_							(				_lowercase			,		_lowercase  ):
						copy_linear(hf_mlp.fca			,		pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca			,		pt_mlp.c_proj  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       pt_linear.weight
						_UpperCamelCase     :   List[str]      =       pt_linear.bias





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy layer norms
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )

						# copy MLP
						copy_mlp(hf_layer.mlp			,		pt_layer.mlp  )

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,		pt_layer.attn  )





def 			a_							(				_lowercase			,		_lowercase  ):
						for hf_layer, pt_layer in zip(_lowercase			,		_lowercase  ):
												copy_layer(_lowercase			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy  embeds
						_UpperCamelCase     :   Tuple      =       pt_model.token_embedding.weight
						_UpperCamelCase     :   List[Any]      =       pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,		pt_model.ln_final  )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,		pt_model.transformer.resblocks  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   str      =       pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   Tuple      =       pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,		pt_model.visual.ln_pre  )
						copy_linear(hf_model.vision_model.post_layernorm			,		pt_model.visual.ln_post  )

						# copy embeds
						_UpperCamelCase     :   str      =       pt_model.visual.conva.weight.data
						_UpperCamelCase     :   Dict      =       pt_model.visual.class_embedding
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,		pt_model.visual.transformer.resblocks  )





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None  ):
						if config_path is not None:
												_UpperCamelCase     :   List[Any]      =       CLIPConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   Optional[int]      =       CLIPConfig(projection_dim=512			,		text_config={}			,		vision_config={}  )

						_UpperCamelCase     :   str      =       CLIPModel(_lowercase  ).eval()

						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       load(_lowercase			,		device='''cpu'''			,		jit=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.eval()

						copy_text_model_and_projection(_lowercase			,		_lowercase  )
						copy_vison_model_and_projection(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   int      =       pt_model.logit_scale

						_UpperCamelCase     :   List[Any]      =       torch.arange(0			,		77  ).unsqueeze(0  )
						_UpperCamelCase     :   Optional[int]      =       torch.randn(1			,		3			,		224			,		224  )

						_UpperCamelCase     :   Tuple      =       hf_model(input_ids=_lowercase			,		pixel_values=_lowercase			,		return_dict=_lowercase  )
						_UpperCamelCase     :   Dict      =       hf_outputs.logits_per_image
						_UpperCamelCase     :   Any      =       hf_outputs.logits_per_text
						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_model(_lowercase			,		_lowercase  )

						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )
						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )

						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						UpperCamelCase_	 =parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



