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 transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)