import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       _A=	torch.load(snake_case_       ,  map_location='cpu'   )
       if "model" in sd.keys():
              _A=	torch.load(snake_case_       ,  map_location='cpu'   )["""model"""]

       # pop unnecessary weights
       _A=	[
           """decoder.version""",
           """decoder.output_projection.weight""",
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(snake_case_   )

       _A=	{
           """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
           """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
           """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
           """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     _A=	sd.pop(snake_case_   )

       _A=	list(sd.keys()   )
       for key in keys:
              if ".qkv_proj." in key:
                     _A=	sd[key]
                     # We split QKV in separate Q,K,V

                     _A=	key.replace('.qkv_proj.'       ,  '.q_proj.'   )
                     _A=	key.replace('.qkv_proj.'       ,  '.k_proj.'   )
                     _A=	key.replace('.qkv_proj.'       ,  '.v_proj.'   )

                     _A=	value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     _A=	torch.split(snake_case_       ,  depth // 3       ,  dim=0   )

                     _A=	q
                     _A=	k
                     _A=	v
                     del sd[key]

       return sd

@torch.no_grad()
def 							UpperCamelCase				(    lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None   )		->						str:




       '''simple docstring'''






       _A=	load_checkpoint(snake_case_   )

       if config is not None:
              _A=	OPTConfig.from_pretrained(snake_case_   )
       else:
              _A=	OPTConfig()

       _A=	OPTModel(snake_case_   ).half().eval()
       model.load_state_dict(snake_case_   )

       # Check results
       Path(snake_case_   ).mkdir(exist_ok=snake_case_   )
       model.save_pretrained(snake_case_   )


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--fairseq_path''',
          type=str,
          help=(
              '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
              ''' https://huggingface.co/models?other=opt_metasq'''
          ),
      )
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
      UpperCAmelCase_								=							parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
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      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=UnCLIPImageVariationPipeline
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=IMAGE_VARIATION_BATCH_PARAMS

     _SCREAMING_SNAKE_CASE     :     int		=[
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     @property
     def 			a__  (					self       ):
            return 32



     @property
     def 			a__  (					self       ):
            return 32



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



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



     @property
     def 			a__  (					self       ):
            return 100



     @property
     def 			a__  (					self       ):
            _A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
            return tokenizer



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	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 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	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=32				,      intermediate_size=37				,      patch_size=1				,      )
            return CLIPVisionModelWithProjection(lowerCAmelCase__       )



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

            _A=	{
                'clip_embeddings_dim': self.text_embedder_hidden_size,
                'time_embed_dim': self.time_embed_dim,
                'cross_attention_dim': self.cross_attention_dim,
            }

            _A=	UnCLIPTextProjModel(**lowerCAmelCase__       )
            return model



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

            _A=	{
                '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',
            }

            _A=	UNetaDConditionModel(**lowerCAmelCase__       )
            return model



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

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



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

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



     def 			a__  (					self       ):
            _A=	self.dummy_decoder
            _A=	self.dummy_text_proj
            _A=	self.dummy_text_encoder
            _A=	self.dummy_tokenizer
            _A=	self.dummy_super_res_first
            _A=	self.dummy_super_res_last

            _A=	UnCLIPScheduler(
                variance_type='learned_range'				,      prediction_type='epsilon'				,      num_train_timesteps=1000				,      )

            _A=	UnCLIPScheduler(
                variance_type='fixed_small_log'				,      prediction_type='epsilon'				,      num_train_timesteps=1000				,      )

            _A=	CLIPImageProcessor(crop_size=32				,      size=32       )

            _A=	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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0				,      lowerCAmelCase__=True       ):
            _A=	floats_tensor((1, 3, 32, 32)				,      rng=random.Random(lowerCAmelCase__       )       ).to(lowerCAmelCase__       )
            if str(lowerCAmelCase__       ).startswith('mps'       ):
                   _A=	torch.manual_seed(lowerCAmelCase__       )
            else:
                   _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )

            if pil_image:
                   _A=	input_image * 0.5 + 0.5
                   _A=	input_image.clamp(0				,      1       )
                   _A=	input_image.cpu().permute(0				,      2				,      3				,      1       ).float().numpy()
                   _A=	DiffusionPipeline.numpy_to_pil(lowerCAmelCase__       )[0]

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



     def 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

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

            _A=	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 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

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

            _A=	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 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            _A=	[
                pipeline_inputs['image'],
                pipeline_inputs['image'],
            ]

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            _A=	[
                tuple_pipeline_inputs['image'],
                tuple_pipeline_inputs['image'],
            ]

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

            assert image.shape == (2, 64, 64, 3)

            _A=	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 			a__  (					self       ):
            _A=	torch.device('cpu'       )





            class      lowerCAmelCase						:
                 _SCREAMING_SNAKE_CASE     :     List[str]		=1


            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
            _A=	pipe.decoder.dtype
            _A=	1

            _A=	(
                batch_size,
                pipe.decoder.config.in_channels,
                pipe.decoder.config.sample_size,
                pipe.decoder.config.sample_size,
            )
            _A=	pipe.prepare_latents(
                lowerCAmelCase__				,      dtype=lowerCAmelCase__				,      device=lowerCAmelCase__				,      generator=lowerCAmelCase__				,      latents=lowerCAmelCase__				,      scheduler=DummyScheduler()       )

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

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      decoder_latents=lowerCAmelCase__				,      super_res_latents=lowerCAmelCase__       ).images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            # Don't pass image, instead pass embedding
            _A=	pipeline_inputs.pop('image'       )
            _A=	pipe.image_encoder(lowerCAmelCase__       ).image_embeds

            _A=	pipe(
                **lowerCAmelCase__				,      decoder_latents=lowerCAmelCase__				,      super_res_latents=lowerCAmelCase__				,      image_embeddings=lowerCAmelCase__				,      ).images

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



     @skip_mps
     def 			a__  (					self       ):
            _A=	torch_device == 'cpu'

            # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
            _A=	1E-2

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



     @skip_mps
     def 			a__  (					self       ):
            _A=	torch_device == 'cpu'
            _A=	True
            _A=	[
                '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 			a__  (					self       ):
            _A=	[
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

            if torch_device == "mps":
                   # TODO: MPS errors with larger batch sizes
                   _A=	[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 			a__  (					self       ):
            return super().test_dict_tuple_outputs_equivalent()



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





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





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



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





     def 			a__  (					self       ):
            _A=	load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'       )
            _A=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'       )

            _A=	UnCLIPImageVariationPipeline.from_pretrained(
                'kakaobrain/karlo-v1-alpha-image-variations'				,      torch_dtype=torch.floataa       )
            _A=	pipeline.to(lowerCAmelCase__       )
            pipeline.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.Generator(device='cpu'       ).manual_seed(0       )
            _A=	pipeline(
                lowerCAmelCase__				,      generator=lowerCAmelCase__				,      output_type='np'				,      )

            _A=	output.images[0]

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

            assert_mean_pixel_difference(lowerCAmelCase__				,      lowerCAmelCase__				,      15       )