





'''simple docstring'''






import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		ShapEPipeline
    lowerCAmelCase_				    =		['prompt']
    lowerCAmelCase_				    =		['prompt']
    lowerCAmelCase_				    =		[
        'num_images_per_prompt',
        'num_inference_steps',
        'generator',
        'latents',
        'guidance_scale',
        'frame_size',
        'output_type',
        'return_dict',
    ]
    lowerCAmelCase_				    =		False



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return 3_2



    @property
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return 3_2



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return self.time_input_dim * 4



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return 8



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



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Tuple								=   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(__A     )



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          torch.manual_seed(0     )

          _lowerCamelCase				:   Union[str, Any]								=   {
              "num_attention_heads": 2,
              "attention_head_dim": 1_6,
              "embedding_dim": self.time_input_dim,
              "num_embeddings": 3_2,
              "embedding_proj_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "num_layers": 1,
              "clip_embed_dim": self.time_input_dim * 2,
              "additional_embeddings": 0,
              "time_embed_act_fn": "gelu",
              "norm_in_type": "layer",
              "encoder_hid_proj_type": None,
              "added_emb_type": None,
          }

          _lowerCamelCase				:   Any								=   PriorTransformer(**__A     )
          return model



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          torch.manual_seed(0     )

          _lowerCamelCase				:   List[str]								=   {
              "param_shapes": (
                  (self.renderer_dim, 9_3),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
              ),
              "d_latent": self.time_input_dim,
              "d_hidden": self.renderer_dim,
              "n_output": 1_2,
              "background": (
                  0.1,
                  0.1,
                  0.1,
              ),
          }
          _lowerCamelCase				:   Any								=   ShapERenderer(**__A     )
          return model



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Tuple								=   self.dummy_prior
          _lowerCamelCase				:   Tuple								=   self.dummy_text_encoder
          _lowerCamelCase				:   List[str]								=   self.dummy_tokenizer
          _lowerCamelCase				:   Dict								=   self.dummy_renderer

          _lowerCamelCase				:   Optional[int]								=   HeunDiscreteScheduler(
              beta_schedule="exp",num_train_timesteps=1_0_2_4,prediction_type="sample",use_karras_sigmas=__A,clip_sample=__A,clip_sample_range=1.0,)
          _lowerCamelCase				:   Optional[int]								=   {
              "prior": prior,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "renderer": renderer,
              "scheduler": scheduler,
          }

          return components



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Any,__A      :       str=0     ):
          if str(__A     ).startswith("mps"     ):
                _lowerCamelCase				:   int								=   torch.manual_seed(__A     )
          else:
                _lowerCamelCase				:   Optional[Any]								=   torch.Generator(device=__A     ).manual_seed(__A     )
          _lowerCamelCase				:   Optional[Any]								=   {
              "prompt": "horse",
              "generator": generator,
              "num_inference_steps": 1,
              "frame_size": 3_2,
              "output_type": "np",
          }
          return inputs



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   "cpu"

          _lowerCamelCase				:   Any								=   self.get_dummy_components()

          _lowerCamelCase				:   Optional[int]								=   self.pipeline_class(**__A     )
          _lowerCamelCase				:   Any								=   pipe.to(__A     )

          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Dict								=   pipe(**self.get_dummy_inputs(__A     )     )
          _lowerCamelCase				:   str								=   output.images[0]
          _lowerCamelCase				:   int								=   image[0, -3:, -3:, -1]

          assert image.shape == (2_0, 3_2, 3_2, 3)

          _lowerCamelCase				:   Any								=   np.array(
              [
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
                  0.00039216,
              ]     )

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



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
          self._test_inference_batch_consistent(batch_sizes=[1, 2]     )



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Tuple								=   torch_device == "cpu"
          _lowerCamelCase				:   Optional[Any]								=   True

          self._test_inference_batch_single_identical(
              batch_size=2,test_max_difference=__A,relax_max_difference=__A,)



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   str								=   self.get_dummy_components()
          _lowerCamelCase				:   Optional[Any]								=   self.pipeline_class(**__A     )
          _lowerCamelCase				:   Union[str, Any]								=   pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   List[str]								=   1
          _lowerCamelCase				:   Any								=   2

          _lowerCamelCase				:   List[str]								=   self.get_dummy_inputs(__A     )

          for key in inputs.keys():
                if key in self.batch_params:
                      _lowerCamelCase				:   Optional[int]								=   batch_size * [inputs[key]]

          _lowerCamelCase				:   Tuple								=   pipe(**__A,num_images_per_prompt=__A     )[0]

          assert images.shape[0] == batch_size * num_images_per_prompt


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



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



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/shap_e/test_shap_e_np_out.npy"     )
          _lowerCamelCase				:   Union[str, Any]								=   ShapEPipeline.from_pretrained("openai/shap-e"     )
          _lowerCamelCase				:   Optional[Any]								=   pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Optional[int]								=   torch.Generator(device=__A     ).manual_seed(0     )

          _lowerCamelCase				:   Tuple								=   pipe(
              "a shark",generator=__A,guidance_scale=15.0,num_inference_steps=6_4,frame_size=6_4,output_type="np",).images[0]

          assert images.shape == (2_0, 6_4, 6_4, 3)

          assert_mean_pixel_difference(__A,__A     )

'''simple docstring'''






import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

      # pop unnecessary weights
      _lowerCamelCase				:   Optional[Any]								=   [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Union[str, Any]								=   {
          "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:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

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

                  _lowerCamelCase				:   int								=   key.replace(".qkv_proj."				,	".q_proj."							)
                  _lowerCamelCase				:   List[Any]								=   key.replace(".qkv_proj."				,	".k_proj."							)
                  _lowerCamelCase				:   Dict								=   key.replace(".qkv_proj."				,	".v_proj."							)

                  _lowerCamelCase				:   Any								=   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
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

                  _lowerCamelCase				:   str								=   q
                  _lowerCamelCase				:   Dict								=   k
                  _lowerCamelCase				:   int								=   v
                  del sd[key]

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

      if config is not None:
            _lowerCamelCase				:   str								=   OPTConfig.from_pretrained(_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Any								=   OPTConfig()

      _lowerCamelCase				:   Optional[int]								=   OPTModel(_lowerCAmelCase							).half().eval()
      model.load_state_dict(_lowerCAmelCase							)

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


if __name__ == "__main__":
 UpperCAmelCase_       :   List[str]   =       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_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)