


import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


snake_case__				:	int					       =					Mapping[str, np.ndarray]
snake_case__				:	int					       =					Mapping[str, Any]  # Is a nested dict.
snake_case__				:	List[Any]					       =					0.0_1





@dataclasses.dataclass(frozen=a__						)
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							42  # [num_res, num_atom_type, 3]

       # Amino-acid type for each residue represented as an integer between 0 and
       # 20, where 20 is 'X'.
       _a   							=							42  # [num_res]

       # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
       # is present and 0.0 if not. This should be used for loss masking.
       _a   							=							42  # [num_res, num_atom_type]

       # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
       _a   							=							42  # [num_res]

       # B-factors, or temperature factors, of each residue (in sq. angstroms units),
       # representing the displacement of the residue from its ground truth mean
       # value.
       _a   							=							42  # [num_res, num_atom_type]

       # Chain indices for multi-chain predictions
       _a   							=							None

       # Optional remark about the protein. Included as a comment in output PDB
       # files
       _a   							=							None

       # Templates used to generate this protein (prediction-only)
       _a   							=							None

       # Chain corresponding to each parent
       _a   							=							None







def    __lowerCamelCase   (      A__						: Tuple					)     ->    Dict:
   lowerCamelCase_						:   str		= R"""(\[[A-Z]+\]\n)"""
   lowerCamelCase_						:   Tuple		= [tag.strip() for tag in re.split(a__						,						a__					) if len(a__					) > 0]
   lowerCamelCase_						:   Tuple		= zip(tags[0::2]						,						[l.split("""\n"""					) for l in tags[1::2]]					)

   lowerCamelCase_						:   Tuple		= ["""N""", """CA""", """C"""]
   lowerCamelCase_						:   List[str]		= None
   lowerCamelCase_						:   Union[str, Any]		= None
   lowerCamelCase_						:   Any		= None
   for g in groups:
      if "[PRIMARY]" == g[0]:
         lowerCamelCase_						:   Any		= g[1][0].strip()
         for i in range(len(a__					)					):
            if seq[i] not in residue_constants.restypes:
               lowerCamelCase_						:   Optional[int]		= """X"""  # FIXME: strings are immutable
         lowerCamelCase_						:   Optional[Any]		= np.array(
             [residue_constants.restype_order.get(a__						,						residue_constants.restype_num					) for res_symbol in seq]					)
      elif "[TERTIARY]" == g[0]:
         lowerCamelCase_						:   Optional[Any]		= []
         for axis in range(3					):
            tertiary.append(list(map(a__						,						g[1][axis].split()					)					)					)
         lowerCamelCase_						:   Any		= np.array(a__					)
         lowerCamelCase_						:   List[Any]		= np.zeros((len(tertiary[0]					) // 3, residue_constants.atom_type_num, 3)					).astype(np.floataa					)
         for i, atom in enumerate(a__					):
            lowerCamelCase_						:   int		= np.transpose(tertiary_np[:, i::3]					)
         atom_positions *= PICO_TO_ANGSTROM
      elif "[MASK]" == g[0]:
         lowerCamelCase_						:   str		= np.array(list(map({"""-""": 0, """+""": 1}.get						,						g[1][0].strip()					)					)					)
         lowerCamelCase_						:   Tuple		= np.zeros(
             (
                 len(a__					),
                 residue_constants.atom_type_num,
             )					).astype(np.floataa					)
         for i, atom in enumerate(a__					):
            lowerCamelCase_						:   List[str]		= 1
         atom_mask *= mask[..., None]

   assert aatype is not None

   return Protein(
       atom_positions=a__						,						atom_mask=a__						,						aatype=a__						,						residue_index=np.arange(len(a__					)					)						,						b_factors=a__						,						)


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Dict = 0					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Optional[Any]		= []

   lowerCamelCase_						:   Any		= prot.remark
   if remark is not None:
      pdb_headers.append(f'''REMARK {remark}'''					)

   lowerCamelCase_						:   str		= prot.parents
   lowerCamelCase_						:   Optional[Any]		= prot.parents_chain_index
   if parents is not None and parents_chain_index is not None:
      lowerCamelCase_						:   List[Any]		= [p for i, p in zip(a__						,						a__					) if i == chain_id]

   if parents is None or len(a__					) == 0:
      lowerCamelCase_						:   Union[str, Any]		= ["""N/A"""]

   pdb_headers.append(f'''PARENT {' '.join(a__					)}'''					)

   return pdb_headers


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: List[str]					)     ->    Optional[int]:
   lowerCamelCase_						:   List[Any]		= []
   lowerCamelCase_						:   List[Any]		= pdb_str.split("""\n"""					)

   lowerCamelCase_						:   int		= prot.remark
   if remark is not None:
      out_pdb_lines.append(f'''REMARK {remark}'''					)

   lowerCamelCase_						:   List[str]		= 42
   if prot.parents is not None and len(prot.parents					) > 0:
      lowerCamelCase_						:   List[Any]		= []
      if prot.parents_chain_index is not None:
         lowerCamelCase_						:   List[str]		= {}
         for p, i in zip(prot.parents						,						prot.parents_chain_index					):
            parent_dict.setdefault(str(a__					)						,						[]					)
            parent_dict[str(a__					)].append(a__					)

         lowerCamelCase_						:   List[Any]		= max([int(a__					) for chain_idx in parent_dict]					)
         for i in range(max_idx + 1					):
            lowerCamelCase_						:   List[Any]		= parent_dict.get(str(a__					)						,						["""N/A"""]					)
            parents_per_chain.append(a__					)
      else:
         parents_per_chain.append(list(prot.parents					)					)
   else:
      lowerCamelCase_						:   Dict		= [["""N/A"""]]

   def make_parent_line(A__						: List[Any]					) -> str:
      return f'''PARENT {' '.join(a__					)}'''

   out_pdb_lines.append(make_parent_line(parents_per_chain[0]					)					)

   lowerCamelCase_						:   Tuple		= 0
   for i, l in enumerate(a__					):
      if "PARENT" not in l and "REMARK" not in l:
         out_pdb_lines.append(a__					)
      if "TER" in l and "END" not in lines[i + 1]:
         chain_counter += 1
         if not chain_counter >= len(a__					):
            lowerCamelCase_						:   Any		= parents_per_chain[chain_counter]
         else:
            lowerCamelCase_						:   Union[str, Any]		= ["""N/A"""]

         out_pdb_lines.append(make_parent_line(a__					)					)

   return "\n".join(a__					)


def    __lowerCamelCase   (      A__						: Tuple					)     ->    int:
   lowerCamelCase_						:   Union[str, Any]		= residue_constants.restypes + ["""X"""]

   def res_atoa(A__						: Dict					) -> str:
      return residue_constants.restype_atoa.get(restypes[r]						,						"""UNK"""					)

   lowerCamelCase_						:   List[Any]		= residue_constants.atom_types

   lowerCamelCase_						:   Dict		= []

   lowerCamelCase_						:   Optional[int]		= prot.atom_mask
   lowerCamelCase_						:   Union[str, Any]		= prot.aatype
   lowerCamelCase_						:   Union[str, Any]		= prot.atom_positions
   lowerCamelCase_						:   Optional[int]		= prot.residue_index.astype(np.intaa					)
   lowerCamelCase_						:   str		= prot.b_factors
   lowerCamelCase_						:   Optional[int]		= prot.chain_index

   if np.any(aatype > residue_constants.restype_num					):
      raise ValueError("""Invalid aatypes."""					)

   lowerCamelCase_						:   Optional[int]		= get_pdb_headers(a__					)
   if len(a__					) > 0:
      pdb_lines.extend(a__					)

   lowerCamelCase_						:   List[Any]		= aatype.shape[0]
   lowerCamelCase_						:   List[str]		= 1
   lowerCamelCase_						:   Tuple		= 0
   lowerCamelCase_						:   Dict		= string.ascii_uppercase
   lowerCamelCase_						:   Tuple		= None
   # Add all atom sites.
   for i in range(a__					):
      lowerCamelCase_						:   List[str]		= res_atoa(aatype[i]					)
      for atom_name, pos, mask, b_factor in zip(a__						,						atom_positions[i]						,						atom_mask[i]						,						b_factors[i]					):
         if mask < 0.5:
            continue

         lowerCamelCase_						:   Optional[Any]		= """ATOM"""
         lowerCamelCase_						:   Dict		= atom_name if len(a__					) == 4 else f''' {atom_name}'''
         lowerCamelCase_						:   Dict		= """"""
         lowerCamelCase_						:   List[Any]		= """"""
         lowerCamelCase_						:   Union[str, Any]		= 1.00
         lowerCamelCase_						:   Optional[int]		= atom_name[0]  # Protein supports only C, N, O, S, this works.
         lowerCamelCase_						:   str		= """"""

         lowerCamelCase_						:   int		= """A"""
         if chain_index is not None:
            lowerCamelCase_						:   Union[str, Any]		= chain_tags[chain_index[i]]

         # PDB is a columnar format, every space matters here!
         lowerCamelCase_						:   Optional[Any]		= (
             f'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
             f'''{res_name_a:>3} {chain_tag:>1}'''
             f'''{residue_index[i]:>4}{insertion_code:>1}   '''
             f'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
             f'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
             f'''{element:>2}{charge:>2}'''
         )
         pdb_lines.append(a__					)
         atom_index += 1

      lowerCamelCase_						:   List[Any]		= i == n - 1
      if chain_index is not None:
         if i != n - 1 and chain_index[i + 1] != prev_chain_index:
            lowerCamelCase_						:   List[Any]		= True
            lowerCamelCase_						:   Any		= chain_index[i + 1]

      if should_terminate:
         # Close the chain.
         lowerCamelCase_						:   Optional[Any]		= """TER"""
         lowerCamelCase_						:   Tuple		= (
             f'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]					):>3} {chain_tag:>1}{residue_index[i]:>4}'''
         )
         pdb_lines.append(a__					)
         atom_index += 1

         if i != n - 1:
            # "prev" is a misnomer here. This happens at the beginning of
            # each new chain.
            pdb_lines.extend(get_pdb_headers(a__						,						a__					)					)

   pdb_lines.append("""END"""					)
   pdb_lines.append(""""""					)
   return "\n".join(a__					)


def    __lowerCamelCase   (      A__						: int					)     ->    int:
   return residue_constants.STANDARD_ATOM_MASK[prot.aatype]



def    __lowerCamelCase   (      A__						: Tuple						,						A__						: Any						,						A__						: Dict = None						,						A__						: Dict = None						,						A__						: Optional[Any] = None						,						A__						: str = None						,						A__						: Optional[Any] = None						,						)     ->    Optional[Any]:
   return Protein(
       aatype=features["""aatype"""]						,						atom_positions=result["""final_atom_positions"""]						,						atom_mask=result["""final_atom_mask"""]						,						residue_index=features["""residue_index"""] + 1						,						b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]					)						,						chain_index=a__						,						remark=a__						,						parents=a__						,						parents_chain_index=a__						,						)








'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = ShapEImgaImgPipeline
 lowerCAmelCase__       = ["image"]
 lowerCAmelCase__       = ["image"]
 lowerCAmelCase__       = [
     "num_images_per_prompt",
     "num_inference_steps",
     "generator",
     "latents",
     "guidance_scale",
     "frame_size",
     "output_type",
     "return_dict",
 ]
 lowerCAmelCase__       = False
 @property
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.time_input_dim * 4
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      return 8
 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[Any]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size   ,				image_size=64   ,				projection_dim=self.text_embedder_hidden_size   ,				intermediate_size=37   ,				num_attention_heads=4   ,				num_channels=3   ,				num_hidden_layers=5   ,				patch_size=1   ,				)

      __SCREAMING_SNAKE_CASE      = CLIPVisionModel(__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = CLIPImageProcessor(
          crop_size=224   ,				do_center_crop=__SCREAMING_SNAKE_CASE   ,				do_normalize=__SCREAMING_SNAKE_CASE   ,				do_resize=__SCREAMING_SNAKE_CASE   ,				image_mean=[0.48145466, 0.4578275, 0.40821073]   ,				image_std=[0.26862954, 0.26130258, 0.27577711]   ,				resample=3   ,				size=224   ,				)

      return image_processor
 @property
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Tuple:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """num_attention_heads""": 2,
          """attention_head_dim""": 16,
          """embedding_dim""": self.time_input_dim,
          """num_embeddings""": 32,
          """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""",
          """embedding_proj_norm_type""": """layer""",
          """encoder_hid_proj_type""": None,
          """added_emb_type""": None,
      }

      __SCREAMING_SNAKE_CASE      = PriorTransformer(**__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """param_shapes""": (
              (self.renderer_dim, 93),
              (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""": 12,
          """background""": (
              0.1,
              0.1,
              0.1,
          ),
      }
      __SCREAMING_SNAKE_CASE      = ShapERenderer(**__SCREAMING_SNAKE_CASE     )
      return model
 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_prior
      __SCREAMING_SNAKE_CASE      = self.dummy_image_encoder
      __SCREAMING_SNAKE_CASE      = self.dummy_image_processor
      __SCREAMING_SNAKE_CASE      = self.dummy_renderer

      __SCREAMING_SNAKE_CASE      = HeunDiscreteScheduler(
          beta_schedule="""exp"""   ,				num_train_timesteps=1_024   ,				prediction_type="""sample"""   ,				use_karras_sigmas=__SCREAMING_SNAKE_CASE   ,				clip_sample=__SCREAMING_SNAKE_CASE   ,				clip_sample_range=1.0   ,				)
      __SCREAMING_SNAKE_CASE      = {
          """prior""": prior,
          """image_encoder""": image_encoder,
          """image_processor""": image_processor,
          """renderer""": renderer,
          """scheduler""": scheduler,
      }

      return components
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			str=0     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {
          """image""": input_image,
          """generator""": generator,
          """num_inference_steps""": 1,
          """frame_size""": 32,
          """output_type""": """np""",
      }
      return inputs
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """cpu"""

      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = output.images[0]
      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]

      assert image.shape == (20, 32, 32, 3)

      __SCREAMING_SNAKE_CASE      = 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  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self._test_inference_batch_consistent(batch_sizes=[1, 2]     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = torch_device == """cpu"""
      __SCREAMING_SNAKE_CASE      = True
      self._test_inference_batch_single_identical(
          batch_size=2   ,				test_max_difference=__SCREAMING_SNAKE_CASE   ,				relax_max_difference=__SCREAMING_SNAKE_CASE   ,				)


 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()
      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 2

      __SCREAMING_SNAKE_CASE      = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = pipe(**__SCREAMING_SNAKE_CASE   ,				num_images_per_prompt=__SCREAMING_SNAKE_CASE     )[0]

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







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



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




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


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""     )
      __SCREAMING_SNAKE_CASE      = load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/shap_e/test_shap_e_img2img_out.npy"""     )
      __SCREAMING_SNAKE_CASE      = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = pipe(
          __SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				guidance_scale=3.0   ,				num_inference_steps=64   ,				frame_size=64   ,				output_type="""np"""   ,				).images[0]

      assert images.shape == (20, 64, 64, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






