





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


lowercase_					     =	Mapping[str, np.ndarray]
lowercase_					     =	Mapping[str, Any]  # Is a nested dict.
lowercase_					     =	0.01
@dataclasses.dataclass(frozen=UpperCAmelCase   )
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	np.ndarray  # [num_res, num_atom_type, 3]

   # Amino-acid type for each residue represented as an integer between 0 and
   # 20, where 20 is 'X'.
   _UpperCamelCase					:	np.ndarray  # [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.
   _UpperCamelCase					:	np.ndarray  # [num_res, num_atom_type]

   # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
   _UpperCamelCase					:	np.ndarray  # [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.
   _UpperCamelCase					:	np.ndarray  # [num_res, num_atom_type]

   # Chain indices for multi-chain predictions
   _UpperCamelCase					:	Optional[np.ndarray]				      = None

   # Optional remark about the protein. Included as a comment in output PDB
   # files
   _UpperCamelCase					:	Optional[str]				      = None

   # Templates used to generate this protein (prediction-only)
   _UpperCamelCase					:	Optional[Sequence[str]]				      = None

   # Chain corresponding to each parent
   _UpperCamelCase					:	Optional[Sequence[int]]				      = None






def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Protein:
  lowercase__			    =						R'(\[[A-Z]+\]\n)'
  lowercase__			    =						[tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	) if len(_SCREAMING_SNAKE_CASE	) > 0]
  lowercase__			    =						zip(tags[0::2]	,		[l.split('\n'	) for l in tags[1::2]]	)

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

  assert aatype is not None

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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 0	)       ->     List[str]:
  lowercase__			    =						[]

  lowercase__			    =						prot.remark
  if remark is not None:
    pdb_headers.append(F"""REMARK {remark}"""	)

  lowercase__			    =						prot.parents
  lowercase__			    =						prot.parents_chain_index
  if parents is not None and parents_chain_index is not None:
    lowercase__			    =						[p for i, p in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	) if i == chain_id]

  if parents is None or len(_SCREAMING_SNAKE_CASE	) == 0:
    lowercase__			    =						['N/A']

  pdb_headers.append(F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE	)}"""	)

  return pdb_headers




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						[]
  lowercase__			    =						pdb_str.split('\n'	)

  lowercase__			    =						prot.remark
  if remark is not None:
    out_pdb_lines.append(F"""REMARK {remark}"""	)

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

      lowercase__			    =						max([int(_SCREAMING_SNAKE_CASE	) for chain_idx in parent_dict]	)
      for i in range(max_idx + 1	):
        lowercase__			    =						parent_dict.get(str(_SCREAMING_SNAKE_CASE	)	,		['N/A']	)
        parents_per_chain.append(_SCREAMING_SNAKE_CASE	)
    else:
      parents_per_chain.append(list(prot.parents	)	)
  else:
    lowercase__			    =						[['N/A']]

  def make_parent_line(_SCREAMING_SNAKE_CASE	) -> str:
    return F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE	)}"""

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

  lowercase__			    =						0
  for i, l in enumerate(_SCREAMING_SNAKE_CASE	):
    if "PARENT" not in l and "REMARK" not in l:
      out_pdb_lines.append(_SCREAMING_SNAKE_CASE	)
    if "TER" in l and "END" not in lines[i + 1]:
      chain_counter += 1
      if not chain_counter >= len(_SCREAMING_SNAKE_CASE	):
        lowercase__			    =						parents_per_chain[chain_counter]
      else:
        lowercase__			    =						['N/A']

      out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE	)	)

  return "\n".join(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						residue_constants.restypes + ['X']

  def res_atoa(_SCREAMING_SNAKE_CASE	) -> str:
    return residue_constants.restype_atoa.get(restypes[r]	,		'UNK'	)

  lowercase__			    =						residue_constants.atom_types

  lowercase__			    =						[]

  lowercase__			    =						prot.atom_mask
  lowercase__			    =						prot.aatype
  lowercase__			    =						prot.atom_positions
  lowercase__			    =						prot.residue_index.astype(np.intaa	)
  lowercase__			    =						prot.b_factors
  lowercase__			    =						prot.chain_index

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

  lowercase__			    =						get_pdb_headers(_SCREAMING_SNAKE_CASE	)
  if len(_SCREAMING_SNAKE_CASE	) > 0:
    pdb_lines.extend(_SCREAMING_SNAKE_CASE	)

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

      lowercase__			    =						'ATOM'
      lowercase__			    =						atom_name if len(_SCREAMING_SNAKE_CASE	) == 4 else F""" {atom_name}"""
      lowercase__			    =						''
      lowercase__			    =						''
      lowercase__			    =						1.0_0
      lowercase__			    =						atom_name[0]  # Protein supports only C, N, O, S, this works.
      lowercase__			    =						''

      lowercase__			    =						'A'
      if chain_index is not None:
        lowercase__			    =						chain_tags[chain_index[i]]

      # PDB is a columnar format, every space matters here!
      lowercase__			    =						(
          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(_SCREAMING_SNAKE_CASE	)
      atom_index += 1

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

    if should_terminate:
      # Close the chain.
      lowercase__			    =						'TER'
      lowercase__			    =						(
          F"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]	):>3} {chain_tag:>1}{residue_index[i]:>4}"""
      )
      pdb_lines.append(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	)

  pdb_lines.append('END'	)
  pdb_lines.append(''	)
  return "\n".join(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     np.ndarray:
  return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		)       ->     Protein:
  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=_SCREAMING_SNAKE_CASE	,		remark=_SCREAMING_SNAKE_CASE	,		parents=_SCREAMING_SNAKE_CASE	,		parents_chain_index=_SCREAMING_SNAKE_CASE	,		)









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase_					     =	False
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   pass






@nightly
@require_torch_gpu
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(a )
       lowercase__			    =						VersatileDiffusionPipeline.from_pretrained(a    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						generator.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'cyberpunk 2077'
     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt=a    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy'    ,  ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )

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

     lowercase__			    =						'A painting of a squirrel eating a burger '
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.text_to_image(
         prompt=a    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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

     lowercase__			    =						pipe.image_variation(a    ,  generator=a    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )

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


