





"""simple docstring"""






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


A	     =					Mapping[str, np.ndarray]
A	     =					Mapping[str, Any]  # Is a nested dict.
A	     =					0.01




@dataclasses.dataclass(frozen=_UpperCamelCase							)
class 			__lowercase     :







					'''simple docstring'''




					__lowerCAmelCase					=					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'.
					__lowerCAmelCase					=					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.
					__lowerCAmelCase					=					42  # [num_res, num_atom_type]

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

					# Chain indices for multi-chain predictions
					__lowerCAmelCase					=					None

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

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

					# Chain corresponding to each parent
					__lowerCAmelCase					=					None






def       __A (     a_  :str)						->						Protein:
				__a	:  int         =					R'''(\[[A-Z]+\]\n)'''
				__a	:  List[str]         =					[tag.strip() for tag in re.split(a_				,    a_) if len(a_) > 0]
				__a	:  Iterator[Tuple[str, List[str]]]         =					zip(tags[0::2]				,    [l.split('''\n''') for l in tags[1::2]])

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

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

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

				if parents is None or len(a_) == 0:
								__a	:  Any         =					['''N/A''']

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

				return pdb_headers

def       __A (     a_  :Protein				,    a_  :str)						->						str:
				__a	:  List[str]         =					[]
				__a	:  str         =					pdb_str.split('''\n''')

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

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

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

				def make_parent_line(a_  :Sequence[str]) -> str:
								return F"""PARENT {" ".join(a_)}"""

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

				__a	:  str         =					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_):
																__a	:  Union[str, Any]         =					parents_per_chain[chain_counter]
												else:
																__a	:  Union[str, Any]         =					['''N/A''']

												out_pdb_lines.append(make_parent_line(a_))

				return "\n".join(a_)

def       __A (     a_  :Protein)						->						str:
				__a	:  List[Any]         =					residue_constants.restypes + ['''X''']

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

				__a	:  int         =					residue_constants.atom_types

				__a	:  List[str]         =					[]

				__a	:  List[Any]         =					prot.atom_mask
				__a	:  Tuple         =					prot.aatype
				__a	:  List[str]         =					prot.atom_positions
				__a	:  List[Any]         =					prot.residue_index.astype(np.intaa)
				__a	:  Optional[Any]         =					prot.b_factors
				__a	:  Dict         =					prot.chain_index

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

				__a	:  str         =					get_pdb_headers(a_)
				if len(a_) > 0:
								pdb_lines.extend(a_)

				__a	:  Optional[int]         =					aatype.shape[0]
				__a	:  Any         =					1
				__a	:  Any         =					0
				__a	:  Optional[Any]         =					string.ascii_uppercase
				__a	:  List[str]         =					None
				# Add all atom sites.
				for i in range(a_):
								__a	:  Dict         =					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

												__a	:  str         =					'''ATOM'''
												__a	:  Any         =					atom_name if len(a_) == 4 else F""" {atom_name}"""
												__a	:  Union[str, Any]         =					''''''
												__a	:  List[str]         =					''''''
												__a	:  Optional[Any]         =					1.0_0
												__a	:  Optional[int]         =					atom_name[0]  # Protein supports only C, N, O, S, this works.
												__a	:  Tuple         =					''''''

												__a	:  Any         =					'''A'''
												if chain_index is not None:
																__a	:  Optional[int]         =					chain_tags[chain_index[i]]

												# PDB is a columnar format, every space matters here!
												__a	:  Union[str, 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

								__a	:  Dict         =					i == n - 1
								if chain_index is not None:
												if i != n - 1 and chain_index[i + 1] != prev_chain_index:
																__a	:  Union[str, Any]         =					True
																__a	:  str         =					chain_index[i + 1]

								if should_terminate:
												# Close the chain.
												__a	:  Tuple         =					'''TER'''
												__a	:  Dict         =					(
												    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       __A (     a_  :Protein)						->						np.ndarray:
				return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def       __A (     a_  :FeatureDict				,    a_  :ModelOutput				,    a_  :Optional[np.ndarray] = None				,    a_  :Optional[np.ndarray] = None				,    a_  :Optional[str] = None				,    a_  :Optional[Sequence[str]] = None				,    a_  :Optional[Sequence[int]] = 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=a_				,    remark=a_				,    parents=a_				,    parents_chain_index=a_				,    )






"""simple docstring"""






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:




		class 			__lowercase     :







							'''simple docstring'''




							@staticmethod
							def 	_lowerCamelCase						(  *_UpperCAmelCase		,     **_UpperCAmelCase       ):
											pass

def       __A (     a_  :Image)						->						str:
				__a	:  List[str]         =					hashlib.mda(image.tobytes())
				return m.hexdigest()[:10]

def       __A (     a_  :Image)						->						Dict:
				__a	:  Any         =					np.array(a_)
				__a	:  Tuple         =					npimg.shape
				return {"hash": hashimage(a_), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
					__lowerCAmelCase					=					dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					MaskGenerationPipeline(model=_UpperCAmelCase		,     image_processor=_UpperCAmelCase       )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									pass







					@require_tf
					@unittest.skip('''Image segmentation not implemented in TF'''       )
					def 	_lowerCamelCase						(  self       ):
									pass







					@slow
					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					pipeline('''mask-generation'''		,     model='''facebook/sam-vit-huge'''       )

									__a	:  Optional[Any]         =					image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									for i, o in enumerate(outputs['''masks''']       ):
													new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase       ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
									        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
									        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
									        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
									        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
									        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
									        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
									        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
									        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
									        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
									        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
									        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
									        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
									        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
									        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
									        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
									        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
									        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
									        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
									        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
									        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
									        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
									        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
									    ]		,     )
									# fmt: on







					@require_torch
					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					'''facebook/sam-vit-huge'''
									__a	:  Tuple         =					pipeline('''mask-generation'''		,     model=_UpperCAmelCase       )

									__a	:  List[Any]         =					image_segmenter(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     pred_iou_thresh=1		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									for i, o in enumerate(outputs['''masks''']       ):
													new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase       ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									    ]		,     )