




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


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







@dataclasses.dataclass(frozen=lowercase__  )
class a :
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		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'.
						UpperCamelCase_	:       str       	=		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.
						UpperCamelCase_	:       Optional[int]       	=		42  # [num_res, num_atom_type]

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

						# Chain indices for multi-chain predictions
						UpperCamelCase_	:       Dict       	=		None

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

						# Templates used to generate this protein (prediction-only)
						UpperCamelCase_	:       Optional[int]       	=		None

						# Chain corresponding to each parent
						UpperCamelCase_	:       Any       	=		None







def 		_A(       UpperCamelCase__				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    r"(\[[A-Z]+\]\n)"
							__lowercase      =    [tag.strip() for tag in re.split(_lowerCamelCase				,       _lowerCamelCase				) if len(_lowerCamelCase				) > 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(_lowerCamelCase				)				):
																												if seq[i] not in residue_constants.restypes:
																																			__lowercase      =    "X"  # FIXME: strings are immutable
																					__lowercase      =    np.array(
																					    [residue_constants.restype_order.get(_lowerCamelCase				,       residue_constants.restype_num				) for res_symbol in seq]				)
														elif "[TERTIARY]" == g[0]:
																					__lowercase      =    []
																					for axis in range(3				):
																												tertiary.append(list(map(_lowerCamelCase				,       g[1][axis].split()				)				)				)
																					__lowercase      =    np.array(_lowerCamelCase				)
																					__lowercase      =    np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
																					for i, atom in enumerate(_lowerCamelCase				):
																												__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(_lowerCamelCase				),
																					        residue_constants.atom_type_num,
																					    )				).astype(np.floataa				)
																					for i, atom in enumerate(_lowerCamelCase				):
																												__lowercase      =    1
																					atom_mask *= mask[..., None]

							assert aatype is not None

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







def 		_A(       UpperCamelCase__				,       UpperCamelCase__ = 0				)    -> str:
							'''simple docstring'''


							__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(_lowerCamelCase				,       _lowerCamelCase				) if i == chain_id]

							if parents is None or len(_lowerCamelCase				) == 0:
														__lowercase      =    ["N/A"]

							pdb_headers.append(F'PARENT {" ".join(_lowerCamelCase				)}'				)

							return pdb_headers







def 		_A(       UpperCamelCase__				,       UpperCamelCase__				)    -> Optional[int]:
							'''simple docstring'''


							__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(_lowerCamelCase				)				,       []				)
																												parent_dict[str(_lowerCamelCase				)].append(_lowerCamelCase				)

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

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

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

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

																					out_pdb_lines.append(make_parent_line(_lowerCamelCase				)				)

							return "\n".join(_lowerCamelCase				)







def 		_A(       UpperCamelCase__				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    residue_constants.restypes + ["X"]

							def res_atoa(UpperCamelCase__				) -> 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(_lowerCamelCase				)
							if len(_lowerCamelCase				) > 0:
														pdb_lines.extend(_lowerCamelCase				)

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

																					__lowercase      =    "ATOM"
																					__lowercase      =    atom_name if len(_lowerCamelCase				) == 4 else F' {atom_name}'
																					__lowercase      =    ""
																					__lowercase      =    ""
																					__lowercase      =    1.00
																					__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(_lowerCamelCase				)
																					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(_lowerCamelCase				)
																					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(_lowerCamelCase				,       _lowerCamelCase				)				)

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







def 		_A(       UpperCamelCase__				)    -> Optional[int]:
							'''simple docstring'''


							return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def 		_A(       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       )    -> Optional[int]:
							'''simple docstring'''


							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=_lowerCamelCase				,       remark=_lowerCamelCase				,       parents=_lowerCamelCase				,       parents_chain_index=_lowerCamelCase				,       )












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__				)

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






