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							 =					0.0_1


@dataclasses.dataclass(frozen=UpperCAmelCase_ )
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						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'.
    a__	:						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.
    a__	:						np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

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







    __UpperCamelCase     :List[str]			  =		[]

    __UpperCamelCase     :Optional[int]			  =		prot.remark
    if remark is not None:
        pdb_headers.append(f"""REMARK {remark}"""	)

    __UpperCamelCase     :Optional[int]			  =		prot.parents
    __UpperCamelCase     :Union[str, Any]			  =		prot.parents_chain_index
    if parents is not None and parents_chain_index is not None:
        __UpperCamelCase     :List[Any]			  =		[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:
        __UpperCamelCase     :Tuple			  =		['''N/A''']

    pdb_headers.append(f"""PARENT {' '.join(SCREAMING_SNAKE_CASE	)}"""	)

    return pdb_headers



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :Dict			  =		pdb_str.split('''\n'''	)

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

    __UpperCamelCase     :List[List[str]]
    if prot.parents is not None and len(prot.parents	) > 0:
        __UpperCamelCase     :Dict			  =		[]
        if prot.parents_chain_index is not None:
            __UpperCamelCase     :Dict[str, List[str]]			  =		{}
            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	)

            __UpperCamelCase     :Union[str, Any]			  =		max([int(SCREAMING_SNAKE_CASE	) for chain_idx in parent_dict]	)
            for i in range(max_idx + 1	):
                __UpperCamelCase     :Union[str, Any]			  =		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:
        __UpperCamelCase     :List[Any]			  =		[['''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]	)	)

    __UpperCamelCase     :Optional[Any]			  =		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	):
                __UpperCamelCase     :List[str]			  =		parents_per_chain[chain_counter]
            else:
                __UpperCamelCase     :Optional[int]			  =		['''N/A''']

            out_pdb_lines.append(make_parent_line(SCREAMING_SNAKE_CASE	)	)

    return "\n".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		residue_constants.restypes + ['''X''']

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

    __UpperCamelCase     :Optional[int]			  =		residue_constants.atom_types

    __UpperCamelCase     :List[str]			  =		[]

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

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

    __UpperCamelCase     :int			  =		get_pdb_headers(SCREAMING_SNAKE_CASE	)
    if len(SCREAMING_SNAKE_CASE	) > 0:
        pdb_lines.extend(SCREAMING_SNAKE_CASE	)

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

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

            __UpperCamelCase     :Dict			  =		'''A'''
            if chain_index is not None:
                __UpperCamelCase     :Union[str, Any]			  =		chain_tags[chain_index[i]]

            # PDB is a columnar format, every space matters here!
            __UpperCamelCase     :str			  =		(
                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

        __UpperCamelCase     :Dict			  =		i == n - 1
        if chain_index is not None:
            if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                __UpperCamelCase     :int			  =		True
                __UpperCamelCase     :List[str]			  =		chain_index[i + 1]

        if should_terminate:
            # Close the chain.
            __UpperCamelCase     :Optional[Any]			  =		'''TER'''
            __UpperCamelCase     :Optional[int]			  =		(
                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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return residue_constants.STANDARD_ATOM_MASK[prot.aatype]



def lowerCamelCase  (   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  ,			):
    '''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=SCREAMING_SNAKE_CASE  ,			remark=SCREAMING_SNAKE_CASE  ,			parents=SCREAMING_SNAKE_CASE  ,			parents_chain_index=SCREAMING_SNAKE_CASE  ,			)






import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
    from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__lowercase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

        # From MELGAN
        __UpperCamelCase     :int			  =		math.log(1E-5)  # Matches MelGAN training.
        __UpperCamelCase     :int			  =		4.0  # Largest value for most examples
        __UpperCamelCase     :str			  =		128

        self.register_modules(
            notes_encoder=__lowercase , continuous_encoder=__lowercase , decoder=__lowercase , scheduler=__lowercase , melgan=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(features - self.min_value) / (self.max_value - self.min_value)
        # Scale to [min_out, max_out].
        return zero_one * (max_out - min_out) + min_out







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(outputs - min_out) / (max_out - min_out)
        # Scale to [self.min_value, self.max_value].
        return zero_one * (self.max_value - self.min_value) + self.min_value







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

        return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
            __UpperCamelCase     :Dict			  =		timesteps[None].to(input_tokens.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCamelCase     :List[str]			  =		timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, 0, self.n_dims] , np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)

        for i, encoder_input_tokens in enumerate(__lowercase):
            if i == 0:
                __UpperCamelCase     :int			  =		torch.from_numpy(pred_mel[:1].copy()).to(
                    device=self.device , dtype=self.decoder.dtype)
                # The first chunk has no previous context.
                __UpperCamelCase     :int			  =		torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)
            else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                __UpperCamelCase     :Tuple			  =		ones

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

            __UpperCamelCase     :int			  =		self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            __UpperCamelCase     :int			  =		randn_tensor(
                shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )

            # set step values
            self.scheduler.set_timesteps(__lowercase)

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
                __UpperCamelCase     :Optional[int]			  =		self.decode(
                    encodings_and_masks=__lowercase , input_tokens=__lowercase , noise_time=t / self.scheduler.config.num_train_timesteps , )

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		mel.cpu().float().numpy()

            __UpperCamelCase     :Any			  =		np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        if output_type == "numpy" and not is_onnx_available():
            raise ValueError(
                '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
        elif output_type == "numpy" and self.melgan is None:
            raise ValueError(
                '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')

        if output_type == "numpy":
            __UpperCamelCase     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)





