





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

class     __SCREAMING_SNAKE_CASE   (       enum.Enum    ):
							snake_case       : int         =    0
							snake_case       : Union[str, Any]         =    1
							snake_case       : Optional[Any]         =    2






@add_end_docstrings(__lowercase    )
class     __SCREAMING_SNAKE_CASE   (       __lowercase    ):
							snake_case       : Optional[Any]         =    "\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "






							def __init__(  self					,	*__lowerCAmelCase					,	**__lowerCAmelCase					):
														super().__init__(*_a					,	**_a					)
														self.check_model_type(
														    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING					)
														if "prefix" not in self._preprocess_params:
																					# This is very specific. The logic is quite complex and needs to be done
																					# as a "default".
																					# It also defines both some preprocess_kwargs and generate_kwargs
																					# which is why we cannot put them in their respective methods.
																					UpperCamelCase__			       =     None
																					if self.model.config.prefix is not None:
																												UpperCamelCase__			       =     self.model.config.prefix
																					if prefix is None and self.model.__class__.__name__ in [
																					    "XLNetLMHeadModel",
																					    "TransfoXLLMHeadModel",
																					    "TFXLNetLMHeadModel",
																					    "TFTransfoXLLMHeadModel",
																					]:
																												# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																												UpperCamelCase__			       =     self.XL_PREFIX
																					if prefix is not None:
																												# Recalculate some generate_kwargs linked to prefix.
																												UpperCamelCase__			       =     self._sanitize_parameters(prefix=_a					,	**self._forward_params					)
																												UpperCamelCase__			       =     {**self._preprocess_params, **preprocess_params}
																												UpperCamelCase__			       =     {**self._forward_params, **forward_params}






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	**__lowerCAmelCase					,	):
														UpperCamelCase__			       =     {}
														if prefix is not None:
																					UpperCamelCase__			       =     prefix
														if prefix:
																					UpperCamelCase__			       =     self.tokenizer(
																					    _a					,	padding=_a					,	add_special_tokens=_a					,	return_tensors=self.framework					)
																					UpperCamelCase__			       =     prefix_inputs['''input_ids'''].shape[-1]

														if handle_long_generation is not None:
																					if handle_long_generation not in {"hole"}:
																												raise ValueError(
																												    f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
																												    """ [None, \'hole\']"""					)
																					UpperCamelCase__			       =     handle_long_generation

														preprocess_params.update(_a					)
														UpperCamelCase__			       =     generate_kwargs

														UpperCamelCase__			       =     {}
														if return_full_text is not None and return_type is None:
																					if return_text is not None:
																												raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""					)
																					if return_tensors is not None:
																												raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""					)
																					UpperCamelCase__			       =     ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
														if return_tensors is not None and return_type is None:
																					if return_text is not None:
																												raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""					)
																					UpperCamelCase__			       =     ReturnType.TENSORS
														if return_type is not None:
																					UpperCamelCase__			       =     return_type
														if clean_up_tokenization_spaces is not None:
																					UpperCamelCase__			       =     clean_up_tokenization_spaces

														if stop_sequence is not None:
																					UpperCamelCase__			       =     self.tokenizer.encode(_a					,	add_special_tokens=_a					)
																					if len(_a					) > 1:
																												warnings.warn(
																												    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
																												    """ the stop sequence will be used as the stop sequence string in the interim."""					)
																					UpperCamelCase__			       =     stop_sequence_ids[0]

														return preprocess_params, forward_params, postprocess_params






							def 			_lowerCamelCase						(  self					,	*__lowerCAmelCase					,	**__lowerCAmelCase					):
														# Parse arguments
														if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																					kwargs.update({"""add_space_before_punct_symbol""": True}					)

														return super()._parse_and_tokenize(*_a					,	**_a					)






							def __call__(  self					,	__lowerCAmelCase					,	**__lowerCAmelCase					):
														return super().__call__(_a					,	**_a					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=""					,	__lowerCAmelCase=None					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     self.tokenizer(
														    prefix + prompt_text					,	padding=_a					,	add_special_tokens=_a					,	return_tensors=self.framework					)
														UpperCamelCase__			       =     prompt_text

														if handle_long_generation == "hole":
																					UpperCamelCase__			       =     inputs['''input_ids'''].shape[-1]
																					if "max_new_tokens" in generate_kwargs:
																												UpperCamelCase__			       =     generate_kwargs['''max_new_tokens''']
																					else:
																												UpperCamelCase__			       =     generate_kwargs.get("""max_length"""					,	self.model.config.max_length					) - cur_len
																												if new_tokens < 0:
																																			raise ValueError("""We cannot infer how many new tokens are expected"""					)
																					if cur_len + new_tokens > self.tokenizer.model_max_length:
																												UpperCamelCase__			       =     self.tokenizer.model_max_length - new_tokens
																												if keep_length <= 0:
																																			raise ValueError(
																																			    """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
																																			    """ models max length"""					)

																												UpperCamelCase__			       =     inputs['''input_ids'''][:, -keep_length:]
																												if "attention_mask" in inputs:
																																			UpperCamelCase__			       =     inputs['''attention_mask'''][:, -keep_length:]

														return inputs






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     model_inputs['''input_ids''']
														UpperCamelCase__			       =     model_inputs.get("""attention_mask"""					,	_a					)
														# Allow empty prompts
														if input_ids.shape[1] == 0:
																					UpperCamelCase__			       =     None
																					UpperCamelCase__			       =     None
																					UpperCamelCase__			       =     1
														else:
																					UpperCamelCase__			       =     input_ids.shape[0]
														UpperCamelCase__			       =     model_inputs.pop("""prompt_text"""					)

														# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
														# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
														UpperCamelCase__			       =     generate_kwargs.pop("""prefix_length"""					,	0					)
														if prefix_length > 0:
																					UpperCamelCase__			       =     '''max_new_tokens''' in generate_kwargs or (
																					    '''generation_config''' in generate_kwargs
																					    and generate_kwargs['''generation_config'''].max_new_tokens is not None
																					)
																					if not has_max_new_tokens:
																												UpperCamelCase__			       =     generate_kwargs.get("""max_length"""					) or self.model.config.max_length
																												generate_kwargs["max_length"] += prefix_length
																					UpperCamelCase__			       =     '''min_new_tokens''' in generate_kwargs or (
																					    '''generation_config''' in generate_kwargs
																					    and generate_kwargs['''generation_config'''].min_new_tokens is not None
																					)
																					if not has_min_new_tokens and "min_length" in generate_kwargs:
																												generate_kwargs["min_length"] += prefix_length

        # BS x SL
														UpperCamelCase__			       =     self.model.generate(input_ids=_a					,	attention_mask=_a					,	**_a					)
														UpperCamelCase__			       =     generated_sequence.shape[0]
														if self.framework == "pt":
																					UpperCamelCase__			       =     generated_sequence.reshape(_a					,	out_b // in_b					,	*generated_sequence.shape[1:]					)
														elif self.framework == "tf":
																					UpperCamelCase__			       =     tf.reshape(_a					,	(in_b, out_b // in_b, *generated_sequence.shape[1:])					)
														return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=ReturnType.FULL_TEXT					,	__lowerCAmelCase=True					):
														UpperCamelCase__			       =     model_outputs['''generated_sequence'''][0]
														UpperCamelCase__			       =     model_outputs['''input_ids''']
														UpperCamelCase__			       =     model_outputs['''prompt_text''']
														UpperCamelCase__			       =     generated_sequence.numpy().tolist()
														UpperCamelCase__			       =     []
														for sequence in generated_sequence:
																					if return_type == ReturnType.TENSORS:
																												UpperCamelCase__			       =     {'''generated_token_ids''': sequence}
																					elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																												# Decode text
																												UpperCamelCase__			       =     self.tokenizer.decode(
																												    _a					,	skip_special_tokens=_a					,	clean_up_tokenization_spaces=_a					,	)

																												# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																												if input_ids is None:
																																			UpperCamelCase__			       =     0
																												else:
																																			UpperCamelCase__			       =     len(
																																			    self.tokenizer.decode(
																																			        input_ids[0]					,	skip_special_tokens=_a					,	clean_up_tokenization_spaces=_a					,	)					)

																												if return_type == ReturnType.FULL_TEXT:
																																			UpperCamelCase__			       =     prompt_text + text[prompt_length:]
																												else:
																																			UpperCamelCase__			       =     text[prompt_length:]

																												UpperCamelCase__			       =     {'''generated_text''': all_text}
																					records.append(_a					)

														return records











from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []

							for rt in rc.restypes:
														UpperCamelCase__			       =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
														UpperCamelCase__			       =     {name: i for i, name in enumerate(a__    )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 14    )
							restype_atomaa_to_atomaa_list.append([0] * 37    )
							restype_atomaa_mask_list.append([0.0] * 14    )

							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.floataa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     protein["""aatype"""].to(torch.long    )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]

							UpperCamelCase__			       =     residx_atomaa_mask
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							UpperCamelCase__			       =     torch.zeros([21, 37]				,     dtype=torch.floataa				,     device=protein["""aatype"""].device    )
							for restype, restype_letter in enumerate(rc.restypes    ):
														UpperCamelCase__			       =     rc.restype_atoa[restype_letter]
														UpperCamelCase__			       =     rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					UpperCamelCase__			       =     rc.atom_order[atom_name]
																					UpperCamelCase__			       =     1

							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_mask

							return protein





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     tree_map(lambda a__    : torch.tensor(a__				,     device=batch["""aatype"""].device    )				,     a__				,     np.ndarray    )
							UpperCamelCase__			       =     tensor_tree_map(lambda a__    : np.array(a__    )				,     make_atomaa_masks(a__    )    )
							return out




