

from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]


							@register_to_config
							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = 5_0_2_5_7       ,							_a = 1_0_2_4       ,							_a = 7_6_8       ,							_a = 1_2       ,							_a = 1_2       ,							_a = None       ,							_a = "gelu_new"       ,							_a = 0.1       ,							_a = 0.1       ,							_a = 0.1       ,							_a = 1e-5       ,							_a = 0.02       ,							_a = True       ,							_a = True       ,							_a = False       ,							_a = False       ,							)    ->    str:
												super().__init__()

												_a	:    int         =		prefix_length

												if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																	raise ValueError(
																	    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																	    F""" `n_embd`: {n_embd} are not equal."""       )

												_a	:    List[str]         =		prefix_inner_dim
												_a	:    int         =		prefix_hidden_dim

												_a	:    Any         =		(
												    nn.Linear(self.prefix_inner_dim       ,							self.prefix_hidden_dim       )
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												_a	:    Optional[int]         =		(
												    nn.Linear(self.prefix_hidden_dim       ,							_a       ) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												_a	:    Any         =		GPTaConfig(
												    vocab_size=_a       ,							n_positions=_a       ,							n_embd=_a       ,							n_layer=_a       ,							n_head=_a       ,							n_inner=_a       ,							activation_function=_a       ,							resid_pdrop=_a       ,							embd_pdrop=_a       ,							attn_pdrop=_a       ,							layer_norm_epsilon=_a       ,							initializer_range=_a       ,							scale_attn_weights=_a       ,							use_cache=_a       ,							scale_attn_by_inverse_layer_idx=_a       ,							reorder_and_upcast_attn=_a       ,							)
												_a	:    Union[str, Any]         =		GPTaLMHeadModel(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = None       ,							)    ->    Union[str, Any]:
												_a	:    List[Any]         =		self.transformer.transformer.wte(_a       )
												_a	:    Optional[Any]         =		self.encode_prefix(_a       )
												_a	:    Any         =		self.decode_prefix(_a       )
												_a	:    List[str]         =		torch.cat((prefix_embeds, embedding_text)       ,							dim=1       )

												if labels is not None:
																	_a	:    Dict         =		self.get_dummy_token(input_ids.shape[0]       ,							input_ids.device       )
																	_a	:    List[Any]         =		torch.cat((dummy_token, input_ids)       ,							dim=1       )
												_a	:    Union[str, Any]         =		self.transformer(inputs_embeds=_a       ,							labels=_a       ,							attention_mask=_a       )
												if self.prefix_hidden_dim is not None:
																	return out, hidden
												else:
																	return out


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    torch.Tensor:
												return torch.zeros(_a       ,							self.prefix_length       ,							dtype=torch.intaa       ,							device=_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[int]:
												return self.encode_prefix(_a       )


							@torch.no_grad()
							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    List[str]         =		torch.split(_a       ,							1       ,							dim=0       )
												_a	:    Optional[int]         =		[]
												_a	:    Optional[Any]         =		[]
												for feature in features:
																	_a	:    int         =		self.decode_prefix(feature.to(_a       )       )  # back to the clip feature
																	# Only support beam search for now
																	_a      ,					_a	:    int         =		self.generate_beam(
																	    input_embeds=_a       ,							device=_a       ,							eos_token_id=_a       )
																	generated_tokens.append(output_tokens[0]       )
																	generated_seq_lengths.append(seq_lengths[0]       )
												_a	:    Any         =		torch.stack(_a       )
												_a	:    Any         =		torch.stack(_a       )
												return generated_tokens, generated_seq_lengths




							@torch.no_grad()
							def 				__lowercase				(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							_a = 5       ,							_a = 6_7       ,							_a = 1.0       ,							_a = None       ,							)    ->    Dict:
												_a	:    List[Any]         =		eos_token_id
												_a	:    str         =		None
												_a	:    Optional[int]         =		None
												_a	:    int         =		torch.ones(_a       ,							device=_a       ,							dtype=torch.int       )
												_a	:    int         =		torch.zeros(_a       ,							device=_a       ,							dtype=torch.bool       )

												if input_embeds is not None:
																	_a	:    Optional[Any]         =		input_embeds
												else:
																	_a	:    Dict         =		self.transformer.transformer.wte(_a       )

												for i in range(_a       ):
																	_a	:    Optional[int]         =		self.transformer(inputs_embeds=_a       )
																	_a	:    Optional[Any]         =		outputs.logits
																	_a	:    str         =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																	_a	:    str         =		logits.softmax(-1       ).log()

																	if scores is None:
																						_a      ,					_a	:    str         =		logits.topk(_a       ,							-1       )
																						_a	:    Union[str, Any]         =		generated.expand(_a       ,							*generated.shape[1:]       )
																						_a      ,					_a	:    Tuple         =		next_tokens.permute(1       ,							0       ), scores.squeeze(0       )
																						if tokens is None:
																											_a	:    str         =		next_tokens
																						else:
																											_a	:    Union[str, Any]         =		tokens.expand(_a       ,							*tokens.shape[1:]       )
																											_a	:    Tuple         =		torch.cat((tokens, next_tokens)       ,							dim=1       )
																	else:
																						_a	:    int         =		-float(np.inf       )
																						_a	:    Dict         =		0
																						_a	:    Any         =		scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						_a	:    Any         =		scores_sum / seq_lengths[:, None]
																						_a      ,					_a	:    Optional[Any]         =		scores_sum_average.view(-1       ).topk(_a       ,							-1       )
																						_a	:    Dict         =		next_tokens // scores_sum.shape[1]
																						_a	:    Optional[Any]         =		seq_lengths[next_tokens_source]
																						_a	:    Tuple         =		next_tokens % scores_sum.shape[1]
																						_a	:    Optional[int]         =		next_tokens.unsqueeze(1       )
																						_a	:    Optional[Any]         =		tokens[next_tokens_source]
																						_a	:    Tuple         =		torch.cat((tokens, next_tokens)       ,							dim=1       )
																						_a	:    List[Any]         =		generated[next_tokens_source]
																						_a	:    Union[str, Any]         =		scores_sum_average * seq_lengths
																						_a	:    List[str]         =		is_stopped[next_tokens_source]

																	_a	:    Tuple         =		self.transformer.transformer.wte(next_tokens.squeeze()       ).view(generated.shape[0]       ,							1       ,							-1       )
																	_a	:    Optional[int]         =		torch.cat((generated, next_token_embed)       ,							dim=1       )
																	_a	:    Optional[int]         =		is_stopped + next_tokens.eq(_a       ).squeeze()
																	if is_stopped.all():
																						break

												_a	:    List[Any]         =		scores / seq_lengths
												_a	:    Optional[Any]         =		scores.argsort(descending=_a       )
												# tokens tensors are already padded to max_seq_length
												_a	:    List[Any]         =		[tokens[i] for i in order]
												_a	:    List[str]         =		torch.stack(_a       ,							dim=0       )
												_a	:    Any         =		torch.tensor([seq_lengths[i] for i in order]       ,							dtype=seq_lengths.dtype       )
												return output_texts, seq_lengths






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



