





"""simple docstring"""
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_ ):
					UpperCamelCase      =							[R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']


					@register_to_config
					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Optional[int] = None     ,       __UpperCamelCase			:int = 5_02_57     ,       __UpperCamelCase			:int = 10_24     ,       __UpperCamelCase			:int = 7_68     ,       __UpperCamelCase			:int = 12     ,       __UpperCamelCase			:int = 12     ,       __UpperCamelCase			:Optional[int] = None     ,       __UpperCamelCase			:str = "gelu_new"     ,       __UpperCamelCase			:float = 0.1     ,       __UpperCamelCase			:float = 0.1     ,       __UpperCamelCase			:float = 0.1     ,       __UpperCamelCase			:float = 1e-5     ,       __UpperCamelCase			:float = 0.02     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       ):
												super().__init__()

												A		=  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		=  prefix_inner_dim
												A		=  prefix_hidden_dim

												A		=  (
												    nn.Linear(self.prefix_inner_dim     ,       self.prefix_hidden_dim		)
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												A		=  (
												    nn.Linear(self.prefix_hidden_dim     ,       __UpperCamelCase		) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												A		=  GPTaConfig(
												    vocab_size=__UpperCamelCase     ,       n_positions=__UpperCamelCase     ,       n_embd=__UpperCamelCase     ,       n_layer=__UpperCamelCase     ,       n_head=__UpperCamelCase     ,       n_inner=__UpperCamelCase     ,       activation_function=__UpperCamelCase     ,       resid_pdrop=__UpperCamelCase     ,       embd_pdrop=__UpperCamelCase     ,       attn_pdrop=__UpperCamelCase     ,       layer_norm_epsilon=__UpperCamelCase     ,       initializer_range=__UpperCamelCase     ,       scale_attn_weights=__UpperCamelCase     ,       use_cache=__UpperCamelCase     ,       scale_attn_by_inverse_layer_idx=__UpperCamelCase     ,       reorder_and_upcast_attn=__UpperCamelCase     ,       )
												A		=  GPTaLMHeadModel(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       ):
												A		=  self.transformer.transformer.wte(__UpperCamelCase		)
												A		=  self.encode_prefix(__UpperCamelCase		)
												A		=  self.decode_prefix(__UpperCamelCase		)
												A		=  torch.cat((prefix_embeds, embedding_text)     ,       dim=1		)

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


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:torch.device		):
												return torch.zeros(__UpperCamelCase     ,       self.prefix_length     ,       dtype=torch.intaa     ,       device=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Any		):
												return self.encode_prefix(__UpperCamelCase		)


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


					@torch.no_grad()
					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:Any=None     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:int = 5     ,       __UpperCamelCase			:int = 67     ,       __UpperCamelCase			:float = 1.0     ,       __UpperCamelCase			:Optional[int] = None     ,       ):
												A		=  eos_token_id
												A		=  None
												A		=  None
												A		=  torch.ones(__UpperCamelCase     ,       device=__UpperCamelCase     ,       dtype=torch.int		)
												A		=  torch.zeros(__UpperCamelCase     ,       device=__UpperCamelCase     ,       dtype=torch.bool		)

												if input_embeds is not None:
																			A		=  input_embeds
												else:
																			A		=  self.transformer.transformer.wte(__UpperCamelCase		)

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

																			if scores is None:
																										A,	A		=  logits.topk(__UpperCamelCase     ,       -1		)
																										A		=  generated.expand(__UpperCamelCase     ,       *generated.shape[1:]		)
																										A,	A		=  next_tokens.permute(1     ,       0		), scores.squeeze(0		)
																										if tokens is None:
																																	A		=  next_tokens
																										else:
																																	A		=  tokens.expand(__UpperCamelCase     ,       *tokens.shape[1:]		)
																																	A		=  torch.cat((tokens, next_tokens)     ,       dim=1		)
																			else:
																										A		=  -float(np.inf		)
																										A		=  0
																										A		=  scores[:, None] + logits
																										seq_lengths[~is_stopped] += 1
																										A		=  scores_sum / seq_lengths[:, None]
																										A,	A		=  scores_sum_average.view(-1		).topk(__UpperCamelCase     ,       -1		)
																										A		=  next_tokens // scores_sum.shape[1]
																										A		=  seq_lengths[next_tokens_source]
																										A		=  next_tokens % scores_sum.shape[1]
																										A		=  next_tokens.unsqueeze(1		)
																										A		=  tokens[next_tokens_source]
																										A		=  torch.cat((tokens, next_tokens)     ,       dim=1		)
																										A		=  generated[next_tokens_source]
																										A		=  scores_sum_average * seq_lengths
																										A		=  is_stopped[next_tokens_source]

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

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









"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format






class 					_UpperCAmelCase   :
					UpperCamelCase      =							None


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)
												A		=  json.loads(feat_extract.to_json_string()		)
												for key, value in self.feat_extract_dict.items():
																			self.assertEqual(obj[key]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  os.path.join(__UpperCamelCase     ,       "feat_extract.json"		)
																			feat_extract_first.to_json_file(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_json_file(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			A		=  feat_extract_first.save_pretrained(__UpperCamelCase		)[0]
																			check_json_file_has_correct_format(__UpperCamelCase		)
																			A		=  self.feature_extraction_class.from_pretrained(__UpperCamelCase		)

												self.assertEqual(feat_extract_second.to_dict()     ,       feat_extract_first.to_dict()		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  self.feature_extraction_class()
												self.assertIsNotNone(__UpperCamelCase		)


