
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				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




 '''simple docstring'''





 snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






 @register_to_config
 def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			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							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
     )

     __A             =			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             =			GPTaLMHeadModel(A			)






 def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
     __A             =			self.transformer.transformer.wte(A			)
     __A             =			self.encode_prefix(A			)
     __A             =			self.decode_prefix(A			)
     __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=A							,labels=A							,attention_mask=A			)
     if self.prefix_hidden_dim is not None:
         return out, hidden
     else:
         return out






 def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
     return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






 def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
     return self.encode_prefix(A			)






 @torch.no_grad()
 def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
     __A             =			torch.split(A							,1							,dim=0			)
     __A             =			[]
     __A             =			[]
     for feature in features:
         __A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
         # Only support beam search for now
         __A      ,							__A             =			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             =			torch.stack(A			)
     __A             =			torch.stack(A			)
     return generated_tokens, generated_seq_lengths







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

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

     for i in range(A			):
         __A             =			self.transformer(inputs_embeds=A			)
         __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(A							,-1			)
             __A             =			generated.expand(A							,*generated.shape[1:]			)
             __A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
             if tokens is None:
                 __A             =			next_tokens
             else:
                 __A             =			tokens.expand(A							,*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(A							,-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(A			).squeeze()
         if is_stopped.all():
             break

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


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
