
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  lowercase__(				UpperCAmelCase ,						UpperCAmelCase ,						UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	[r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
	@register_to_config
	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			int = 5_0_2_5_7       ,							SCREAMING_SNAKE_CASE_  :			int = 1_0_2_4       ,							SCREAMING_SNAKE_CASE_  :			int = 7_6_8       ,							SCREAMING_SNAKE_CASE_  :			int = 1_2       ,							SCREAMING_SNAKE_CASE_  :			int = 1_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			str = "gelu_new"       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 1e-5       ,							SCREAMING_SNAKE_CASE_  :			float = 0.02       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							)       ->     List[str]:
					super().__init__()

					lowercase_       =					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.'''       )

					lowercase_       =					prefix_inner_dim
					lowercase_       =					prefix_hidden_dim

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

					lowercase_       =					GPTaConfig(
					    vocab_size=SCREAMING_SNAKE_CASE_       ,							n_positions=SCREAMING_SNAKE_CASE_       ,							n_embd=SCREAMING_SNAKE_CASE_       ,							n_layer=SCREAMING_SNAKE_CASE_       ,							n_head=SCREAMING_SNAKE_CASE_       ,							n_inner=SCREAMING_SNAKE_CASE_       ,							activation_function=SCREAMING_SNAKE_CASE_       ,							resid_pdrop=SCREAMING_SNAKE_CASE_       ,							embd_pdrop=SCREAMING_SNAKE_CASE_       ,							attn_pdrop=SCREAMING_SNAKE_CASE_       ,							layer_norm_epsilon=SCREAMING_SNAKE_CASE_       ,							initializer_range=SCREAMING_SNAKE_CASE_       ,							scale_attn_weights=SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       ,							scale_attn_by_inverse_layer_idx=SCREAMING_SNAKE_CASE_       ,							reorder_and_upcast_attn=SCREAMING_SNAKE_CASE_       ,							)
					lowercase_       =					GPTaLMHeadModel(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			torch.Tensor       ,							SCREAMING_SNAKE_CASE_  :			torch.Tensor       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.Tensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.Tensor] = None       ,							)       ->     Union[str, Any]:
					lowercase_       =					self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.encode_prefix(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.decode_prefix(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					torch.cat((prefix_embeds, embedding_text)       ,							dim=1       )

					if labels is not None:
									lowercase_       =					self.get_dummy_token(input_ids.shape[0]       ,							input_ids.device       )
									lowercase_       =					torch.cat((dummy_token, input_ids)       ,							dim=1       )
					lowercase_       =					self.transformer(inputs_embeds=SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_       )
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			torch.device       )       ->     torch.Tensor:
					return torch.zeros(SCREAMING_SNAKE_CASE_       ,							self.prefix_length       ,							dtype=torch.intaa       ,							device=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     List[str]:
					return self.encode_prefix(SCREAMING_SNAKE_CASE_       )
	@torch.no_grad()
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					lowercase_       =					torch.split(SCREAMING_SNAKE_CASE_       ,							1       ,							dim=0       )
					lowercase_       =					[]
					lowercase_       =					[]
					for feature in features:
									lowercase_       =					self.decode_prefix(feature.to(SCREAMING_SNAKE_CASE_       )       )  # back to the clip feature
									# Only support beam search for now
									lowercase_  ,	lowercase_       =					self.generate_beam(
									    input_embeds=SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )
									generated_tokens.append(output_tokens[0]       )
									generated_seq_lengths.append(seq_lengths[0]       )
					lowercase_       =					torch.stack(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					torch.stack(SCREAMING_SNAKE_CASE_       )
					return generated_tokens, generated_seq_lengths




	@torch.no_grad()
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=None       ,							SCREAMING_SNAKE_CASE_  :			int = 5       ,							SCREAMING_SNAKE_CASE_  :			int = 6_7       ,							SCREAMING_SNAKE_CASE_  :			float = 1.0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							)       ->     List[str]:
					lowercase_       =					eos_token_id
					lowercase_       =					None
					lowercase_       =					None
					lowercase_       =					torch.ones(SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							dtype=torch.int       )
					lowercase_       =					torch.zeros(SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							dtype=torch.bool       )

					if input_embeds is not None:
									lowercase_       =					input_embeds
					else:
									lowercase_       =					self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_       )

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

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

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

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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




