



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_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			int      =  [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']






						@register_to_config
						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = 50_257				,							_A = 1_024				,							_A = 768				,							_A = 12				,							_A = 12				,							_A = None				,							_A = "gelu_new"				,							_A = 0.1				,							_A = 0.1				,							_A = 0.1				,							_A = 1e-5				,							_A = 0.0_2				,							_A = True				,							_A = True				,							_A = False				,							_A = False				,							):







													'''simple docstring'''

													super().__init__()

													__SCREAMING_SNAKE_CASE									= 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."""		)

													__SCREAMING_SNAKE_CASE									= prefix_inner_dim
													__SCREAMING_SNAKE_CASE									= prefix_hidden_dim

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

													__SCREAMING_SNAKE_CASE									= 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				,							)
													__SCREAMING_SNAKE_CASE									= GPTaLMHeadModel(_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A = None				,							_A = None				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.transformer.transformer.wte(_A		)
													__SCREAMING_SNAKE_CASE									= self.encode_prefix(_A		)
													__SCREAMING_SNAKE_CASE									= self.decode_prefix(_A		)
													__SCREAMING_SNAKE_CASE									= torch.cat((prefix_embeds, embedding_text)				,							dim=1		)

													if labels is not None:
																				__SCREAMING_SNAKE_CASE									= self.get_dummy_token(input_ids.shape[0]				,							input_ids.device		)
																				__SCREAMING_SNAKE_CASE									= torch.cat((dummy_token, input_ids)				,							dim=1		)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													return torch.zeros(_A				,							self.prefix_length				,							dtype=torch.intaa				,							device=_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.encode_prefix(_A		)






						@torch.no_grad()
						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.split(_A				,							1				,							dim=0		)
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []
													for feature in features:
																				__SCREAMING_SNAKE_CASE									= self.decode_prefix(feature.to(_A		)		)  # back to the clip feature
																				# Only support beam search for now
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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]		)
													__SCREAMING_SNAKE_CASE									= torch.stack(_A		)
													__SCREAMING_SNAKE_CASE									= torch.stack(_A		)
													return generated_tokens, generated_seq_lengths






						@torch.no_grad()
						def 	_A     (   self				,							_A=None				,							_A=None				,							_A=None				,							_A = 5				,							_A = 67				,							_A = 1.0				,							_A = None				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= eos_token_id
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= torch.ones(_A				,							device=_A				,							dtype=torch.int		)
													__SCREAMING_SNAKE_CASE									= torch.zeros(_A				,							device=_A				,							dtype=torch.bool		)

													if input_embeds is not None:
																				__SCREAMING_SNAKE_CASE									= input_embeds
													else:
																				__SCREAMING_SNAKE_CASE									= self.transformer.transformer.wte(_A		)

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

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

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

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__		:					str							={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ViTMAEForPreTraining''',
			    '''ViTMAELayer''',
			    '''ViTMAEModel''',
			    '''ViTMAEPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TFViTMAEForPreTraining''',
			    '''TFViTMAEModel''',
			    '''TFViTMAEPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_mae import (
						    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMAEForPreTraining,
						    ViTMAELayer,
						    ViTMAEModel,
						    ViTMAEPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					Optional[int]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






