






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_  (		__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase		):
     __lowerCamelCase      				=  [R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']




     @register_to_config
     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 50257	,						_lowerCAmelCase = 1024	,						_lowerCAmelCase = 768	,						_lowerCAmelCase = 12	,						_lowerCAmelCase = 12	,						_lowerCAmelCase = None	,						_lowerCAmelCase = "gelu_new"	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 1e-5	,						_lowerCAmelCase = 0.0_2	,						_lowerCAmelCase = True	,						_lowerCAmelCase = True	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						):
          super().__init__()

          UpperCAmelCase__      :  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."		)

          UpperCAmelCase__      :  Optional[Any]				      =			prefix_inner_dim
          UpperCAmelCase__      :  Union[str, Any]				      =			prefix_hidden_dim

          UpperCAmelCase__      :  List[Any]				      =			(
              nn.Linear(self.prefix_inner_dim	,						self.prefix_hidden_dim		)
              if self.prefix_hidden_dim is not None
              else nn.Identity()
          )
          UpperCAmelCase__      :  str				      =			(
              nn.Linear(self.prefix_hidden_dim	,						_lowerCAmelCase		) if self.prefix_hidden_dim is not None else nn.Identity()
          )

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  Optional[Any]				      =			self.transformer.transformer.wte(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			self.encode_prefix(_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			self.decode_prefix(_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			torch.cat((prefix_embeds, embedding_text)	,						dim=1		)

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return torch.zeros(_lowerCAmelCase	,						self.prefix_length	,						dtype=torch.intaa	,						device=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return self.encode_prefix(_lowerCAmelCase		)




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




     @torch.no_grad()
     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase = 5	,						_lowerCAmelCase = 67	,						_lowerCAmelCase = 1.0	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			eos_token_id
          UpperCAmelCase__      :  Any				      =			None
          UpperCAmelCase__      :  List[str]				      =			None
          UpperCAmelCase__      :  Dict				      =			torch.ones(_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=torch.int		)
          UpperCAmelCase__      :  str				      =			torch.zeros(_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=torch.bool		)

          if input_embeds is not None:
               UpperCAmelCase__      :  Union[str, Any]				      =			input_embeds
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			self.transformer.transformer.wte(_lowerCAmelCase		)

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

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

               UpperCAmelCase__      :  str				      =			self.transformer.transformer.wte(next_tokens.squeeze()		).view(generated.shape[0]	,						1	,						-1		)
               UpperCAmelCase__      :  Union[str, Any]				      =			torch.cat((generated, next_token_embed)	,						dim=1		)
               UpperCAmelCase__      :  List[str]				      =			is_stopped + next_tokens.eq(_lowerCAmelCase		).squeeze()
               if is_stopped.all():
                    break

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










from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase__    :Dict       =				logging.get_logger(__name__)

lowerCAmelCase__    :Optional[int]       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      'perceiver'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=26     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="kv"     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=262     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=56     ,       _SCREAMING_SNAKE_CASE=[368, 496]     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=1920     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=[1, 16, 224, 224]     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     num_latents
									_UpperCAmelCase											=     d_latents
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     num_blocks
									_UpperCAmelCase											=     num_self_attends_per_block
									_UpperCAmelCase											=     num_self_attention_heads
									_UpperCAmelCase											=     num_cross_attention_heads
									_UpperCAmelCase											=     qk_channels
									_UpperCAmelCase											=     v_channels
									_UpperCAmelCase											=     cross_attention_shape_for_attention
									_UpperCAmelCase											=     self_attention_widening_factor
									_UpperCAmelCase											=     cross_attention_widening_factor
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     use_query_residual
									# masked language modeling attributes
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									# image classification attributes
									_UpperCAmelCase											=     image_size
									# flow attributes
									_UpperCAmelCase											=     train_size
									# multimodal autoencoding attributes
									_UpperCAmelCase											=     num_frames
									_UpperCAmelCase											=     audio_samples_per_frame
									_UpperCAmelCase											=     samples_per_patch
									_UpperCAmelCase											=     output_shape



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('inputs', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
		@property
		def       UpperCAmelCase__							(     self		)					->			float:




									"""simple docstring"""





									return 1e-4






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 3     ,       _SCREAMING_SNAKE_CASE = 40     ,       _SCREAMING_SNAKE_CASE = 40     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch     ,       num_token_to_add=0		)
																# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																_UpperCAmelCase											=     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)
																# Generate dummy inputs according to compute batch and sequence
																_UpperCAmelCase											=     [' '.join(['a']		) * seq_length] * batch_size
																_UpperCAmelCase											=     dict(preprocessor(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('input_ids'		)
																return inputs
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) and preprocessor.model_input_names[0] == "pixel_values":
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch		)
																_UpperCAmelCase											=     self._generate_dummy_images(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     dict(preprocessor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('pixel_values'		)
																return inputs
									else:
																raise ValueError(
																    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'		)
