



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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCAmelCase	:   int    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Optional[int]    =   {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'gptj'
		SCREAMING_SNAKE_CASE__								=	{
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}
		def __init__(							self    ,a_=5_0400    ,a_=2048    ,a_=4096    ,a_=28    ,a_=16    ,a_=64    ,a_=None    ,a_="gelu_new"    ,a_=0.0    ,a_=0.0    ,a_=0.0    ,a_=1e-5    ,a_=0.02    ,a_=True    ,a_=5_0256    ,a_=5_0256    ,a_=False    ,**a_    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  n_positions
						lowerCAmelCase__		  =  n_embd
						lowerCAmelCase__		  =  n_layer
						lowerCAmelCase__		  =  n_head
						lowerCAmelCase__		  =  n_inner
						lowerCAmelCase__		  =  rotary_dim
						lowerCAmelCase__		  =  activation_function
						lowerCAmelCase__		  =  resid_pdrop
						lowerCAmelCase__		  =  embd_pdrop
						lowerCAmelCase__		  =  attn_pdrop
						lowerCAmelCase__		  =  layer_norm_epsilon
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  use_cache

						lowerCAmelCase__		  =  bos_token_id
						lowerCAmelCase__		  =  eos_token_id

						super().__init__(
						    bos_token_id=a_    ,eos_token_id=a_    ,tie_word_embeddings=a_    ,**a_						)



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_    ,a_ = "default"    ,a_ = None    ,a_ = False    ,):







						"""simple docstring"""
						super().__init__(a_    ,task=a_    ,patching_specs=a_    ,use_past=a_						)
						if not getattr(self._config    ,'pad_token_id'    ,a_						):
										# TODO: how to do that better?
										lowerCAmelCase__		  =  0
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
						if self.use_past:
										self.fill_with_past_key_values_(a_    ,direction='inputs'						)
										lowerCAmelCase__		  =  {0: 'batch', 1: 'past_sequence + sequence'}
						else:
										lowerCAmelCase__		  =  {0: 'batch', 1: 'sequence'}

						return common_inputs
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self._config.n_layer
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self._config.n_head
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  super(a_    ,self						).generate_dummy_inputs(
						    a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_						)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__		  =  OrderedDict({'input_ids': common_inputs['input_ids']}						)

						# Need to add the past_keys
						if self.use_past:
										if not is_torch_available():
														raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
										else:
														import torch

														lowerCAmelCase__  ,   lowerCAmelCase__		  =  common_inputs['input_ids'].shape
														# Not using the same length for past_key_values
														lowerCAmelCase__		  =  seqlen + 2
														lowerCAmelCase__		  =  (
														    batch,
														    self.num_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // self.num_attention_heads,
														)
														lowerCAmelCase__		  =  [
														    (torch.zeros(a_						), torch.zeros(a_						)) for _ in range(self.num_layers						)
														]

						lowerCAmelCase__		  =  common_inputs['attention_mask']
						if self.use_past:
										lowerCAmelCase__		  =  ordered_inputs['attention_mask'].dtype
										lowerCAmelCase__		  =  torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(a_    ,a_    ,dtype=a_						)]    ,dim=1						)

						return ordered_inputs



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 13





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
