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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = 42





class UpperCAmelCase						( __A ,	__A  ):


				'''simple docstring'''






				@register_to_config
				def __init__(						self		,     lowercase = 1_6		,     lowercase = 8_8		,     lowercase = None		,     lowercase = None		,     lowercase = 1		,     lowercase = 0.0		,     lowercase = 3_2		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = "geglu"		,     lowercase = True		,     lowercase = True		,     ):



						"""simple docstring"""





						super().__init__()
						A_		:    List[str]         =				num_attention_heads
						A_		:    List[str]         =				attention_head_dim
						A_		:    List[str]         =				num_attention_heads * attention_head_dim

						A_		:    str         =				in_channels

						A_		:    int         =				torch.nn.GroupNorm(num_groups=lowercase		,     num_channels=lowercase		,     eps=1E-6		,     affine=lowercase )
						A_		:    List[Any]         =				nn.Linear(lowercase		,     lowercase )

						# 3. Define transformers blocks
						A_		:    List[Any]         =				nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            lowercase		,     lowercase		,     lowercase		,     dropout=lowercase		,     cross_attention_dim=lowercase		,     activation_fn=lowercase		,     attention_bias=lowercase		,     double_self_attention=lowercase		,     norm_elementwise_affine=lowercase		,     )
						        for d in range(lowercase )
						    ] )

						A_		:    Dict         =				nn.Linear(lowercase		,     lowercase )







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     lowercase=1		,     lowercase=None		,     lowercase = True		,     ):



						"""simple docstring"""





						A_       ,      A_       ,      A_       ,      A_		:    Optional[Any]         =				hidden_states.shape
						A_		:    Union[str, Any]         =				batch_frames // num_frames

						A_		:    List[Any]         =				hidden_states

						A_		:    Tuple         =				hidden_states[None, :].reshape(lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
						A_		:    List[str]         =				hidden_states.permute(0		,     2		,     1		,     3		,     4 )

						A_		:    Optional[int]         =				self.norm(lowercase )
						A_		:    Any         =				hidden_states.permute(0		,     3		,     4		,     2		,     1 ).reshape(batch_size * height * width		,     lowercase		,     lowercase )

						A_		:    str         =				self.proj_in(lowercase )

						# 2. Blocks
						for block in self.transformer_blocks:
								A_		:    Union[str, Any]         =				block(
								    lowercase		,     encoder_hidden_states=lowercase		,     timestep=lowercase		,     cross_attention_kwargs=lowercase		,     class_labels=lowercase		,     )

						# 3. Output
						A_		:    Dict         =				self.proj_out(lowercase )
						A_		:    int         =				(
						    hidden_states[None, None, :]
						    .reshape(lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
						    .permute(0		,     3		,     4		,     1		,     2 )
						    .contiguous()
						)
						A_		:    Optional[Any]         =				hidden_states.reshape(lowercase		,     lowercase		,     lowercase		,     lowercase )

						A_		:    Union[str, Any]         =				hidden_states + residual

						if not return_dict:
								return (output,)

						return TransformerTemporalModelOutput(sample=lowercase )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase            =		{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






