
'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""t5"""
		lowerCamelCase     							=						["""past_key_values"""]
		lowerCamelCase     							=						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



		def __init__(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]=3_2128  ,	UpperCamelCase__				:			Optional[Any]=512  ,	UpperCamelCase__				:			Optional[Any]=64  ,	UpperCamelCase__				:			int=2048  ,	UpperCamelCase__				:			Dict=6  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			List[Any]=8  ,	UpperCamelCase__				:			List[Any]=32  ,	UpperCamelCase__				:			Any=128  ,	UpperCamelCase__				:			List[Any]=0.1  ,	UpperCamelCase__				:			Optional[int]=1e-6  ,	UpperCamelCase__				:			Union[str, Any]=1.0  ,	UpperCamelCase__				:			Optional[Any]="relu"  ,	UpperCamelCase__				:			str=True  ,	UpperCamelCase__				:			Optional[int]=True  ,	UpperCamelCase__				:			Optional[int]=0  ,	UpperCamelCase__				:			str=1  ,	**UpperCamelCase__				:			Any  ,	)						->			Tuple:






				"""simple docstring"""







				snake_case  :       str     			=						vocab_size
				snake_case  :       List[Any]     			=						d_model
				snake_case  :       List[Any]     			=						d_kv
				snake_case  :       List[Any]     			=						d_ff
				snake_case  :       Union[str, Any]     			=						num_layers
				snake_case  :       int     			=						(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				snake_case  :       Tuple     			=						num_heads
				snake_case  :       Union[str, Any]     			=						relative_attention_num_buckets
				snake_case  :       Optional[int]     			=						relative_attention_max_distance
				snake_case  :       Any     			=						dropout_rate
				snake_case  :       Optional[int]     			=						layer_norm_epsilon
				snake_case  :       str     			=						initializer_factor
				snake_case  :       Optional[Any]     			=						feed_forward_proj
				snake_case  :       str     			=						use_cache

				snake_case  :       Dict     			=						self.feed_forward_proj.split('''-'''      )
				snake_case  :       Optional[int]     			=						act_info[-1]
				snake_case  :       Any     			=						act_info[0] == '''gated'''

				if len(UpperCamelCase__      ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__      ) > 2:
						raise ValueError(
						    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
						    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
						    '''\'gated-gelu\' or \'relu\''''      )

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
						snake_case  :       Any     			=						'''gelu_new'''

				super().__init__(
				    pad_token_id=UpperCamelCase__  ,	eos_token_id=UpperCamelCase__  ,	is_encoder_decoder=UpperCamelCase__  ,	**UpperCamelCase__  ,	)







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
				    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
				}
				if self.use_past:
						snake_case  :       Union[str, Any]     			=						'''past_encoder_sequence + sequence'''
						snake_case  :       int     			=						{0: '''batch'''}
						snake_case  :       Any     			=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
				else:
						snake_case  :       int     			=						{0: '''batch''', 1: '''decoder_sequence'''}
						snake_case  :       str     			=						{0: '''batch''', 1: '''decoder_sequence'''}

				if self.use_past:
						self.fill_with_past_key_values_(UpperCamelCase__  ,	direction='''inputs'''      )

				return common_inputs



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			int:






				"""simple docstring"""







				return 13




'''simple docstring'''
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					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

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

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


