
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      int=1       )  ->    Tuple:
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("""."""       )[n_shave_prefix_segments:]       )
							else:
														return ".".join(path.split("""."""       )[:n_shave_prefix_segments]       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Dict=0       )  ->    Dict:
							lowerCamelCase_							  =					[]
							for old_item in old_list:
														lowerCamelCase_							  =					old_item.replace("""in_layers.0"""			,				"""norm1"""       )
														lowerCamelCase_							  =					new_item.replace("""in_layers.2"""			,				"""conv1"""       )

														lowerCamelCase_							  =					new_item.replace("""out_layers.0"""			,				"""norm2"""       )
														lowerCamelCase_							  =					new_item.replace("""out_layers.3"""			,				"""conv2"""       )

														lowerCamelCase_							  =					new_item.replace("""emb_layers.1"""			,				"""time_emb_proj"""       )
														lowerCamelCase_							  =					new_item.replace("""skip_connection"""			,				"""conv_shortcut"""       )

														lowerCamelCase_							  =					shave_segments(UpperCAmelCase__			,				n_shave_prefix_segments=UpperCAmelCase__       )

														mapping.append({"""old""": old_item, """new""": new_item}       )

							return mapping



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      List[Any]=0       )  ->    List[str]:
							lowerCamelCase_							  =					[]
							for old_item in old_list:
														lowerCamelCase_							  =					old_item

														lowerCamelCase_							  =					new_item.replace("""norm.weight"""			,				"""group_norm.weight"""       )
														lowerCamelCase_							  =					new_item.replace("""norm.bias"""			,				"""group_norm.bias"""       )

														lowerCamelCase_							  =					new_item.replace("""proj_out.weight"""			,				"""proj_attn.weight"""       )
														lowerCamelCase_							  =					new_item.replace("""proj_out.bias"""			,				"""proj_attn.bias"""       )

														lowerCamelCase_							  =					shave_segments(UpperCAmelCase__			,				n_shave_prefix_segments=UpperCAmelCase__       )

														mapping.append({"""old""": old_item, """new""": new_item}       )

							return mapping



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      List[Any]=None			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      Union[str, Any]=None       )  ->    Any:
							assert isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

							# Splits the attention layers into three variables.
							if attention_paths_to_split is not None:
														for path, path_map in attention_paths_to_split.items():
																					lowerCamelCase_							  =					old_checkpoint[path]
																					lowerCamelCase_							  =					old_tensor.shape[0] // 3

																					lowerCamelCase_							  =					(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

																					lowerCamelCase_							  =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

																					lowerCamelCase_							  =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
																					lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					old_tensor.split(channels // num_heads			,				dim=1       )

																					lowerCamelCase_							  =					query.reshape(UpperCAmelCase__       )
																					lowerCamelCase_							  =					key.reshape(UpperCAmelCase__       )
																					lowerCamelCase_							  =					value.reshape(UpperCAmelCase__       )

							for path in paths:
														lowerCamelCase_							  =					path["""new"""]

														# These have already been assigned
														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														# Global renaming happens here
														lowerCamelCase_							  =					new_path.replace("""middle_block.0"""			,				"""mid_block.resnets.0"""       )
														lowerCamelCase_							  =					new_path.replace("""middle_block.1"""			,				"""mid_block.attentions.0"""       )
														lowerCamelCase_							  =					new_path.replace("""middle_block.2"""			,				"""mid_block.resnets.1"""       )

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												lowerCamelCase_							  =					new_path.replace(replacement["""old"""]			,				replacement["""new"""]       )

        # proj_attn.weight has to be converted from conv 1D to linear
														if "proj_attn.weight" in new_path:
																					lowerCamelCase_							  =					old_checkpoint[path["""old"""]][:, :, 0]
														else:
																					lowerCamelCase_							  =					old_checkpoint[path["""old"""]]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Tuple       )  ->    Any:
							lowerCamelCase_							  =					{}

							lowerCamelCase_							  =					checkpoint["""time_embed.0.weight"""]
							lowerCamelCase_							  =					checkpoint["""time_embed.0.bias"""]
							lowerCamelCase_							  =					checkpoint["""time_embed.2.weight"""]
							lowerCamelCase_							  =					checkpoint["""time_embed.2.bias"""]

							lowerCamelCase_							  =					checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase_							  =					checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase_							  =					checkpoint["""out.0.weight"""]
							lowerCamelCase_							  =					checkpoint["""out.0.bias"""]
							lowerCamelCase_							  =					checkpoint["""out.2.weight"""]
							lowerCamelCase_							  =					checkpoint["""out.2.bias"""]

							# Retrieves the keys for the input blocks only
							lowerCamelCase_							  =					len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """input_blocks""" in layer}       )
							lowerCamelCase_							  =					{
							    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
							    for layer_id in range(UpperCAmelCase__       )
							}

							# Retrieves the keys for the middle blocks only
							lowerCamelCase_							  =					len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """middle_block""" in layer}       )
							lowerCamelCase_							  =					{
							    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
							    for layer_id in range(UpperCAmelCase__       )
							}

							# Retrieves the keys for the output blocks only
							lowerCamelCase_							  =					len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """output_blocks""" in layer}       )
							lowerCamelCase_							  =					{
							    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
							    for layer_id in range(UpperCAmelCase__       )
							}

							for i in range(1			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					(i - 1) // (config["""num_res_blocks"""] + 1)
														lowerCamelCase_							  =					(i - 1) % (config["""num_res_blocks"""] + 1)

														lowerCamelCase_							  =					[key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
														lowerCamelCase_							  =					[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

														if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
																					lowerCamelCase_							  =					checkpoint[
																					    F'''input_blocks.{i}.0.op.weight'''
																					]
																					lowerCamelCase_							  =					checkpoint[
																					    F'''input_blocks.{i}.0.op.bias'''
																					]
																					continue

														lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__       )
														lowerCamelCase_							  =					{"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
														lowerCamelCase_							  =					{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
														assign_to_checkpoint(
														    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				additional_replacements=[meta_path, resnet_op]			,				config=UpperCAmelCase__       )

														if len(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					renew_attention_paths(UpperCAmelCase__       )
																					lowerCamelCase_							  =					{
																					    """old""": F'''input_blocks.{i}.1''',
																					    """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
																					}
																					lowerCamelCase_							  =					{
																					    F'''input_blocks.{i}.1.qkv.bias''': {
																					        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																					        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																					        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																					    },
																					    F'''input_blocks.{i}.1.qkv.weight''': {
																					        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																					        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																					        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																					    },
																					}
																					assign_to_checkpoint(
																					    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				additional_replacements=[meta_path]			,				attention_paths_to_split=UpperCAmelCase__			,				config=UpperCAmelCase__			,				)

							lowerCamelCase_							  =					middle_blocks[0]
							lowerCamelCase_							  =					middle_blocks[1]
							lowerCamelCase_							  =					middle_blocks[2]

							lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__       )
							assign_to_checkpoint(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				config=UpperCAmelCase__       )

							lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__       )
							assign_to_checkpoint(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				config=UpperCAmelCase__       )

							lowerCamelCase_							  =					renew_attention_paths(UpperCAmelCase__       )
							lowerCamelCase_							  =					{
							    """middle_block.1.qkv.bias""": {
							        """key""": """mid_block.attentions.0.key.bias""",
							        """query""": """mid_block.attentions.0.query.bias""",
							        """value""": """mid_block.attentions.0.value.bias""",
							    },
							    """middle_block.1.qkv.weight""": {
							        """key""": """mid_block.attentions.0.key.weight""",
							        """query""": """mid_block.attentions.0.query.weight""",
							        """value""": """mid_block.attentions.0.value.weight""",
							    },
							}
							assign_to_checkpoint(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				attention_paths_to_split=UpperCAmelCase__			,				config=UpperCAmelCase__       )

							for i in range(UpperCAmelCase__       ):
														lowerCamelCase_							  =					i // (config["""num_res_blocks"""] + 1)
														lowerCamelCase_							  =					i % (config["""num_res_blocks"""] + 1)
														lowerCamelCase_							  =					[shave_segments(UpperCAmelCase__			,				2       ) for name in output_blocks[i]]
														lowerCamelCase_							  =					{}

														for layer in output_block_layers:
																					lowerCamelCase_  ,   lowerCamelCase_							  =					layer.split("""."""       )[0], shave_segments(UpperCAmelCase__			,				1       )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(UpperCAmelCase__       )
																					else:
																												lowerCamelCase_							  =					[layer_name]

														if len(UpperCAmelCase__       ) > 1:
																					lowerCamelCase_							  =					[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																					lowerCamelCase_							  =					[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

																					lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__       )
																					lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__       )

																					lowerCamelCase_							  =					{"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
																					assign_to_checkpoint(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				additional_replacements=[meta_path]			,				config=UpperCAmelCase__       )

																					if ["conv.weight", "conv.bias"] in output_block_list.values():
																												lowerCamelCase_							  =					list(output_block_list.values()       ).index(["""conv.weight""", """conv.bias"""]       )
																												lowerCamelCase_							  =					checkpoint[
																												    F'''output_blocks.{i}.{index}.conv.weight'''
																												]
																												lowerCamelCase_							  =					checkpoint[
																												    F'''output_blocks.{i}.{index}.conv.bias'''
																												]

																												# Clear attentions as they have been attributed above.
																												if len(UpperCAmelCase__       ) == 2:
																																			lowerCamelCase_							  =					[]

																					if len(UpperCAmelCase__       ):
																												lowerCamelCase_							  =					renew_attention_paths(UpperCAmelCase__       )
																												lowerCamelCase_							  =					{
																												    """old""": F'''output_blocks.{i}.1''',
																												    """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																												}
																												lowerCamelCase_							  =					{
																												    F'''output_blocks.{i}.1.qkv.bias''': {
																												        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																												        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																												        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																												    },
																												    F'''output_blocks.{i}.1.qkv.weight''': {
																												        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																												        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																												        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																												    },
																												}
																												assign_to_checkpoint(
																												    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				additional_replacements=[meta_path]			,				attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions       ) else None			,				config=UpperCAmelCase__			,				)
														else:
																					lowerCamelCase_							  =					renew_resnet_paths(UpperCAmelCase__			,				n_shave_prefix_segments=1       )
																					for path in resnet_0_paths:
																												lowerCamelCase_							  =					""".""".join(["""output_blocks""", str(UpperCAmelCase__       ), path["""old"""]]       )
																												lowerCamelCase_							  =					""".""".join(["""up_blocks""", str(UpperCAmelCase__       ), """resnets""", str(UpperCAmelCase__       ), path["""new"""]]       )

																												lowerCamelCase_							  =					checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
		lowercase        =  argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		lowercase        =  parser.parse_args()

		lowercase        =  torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				lowercase        =  json.loads(f.read())

		lowercase        =  convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		lowercase        =  UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				lowercase        =  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				lowercase        =  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				lowercase        =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)







import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.ModuleList()
												for lyr_num in range(__UpperCamelCase   ):
																			# FiLM conditional T5 decoder
																			lowerCamelCase_							  =					DecoderLayer(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
																			self.decoders.append(__UpperCamelCase   )

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

												# decoder_noise_time is in [0, 1), so rescale to expected timing range.
												lowerCamelCase_							  =					get_timestep_embedding(
												    decoder_noise_time * self.config.max_decoder_noise_time   ,					embedding_dim=self.config.d_model   ,					max_period=self.config.max_decoder_noise_time   ,					).to(dtype=self.dtype   )

												lowerCamelCase_							  =					self.conditioning_emb(__UpperCamelCase   ).unsqueeze(1   )

												assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

												lowerCamelCase_							  =					decoder_input_tokens.shape[1]

												# If we want to use relative positions for audio context, we can just offset
												# this sequence by the length of encodings_and_masks.
												lowerCamelCase_							  =					torch.broadcast_to(
												    torch.arange(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												# decoder: No padding present.
												lowerCamelCase_							  =					torch.ones(
												    decoder_input_tokens.shape[:2]   ,					device=decoder_input_tokens.device   ,					dtype=inputs.dtype   )

												# Translate encoding masks to encoder-decoder masks.
												lowerCamelCase_							  =					[(x, self.encoder_decoder_mask(__UpperCamelCase   ,					__UpperCamelCase   )) for x, y in encodings_and_masks]

												# cross attend style: concat encodings
												lowerCamelCase_							  =					torch.cat([x[0] for x in encodings_and_encdec_masks]   ,					dim=1   )
												lowerCamelCase_							  =					torch.cat([x[1] for x in encodings_and_encdec_masks]   ,					dim=-1   )

												for lyr in self.decoders:
																			lowerCamelCase_							  =					lyr(
																			    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					encoder_attention_mask=__UpperCamelCase   ,					)[0]

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.ModuleList()

												# cond self attention: layer 0
												self.layer.append(
												    TaLayerSelfAttentionCond(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )   )

												# cross attention: layer 1
												self.layer.append(
												    TaLayerCrossAttention(
												        d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   ,					)   )

												# Film Cond MLP + dropout: last layer
												self.layer.append(
												    TaLayerFFCond(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   )   )



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												if encoder_hidden_states is not None:
																			lowerCamelCase_							  =					torch.where(encoder_attention_mask > 0   ,					0   ,					-1E10   ).to(
																			    encoder_hidden_states.dtype   )

																			lowerCamelCase_							  =					self.layer[1](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												# Apply Film Conditional Feed Forward layer
												lowerCamelCase_							  =					self.layer[-1](__UpperCamelCase   ,					__UpperCamelCase   )

												return (hidden_states,)





class       __A(				nn.Module       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												super().__init__()
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.FiLMLayer(__UpperCamelCase   ,					__UpperCamelCase   )

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					TaDenseGatedActDense(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
												# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
												# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
												# half-precision inputs is done in fp32

												lowerCamelCase_							  =					hidden_states.to(torch.floataa   ).pow(2   ).mean(-1   ,					keepdim=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

												# convert into half-precision if necessary
												if self.weight.dtype in [torch.floataa, torch.bfloataa]:
																			lowerCamelCase_							  =					hidden_states.to(self.weight.dtype   )

												return self.weight * hidden_states





class       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





