"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[Any]=1      ) ->      str:



							'''simple docstring'''

							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("."      )[n_shave_prefix_segments:]      )
							else:
														return ".".join(path.split("."      )[:n_shave_prefix_segments]      )


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int=0      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     []
							for old_item in old_list:
														__lowerCamelCase					:		List[Any]		   =     old_item.replace("in_layers.0"						,   "norm1"      )
														__lowerCamelCase					:		Union[str, Any]		   =     new_item.replace("in_layers.2"						,   "conv1"      )

														__lowerCamelCase					:		List[Any]		   =     new_item.replace("out_layers.0"						,   "norm2"      )
														__lowerCamelCase					:		int		   =     new_item.replace("out_layers.3"						,   "conv2"      )

														__lowerCamelCase					:		Any		   =     new_item.replace("emb_layers.1"						,   "time_emb_proj"      )
														__lowerCamelCase					:		Dict		   =     new_item.replace("skip_connection"						,   "conv_shortcut"      )

														__lowerCamelCase					:		int		   =     shave_segments(_lowerCamelCase						,   n_shave_prefix_segments=_lowerCamelCase      )

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

							return mapping


def    lowercase_   (   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Optional[int]=0      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     []
							for old_item in old_list:
														__lowerCamelCase					:		Any		   =     old_item

														__lowerCamelCase					:		Any		   =     new_item.replace("norm.weight"						,   "group_norm.weight"      )
														__lowerCamelCase					:		Tuple		   =     new_item.replace("norm.bias"						,   "group_norm.bias"      )

														__lowerCamelCase					:		Dict		   =     new_item.replace("proj_out.weight"						,   "proj_attn.weight"      )
														__lowerCamelCase					:		Any		   =     new_item.replace("proj_out.bias"						,   "proj_attn.bias"      )

														__lowerCamelCase					:		Optional[Any]		   =     shave_segments(_lowerCamelCase						,   n_shave_prefix_segments=_lowerCamelCase      )

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

							return mapping


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Dict=None						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Union[str, Any]=None      ) ->      Union[str, Any]:



							'''simple docstring'''

							assert isinstance(_lowerCamelCase						,   _lowerCamelCase      ), "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					:		Union[str, Any]		   =     old_checkpoint[path]
																					__lowerCamelCase					:		List[str]		   =     old_tensor.shape[0] // 3

																					__lowerCamelCase					:		Any		   =     (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

																					__lowerCamelCase					:		List[str]		   =     old_tensor.shape[0] // config["num_head_channels"] // 3

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

																					__lowerCamelCase					:		Optional[Any]		   =     query.reshape(_lowerCamelCase      )
																					__lowerCamelCase					:		List[Any]		   =     key.reshape(_lowerCamelCase      )
																					__lowerCamelCase					:		Optional[Any]		   =     value.reshape(_lowerCamelCase      )

							for path in paths:
														__lowerCamelCase					:		Tuple		   =     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					:		List[str]		   =     new_path.replace("middle_block.0"						,   "mid_block.resnets.0"      )
														__lowerCamelCase					:		Dict		   =     new_path.replace("middle_block.1"						,   "mid_block.attentions.0"      )
														__lowerCamelCase					:		int		   =     new_path.replace("middle_block.2"						,   "mid_block.resnets.1"      )

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												__lowerCamelCase					:		Union[str, Any]		   =     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					:		Dict		   =     old_checkpoint[path["old"]][:, :, 0]
														else:
																					__lowerCamelCase					:		Union[str, Any]		   =     old_checkpoint[path["old"]]


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     {}

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["time_embed.0.weight"]
							__lowerCamelCase					:		str		   =     checkpoint["time_embed.0.bias"]
							__lowerCamelCase					:		Optional[Any]		   =     checkpoint["time_embed.2.weight"]
							__lowerCamelCase					:		int		   =     checkpoint["time_embed.2.bias"]

							__lowerCamelCase					:		List[Any]		   =     checkpoint["input_blocks.0.0.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["input_blocks.0.0.bias"]

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["out.0.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.0.bias"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.2.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.2.bias"]

							# Retrieves the keys for the input blocks only
							__lowerCamelCase					:		List[str]		   =     len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "input_blocks" in layer}      )
							__lowerCamelCase					:		Union[str, Any]		   =     {
							    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(_lowerCamelCase      )
							}

							# Retrieves the keys for the middle blocks only
							__lowerCamelCase					:		Any		   =     len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "middle_block" in layer}      )
							__lowerCamelCase					:		Optional[int]		   =     {
							    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
							    for layer_id in range(_lowerCamelCase      )
							}

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

							for i in range(1						,   _lowerCamelCase      ):
														__lowerCamelCase					:		Union[str, Any]		   =     (i - 1) // (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Union[str, Any]		   =     (i - 1) % (config["num_res_blocks"] + 1)

														__lowerCamelCase					:		Any		   =     [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
														__lowerCamelCase					:		str		   =     [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					:		Tuple		   =     checkpoint[
																					    F"""input_blocks.{i}.0.op.weight"""
																					]
																					__lowerCamelCase					:		int		   =     checkpoint[
																					    F"""input_blocks.{i}.0.op.bias"""
																					]
																					continue

														__lowerCamelCase					:		Union[str, Any]		   =     renew_resnet_paths(_lowerCamelCase      )
														__lowerCamelCase					:		Optional[int]		   =     {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														__lowerCamelCase					:		Dict		   =     {"old": "resnets.2.op", "new": "downsamplers.0.op"}
														assign_to_checkpoint(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path, resnet_op]						,   config=_lowerCamelCase      )

														if len(_lowerCamelCase      ):
																					__lowerCamelCase					:		Optional[Any]		   =     renew_attention_paths(_lowerCamelCase      )
																					__lowerCamelCase					:		List[str]		   =     {
																					    "old": F"""input_blocks.{i}.1""",
																					    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																					}
																					__lowerCamelCase					:		List[str]		   =     {
																					    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(
																					    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   attention_paths_to_split=_lowerCamelCase						,   config=_lowerCamelCase						,   )

							__lowerCamelCase					:		List[Any]		   =     middle_blocks[0]
							__lowerCamelCase					:		Optional[Any]		   =     middle_blocks[1]
							__lowerCamelCase					:		int		   =     middle_blocks[2]

							__lowerCamelCase					:		Any		   =     renew_resnet_paths(_lowerCamelCase      )
							assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   config=_lowerCamelCase      )

							__lowerCamelCase					:		Tuple		   =     renew_resnet_paths(_lowerCamelCase      )
							assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   config=_lowerCamelCase      )

							__lowerCamelCase					:		str		   =     renew_attention_paths(_lowerCamelCase      )
							__lowerCamelCase					:		Dict		   =     {
							    "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(
							    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   attention_paths_to_split=_lowerCamelCase						,   config=_lowerCamelCase      )

							for i in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Tuple		   =     i // (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Optional[Any]		   =     i % (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Tuple		   =     [shave_segments(_lowerCamelCase						,   2      ) for name in output_blocks[i]]
														__lowerCamelCase					:		Optional[int]		   =     {}

														for layer in output_block_layers:
																					__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     layer.split("."      )[0], shave_segments(_lowerCamelCase						,   1      )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(_lowerCamelCase      )
																					else:
																												__lowerCamelCase					:		List[str]		   =     [layer_name]

														if len(_lowerCamelCase      ) > 1:
																					__lowerCamelCase					:		Union[str, Any]		   =     [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					__lowerCamelCase					:		Optional[int]		   =     [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

																					__lowerCamelCase					:		List[str]		   =     renew_resnet_paths(_lowerCamelCase      )
																					__lowerCamelCase					:		Dict		   =     renew_resnet_paths(_lowerCamelCase      )

																					__lowerCamelCase					:		Union[str, Any]		   =     {"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   config=_lowerCamelCase      )

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

																												# Clear attentions as they have been attributed above.
																												if len(_lowerCamelCase      ) == 2:
																																			__lowerCamelCase					:		Union[str, Any]		   =     []

																					if len(_lowerCamelCase      ):
																												__lowerCamelCase					:		Tuple		   =     renew_attention_paths(_lowerCamelCase      )
																												__lowerCamelCase					:		Optional[int]		   =     {
																												    "old": F"""output_blocks.{i}.1""",
																												    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												__lowerCamelCase					:		Optional[Any]		   =     {
																												    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(
																												    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   attention_paths_to_split=to_split if any("qkv" in key for key in attentions      ) else None						,   config=_lowerCamelCase						,   )
														else:
																					__lowerCamelCase					:		str		   =     renew_resnet_paths(_lowerCamelCase						,   n_shave_prefix_segments=1      )
																					for path in resnet_0_paths:
																												__lowerCamelCase					:		List[Any]		   =     ".".join(["output_blocks", str(_lowerCamelCase      ), path["old"]]      )
																												__lowerCamelCase					:		int		   =     ".".join(["up_blocks", str(_lowerCamelCase      ), "resnets", str(_lowerCamelCase      ), path["new"]]      )

																												__lowerCamelCase					:		List[str]		   =     checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
				__A  =				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.''')

				__A  =				parser.parse_args()

				__A  =				torch.load(args.checkpoint_path)

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

				__A  =				convert_ldm_checkpoint(checkpoint, config)

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

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

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

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





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  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     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					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

													__lowerCamelCase					:		Optional[int]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     nn.ModuleList()
													for lyr_num in range(UpperCAmelCase    ):
																				# FiLM conditional T5 decoder
																				__lowerCamelCase					:		List[str]		   =     DecoderLayer(d_model=UpperCAmelCase							,					d_kv=UpperCAmelCase							,					num_heads=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
																				self.decoders.append(UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

													# decoder_noise_time is in [0, 1), so rescale to expected timing range.
													__lowerCamelCase					:		str		   =     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					:		Optional[int]		   =     self.conditioning_emb(UpperCAmelCase    ).unsqueeze(1    )

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

													__lowerCamelCase					:		Tuple		   =     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					:		int		   =     torch.broadcast_to(
													    torch.arange(UpperCAmelCase							,					device=decoder_input_tokens.device    )							,					(batch, seq_length)							,					)

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.continuous_inputs_projection(UpperCAmelCase    )
													inputs += position_encodings
													__lowerCamelCase					:		List[Any]		   =     self.dropout(UpperCAmelCase    )

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

													# Translate encoding masks to encoder-decoder masks.
													__lowerCamelCase					:		Optional[Any]		   =     [(x, self.encoder_decoder_mask(UpperCAmelCase							,					UpperCAmelCase    )) for x, y in encodings_and_masks]

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

													for lyr in self.decoders:
																				__lowerCamelCase					:		List[Any]		   =     lyr(
																				    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					)[0]

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     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       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

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

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

													if conditioning_emb is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.FiLMLayer(UpperCAmelCase							,					UpperCAmelCase    )

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     NewGELUActivation()







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Parameter(torch.ones(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     eps







						def       lowerCamelCase__     (       self      :			Tuple							,					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					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

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

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x