




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=0	)  ->Union[str, Any]:






					"""simple docstring"""






					if name is None:
										__magic_name__ :							Tuple		       =							None
					else:
										__magic_name__ :							Any		       =							'''.''' * max(0,     spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
										__magic_name__ :							int		       =							fmt.format(UpperCAmelCase	)

					# Print and recurse (if needed).
					if isinstance(UpperCAmelCase,     UpperCAmelCase	):
										if msg is not None:
															print(UpperCAmelCase	)
										for k in val.keys():
															recursive_print(UpperCAmelCase,     val[k],     spaces + 2	)
					elif isinstance(UpperCAmelCase,     torch.Tensor	):
										print(UpperCAmelCase,     ''':''',     val.size()	)
					else:
										print(UpperCAmelCase,     ''':''',     UpperCAmelCase	)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Any:






					"""simple docstring"""






					__magic_name__ :							Optional[int]		       =							param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										__magic_name__ :							List[str]		       =							(num_heads, hidden_size, num_splits) + input_shape[1:]
										__magic_name__ :							List[str]		       =							param.view(*UpperCAmelCase	)
										__magic_name__ :							Dict		       =							param.transpose(0,     2	)
										__magic_name__ :							Optional[int]		       =							param.transpose(1,     2	).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										__magic_name__ :							List[Any]		       =							(num_heads, num_splits, hidden_size) + input_shape[1:]
										__magic_name__ :							str		       =							param.view(*UpperCAmelCase	)
										__magic_name__ :							Optional[int]		       =							param.transpose(0,     1	).contiguous()
					__magic_name__ :							Optional[int]		       =							param.view(*UpperCAmelCase	)
					return param







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							{}

					# old versions did not store training args
					__magic_name__ :							Optional[int]		       =							input_state_dict.get('''args''',     UpperCAmelCase	)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										__magic_name__ :							Dict		       =							ds_args.tokenizer_type
										__magic_name__ :							Tuple		       =							ds_args.padded_vocab_size
										__magic_name__ :							Optional[int]		       =							ds_args.max_position_embeddings
										__magic_name__ :							List[Any]		       =							ds_args.hidden_size
										__magic_name__ :							Dict		       =							ds_args.num_layers
										__magic_name__ :							Optional[int]		       =							ds_args.num_attention_heads
										__magic_name__ :							Any		       =							ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					__magic_name__ :							Union[str, Any]		       =							config.num_attention_heads
					# The hidden_size per head.
					__magic_name__ :							Union[str, Any]		       =							config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										__magic_name__ :							Optional[Any]		       =							input_state_dict['''checkpoint_version''']
					else:
										__magic_name__ :							Tuple		       =							0.0

					# The model.
					__magic_name__ :							Any		       =							input_state_dict['''model''']
					# The language model.
					__magic_name__ :							Union[str, Any]		       =							model['''language_model''']
					# The embeddings.
					__magic_name__ :							List[Any]		       =							lm['''embedding''']

					# The word embeddings.
					__magic_name__ :							Tuple		       =							embeddings['''word_embeddings''']['''weight''']
					# Truncate the embedding table to vocab_size rows.
					__magic_name__ :							int		       =							word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					__magic_name__ :							List[str]		       =							word_embeddings

					# The position embeddings.
					__magic_name__ :							str		       =							embeddings['''position_embeddings''']['''weight''']
					assert pos_embeddings.size(0	) == config.max_position_embeddings and pos_embeddings.size(1	) == config.hidden_size
					# Store the position embeddings.
					__magic_name__ :							Optional[Any]		       =							pos_embeddings

					# The token-type embeddings.
					__magic_name__ :							int		       =							embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					__magic_name__ :							Tuple		       =							tokentype_embeddings

					# The transformer.
					__magic_name__ :							Tuple		       =							lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

					# The regex to extract layer names.
					__magic_name__ :							int		       =							re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''	)

					# The simple map of names for "automated" rules.
					__magic_name__ :							Union[str, Any]		       =							{
					    '''attention.dense''': '''.attention.output.dense.''',
					    '''self_attention.dense''': '''.attention.output.dense.''',
					    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
					    '''mlp.dense_4h_to_h''': '''.output.dense.''',
					}

					# Keep track of the attention/query/value tensor.
					__magic_name__ :							Optional[Any]		       =							None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										__magic_name__ :							int		       =							layer_re.match(UpperCAmelCase	)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										__magic_name__ :							List[Any]		       =							int(m.group(1	)	)
										# The name of the operation.
										__magic_name__ :							Optional[Any]		       =							m.group(2	)
										# Is it a weight or a bias?
										__magic_name__ :							Optional[Any]		       =							m.group(3	)

										# The name of the layer.
										__magic_name__ :							List[Any]		       =							F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''	):
															__magic_name__ :							Optional[int]		       =							'''attention.ln''' if op_name.startswith('''input'''	) else '''ln'''
															__magic_name__ :							List[Any]		       =							val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															__magic_name__ :							Any		       =							fix_query_key_value_ordering(UpperCAmelCase,     UpperCAmelCase,     3,     UpperCAmelCase,     UpperCAmelCase	)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__magic_name__ :							str		       =							out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															__magic_name__ :							List[str]		       =							attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__magic_name__ :							str		       =							attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__magic_name__ :							Optional[Any]		       =							attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															__magic_name__ :							str		       =							fix_query_key_value_ordering(UpperCAmelCase,     UpperCAmelCase,     3,     UpperCAmelCase,     UpperCAmelCase	)
															# Split the bias.
															__magic_name__ :							Optional[int]		       =							out_val[0 * config.hidden_size : 1 * config.hidden_size]
															__magic_name__ :							str		       =							out_val[1 * config.hidden_size : 2 * config.hidden_size]
															__magic_name__ :							List[str]		       =							out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															__magic_name__ :							str		       =							q
															__magic_name__ :							List[Any]		       =							q_bias
															__magic_name__ :							List[str]		       =							k
															__magic_name__ :							Optional[Any]		       =							k_bias
															__magic_name__ :							Union[str, Any]		       =							v
															__magic_name__ :							str		       =							v_bias

															# Clear the stored tensor.
															__magic_name__ :							List[str]		       =							None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															__magic_name__ :							Dict		       =							megatron_to_transformers[op_name]
															__magic_name__ :							Optional[Any]		       =							val

    # The final layernorm.
					__magic_name__ :							Union[str, Any]		       =							transformer['''final_layernorm.weight''']
					__magic_name__ :							Union[str, Any]		       =							transformer['''final_layernorm.bias''']

					# The pooler.
					__magic_name__ :							List[Any]		       =							lm['''pooler''']

					# Store the matrix and the bias.
					__magic_name__ :							Tuple		       =							pooler['''dense.weight''']
					__magic_name__ :							str		       =							pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					__magic_name__ :							Any		       =							model['''lm_head''']

					# The transform matrix.
					__magic_name__ :							Optional[int]		       =							lm_head['''dense.weight''']
					__magic_name__ :							int		       =							lm_head['''dense.bias''']

					# The transform LN.
					__magic_name__ :							str		       =							lm_head['''layernorm.weight''']
					__magic_name__ :							Any		       =							lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					__magic_name__ :							int		       =							word_embeddings
					__magic_name__ :							Tuple		       =							lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					__magic_name__ :							Optional[Any]		       =							model['''binary_head''']

					# Store the classifier.
					__magic_name__ :							Union[str, Any]		       =							binary_head['''weight''']
					__magic_name__ :							List[str]		       =							binary_head['''bias''']

					# It should be done!
					return output_state_dict







def 			lowerCAmelCase  (     )  ->Optional[int]:






					"""simple docstring"""






					__magic_name__ :							Union[str, Any]		       =							argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure''',     action='''store_true'''	)
					parser.add_argument('''path_to_checkpoint''',     type=UpperCAmelCase,     help='''Path to the ZIP file containing the checkpoint'''	)
					parser.add_argument(
					    '''--config_file''',     default='''''',     type=UpperCAmelCase,     help='''An optional config json file describing the pre-trained model.''',     )
					__magic_name__ :							Dict		       =							parser.parse_args()

					# Extract the basename.
					__magic_name__ :							Tuple		       =							os.path.dirname(args.path_to_checkpoint	)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(F'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''	)
					if args.path_to_checkpoint.endswith('''.zip'''	):
										with zipfile.ZipFile(args.path_to_checkpoint,     '''r'''	) as checkpoint:
															with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''	) as pytorch_dict:
																				__magic_name__ :							Tuple		       =							torch.load(UpperCAmelCase,     map_location='''cpu'''	)
					else:
										__magic_name__ :							Optional[int]		       =							torch.load(args.path_to_checkpoint,     map_location='''cpu'''	)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										__magic_name__ :							Tuple		       =							MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										__magic_name__ :							Optional[int]		       =							input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										__magic_name__ :							Union[str, Any]		       =							MegatronBertConfig.from_json_file(args.config_file	)

					# Convert.
					print('''Converting'''	)
					__magic_name__ :							List[Any]		       =							convert_megatron_checkpoint(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(UpperCAmelCase,     UpperCAmelCase	)

					# Store the config to file.
					print('''Saving config'''	)
					config.save_pretrained(UpperCAmelCase	)

					# Store the state_dict to file.
					__magic_name__ :							List[Any]		       =							os.path.join(UpperCAmelCase,     '''pytorch_model.bin'''	)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''	)
					torch.save(UpperCAmelCase,     UpperCAmelCase	)


####################################################################################################

if __name__ == "__main__":
				main()

####################################################################################################









import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = False,     UpperCAmelCase = 1,     UpperCAmelCase = 1,     UpperCAmelCase = 1_0000,     )  ->List[Any]:






					"""simple docstring"""






					assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

					__magic_name__ :							List[str]		       =							embedding_dim // 2
					__magic_name__ :							Dict		       =							-math.log(UpperCAmelCase	) * torch.arange(
					    start=0,     end=UpperCAmelCase,     dtype=torch.floataa,     device=timesteps.device	)
					__magic_name__ :							Any		       =							exponent / (half_dim - downscale_freq_shift)

					__magic_name__ :							Tuple		       =							torch.exp(UpperCAmelCase	)
					__magic_name__ :							Tuple		       =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__ :							Optional[Any]		       =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__ :							Optional[int]		       =							torch.cat([torch.sin(UpperCAmelCase	), torch.cos(UpperCAmelCase	)],     dim=-1	)

					# flip sine and cosine embeddings
					if flip_sin_to_cos:
										__magic_name__ :							Tuple		       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]],     dim=-1	)

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__ :							List[str]		       =							torch.nn.functional.pad(UpperCAmelCase,     (0, 1, 0, 0)	)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=False,     UpperCAmelCase=0	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.meshgrid(UpperCAmelCase,     UpperCAmelCase	)  # here w goes first
					__magic_name__ :							Tuple		       =							np.stack(UpperCAmelCase,     axis=0	)

					__magic_name__ :							Any		       =							grid.reshape([2, 1, grid_size, grid_size]	)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(UpperCAmelCase,     UpperCAmelCase	)
					if cls_token and extra_tokens > 0:
										__magic_name__ :							List[Any]		       =							np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed],     axis=0	)
					return pos_embed







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					# use half of dimensions to encode grid_h
					__magic_name__ :							Union[str, Any]		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[0]	)  # (H*W, D/2)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[1]	)  # (H*W, D/2)

					__magic_name__ :							Tuple		       =							np.concatenate([emb_h, emb_w],     axis=1	)  # (H*W, D)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					__magic_name__ :							str		       =							np.arange(embed_dim // 2,     dtype=np.floataa	)
					omega /= embed_dim / 2.0
					__magic_name__ :							str		       =							1.0 / 1_0000**omega  # (D/2,)

					__magic_name__ :							List[str]		       =							pos.reshape(-1	)  # (M,)
					__magic_name__ :							Dict		       =							np.einsum('''m,d->md''',     UpperCAmelCase,     UpperCAmelCase	)  # (M, D/2), outer product

					__magic_name__ :							Union[str, Any]		       =							np.sin(UpperCAmelCase	)  # (M, D/2)
					__magic_name__ :							str		       =							np.cos(UpperCAmelCase	)  # (M, D/2)

					__magic_name__ :							int		       =							np.concatenate([emb_sin, emb_cos],     axis=1	)  # (M, D)
					return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase=224    ,					lowerCamelCase=224    ,					lowerCamelCase=16    ,					lowerCamelCase=3    ,					lowerCamelCase=768    ,					lowerCamelCase=False    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					)				->       Any:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							(height // patch_size) * (width // patch_size)
						__magic_name__ :							Tuple		       =							flatten
						__magic_name__ :							List[Any]		       =							layer_norm

						__magic_name__ :							Optional[Any]		       =							nn.Convad(
						    lowerCamelCase    ,					lowerCamelCase    ,					kernel_size=(patch_size, patch_size)    ,					stride=lowerCamelCase    ,					bias=lowerCamelCase						)
						if layer_norm:
											__magic_name__ :							Optional[int]		       =							nn.LayerNorm(lowerCamelCase    ,					elementwise_affine=lowerCamelCase    ,					eps=1e-6						)
						else:
											__magic_name__ :							int		       =							None

						__magic_name__ :							List[Any]		       =							get_ad_sincos_pos_embed(lowerCamelCase    ,					int(num_patches**0.5						)						)
						self.register_buffer('''pos_embed'''    ,					torch.from_numpy(lowerCamelCase						).float().unsqueeze(0						)    ,					persistent=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.proj(lowerCamelCase						)
						if self.flatten:
											__magic_name__ :							Dict		       =							latent.flatten(2						).transpose(1    ,					2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__ :							Tuple		       =							self.norm(lowerCamelCase						)
						return latent + self.pos_embed





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = "silu"    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase=None    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Union[str, Any]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if cond_proj_dim is not None:
											__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					bias=lowerCamelCase						)
						else:
											__magic_name__ :							Union[str, Any]		       =							None

						__magic_name__ :							Union[str, Any]		       =							get_activation(lowerCamelCase						)

						if out_dim is not None:
											__magic_name__ :							Tuple		       =							out_dim
						else:
											__magic_name__ :							List[str]		       =							time_embed_dim
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if post_act_fn is None:
											__magic_name__ :							List[str]		       =							None
						else:
											__magic_name__ :							Dict		       =							get_activation(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if condition is not None:
											__magic_name__ :							List[Any]		       =							sample + self.cond_proj(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							self.linear_a(lowerCamelCase						)

						if self.act is not None:
											__magic_name__ :							str		       =							self.act(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							self.linear_a(lowerCamelCase						)

						if self.post_act is not None:
											__magic_name__ :							List[str]		       =							self.post_act(lowerCamelCase						)
						return sample





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							num_channels
						__magic_name__ :							Tuple		       =							flip_sin_to_cos
						__magic_name__ :							List[Any]		       =							downscale_freq_shift






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							get_timestep_embedding(
						    lowerCamelCase    ,					self.num_channels    ,					flip_sin_to_cos=self.flip_sin_to_cos    ,					downscale_freq_shift=self.downscale_freq_shift    ,					)
						return t_emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 1.0    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)
						__magic_name__ :							List[str]		       =							log
						__magic_name__ :							Optional[int]		       =							flip_sin_to_cos

						if set_W_to_weight:
											# to delete later
											__magic_name__ :							Tuple		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)

											__magic_name__ :							List[str]		       =							self.W






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						if self.log:
											__magic_name__ :							Tuple		       =							torch.log(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
											__magic_name__ :							Any		       =							torch.cat([torch.cos(lowerCamelCase						), torch.sin(lowerCamelCase						)]    ,					dim=-1						)
						else:
											__magic_name__ :							Dict		       =							torch.cat([torch.sin(lowerCamelCase						), torch.cos(lowerCamelCase						)]    ,					dim=-1						)
						return out





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Tuple		       =							height
						__magic_name__ :							Dict		       =							width
						__magic_name__ :							Any		       =							num_embed
						__magic_name__ :							List[str]		       =							embed_dim

						__magic_name__ :							Tuple		       =							nn.Embedding(self.num_embed    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.height    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.width    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.emb(lowerCamelCase						)

						__magic_name__ :							Dict		       =							self.height_emb(torch.arange(self.height    ,					device=index.device						).view(1    ,					self.height						)						)

						# 1 x H x D -> 1 x H x 1 x D
						__magic_name__ :							Any		       =							height_emb.unsqueeze(2						)

						__magic_name__ :							Optional[int]		       =							self.width_emb(torch.arange(self.width    ,					device=index.device						).view(1    ,					self.width						)						)

						# 1 x W x D -> 1 x 1 x W x D
						__magic_name__ :							str		       =							width_emb.unsqueeze(1						)

						__magic_name__ :							Dict		       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						__magic_name__ :							Any		       =							pos_emb.view(1    ,					self.height * self.width    ,					-1						)

						__magic_name__ :							Optional[Any]		       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							dropout_prob > 0
						__magic_name__ :							Optional[int]		       =							nn.Embedding(num_classes + use_cfg_embedding    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							num_classes
						__magic_name__ :							Union[str, Any]		       =							dropout_prob






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if force_drop_ids is None:
											__magic_name__ :							Dict		       =							torch.rand(labels.shape[0]    ,					device=labels.device						) < self.dropout_prob
						else:
											__magic_name__ :							List[str]		       =							torch.tensor(force_drop_ids == 1						)
						__magic_name__ :							Optional[int]		       =							torch.where(lowerCamelCase    ,					self.num_classes    ,					lowerCamelCase						)
						return labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
											__magic_name__ :							Optional[Any]		       =							self.token_drop(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							self.embedding_table(lowerCamelCase						)
						return embeddings





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 1024    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 10    ,					)				->       str:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							int		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							text_embeds.shape[0]

						# image
						__magic_name__ :							Optional[Any]		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_text_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)

						# text
						__magic_name__ :							Dict		       =							self.text_proj(lowerCamelCase						)

						return torch.cat([image_text_embeds, text_embeds]    ,					dim=1						)





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 32    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							List[Any]		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							image_embeds.shape[0]

						# image
						__magic_name__ :							Any		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)
						__magic_name__ :							Union[str, Any]		       =							self.norm(lowerCamelCase						)
						return image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=0.1						)				->       List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							Timesteps(num_channels=256    ,					flip_sin_to_cos=lowerCamelCase    ,					downscale_freq_shift=1						)
						__magic_name__ :							int		       =							TimestepEmbedding(in_channels=256    ,					time_embed_dim=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							LabelEmbedding(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.time_proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase						)						)  # (N, D)

						__magic_name__ :							Optional[Any]		       =							self.class_embedder(lowerCamelCase						)  # (N, D)

						__magic_name__ :							Dict		       =							timesteps_emb + class_labels  # (N, D)

						return conditioning





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 64						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							AttentionPooling(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Dict		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.norma(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							self.pool(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.norma(lowerCamelCase						)
						return hidden_states





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.text_proj(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.text_norm(lowerCamelCase						)

						# image
						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)

						return time_image_embeds + time_text_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Optional[int]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							int		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							int		       =							self.image_norm(lowerCamelCase						)
						return time_image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       List[str]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Any		       =							nn.Sequential(
						    nn.Convad(3    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					32    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					32    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					96    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					96    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					256    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(256    ,					4    ,					3    ,					padding=1						)    ,					)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							str		       =							self.image_norm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.input_hint_block(lowerCamelCase						)
						return time_image_embeds, hint





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							str		       =							dtype
						__magic_name__ :							str		       =							nn.Parameter(torch.randn(1    ,					lowerCamelCase						) / embed_dim**0.5						)
						__magic_name__ :							Tuple		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							str		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							num_heads
						__magic_name__ :							List[Any]		       =							embed_dim // self.num_heads






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__       ,			__magic_name__       ,			__magic_name__ :							str		       =							x.size()

						def shape(lowerCamelCase						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__magic_name__ :							Union[str, Any]		       =							x.view(lowerCamelCase    ,					-1    ,					self.num_heads    ,					self.dim_per_head						)
											# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
											__magic_name__ :							str		       =							x.transpose(1    ,					2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__magic_name__ :							List[str]		       =							x.reshape(bs * self.num_heads    ,					-1    ,					self.dim_per_head						)
											# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
											__magic_name__ :							int		       =							x.transpose(1    ,					2						)
											return x

						__magic_name__ :							List[Any]		       =							x.mean(dim=1    ,					keepdim=lowerCamelCase						) + self.positional_embedding.to(x.dtype						)
						__magic_name__ :							int		       =							torch.cat([class_token, x]    ,					dim=1						)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__ :							int		       =							shape(self.q_proj(lowerCamelCase						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__ :							str		       =							shape(self.k_proj(lowerCamelCase						)						)
						__magic_name__ :							Any		       =							shape(self.v_proj(lowerCamelCase						)						)

						# (bs*n_heads, class_token_length, length+class_token_length):
						__magic_name__ :							Optional[int]		       =							1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
						__magic_name__ :							Union[str, Any]		       =							torch.einsum('''bct,bcs->bts'''    ,					q * scale    ,					k * scale						)  # More stable with f16 than dividing afterwards
						__magic_name__ :							Union[str, Any]		       =							torch.softmax(weight.float()    ,					dim=-1						).type(weight.dtype						)

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__ :							Dict		       =							torch.einsum('''bts,bcs->bct'''    ,					lowerCamelCase    ,					lowerCamelCase						)

						# (bs, length+1, width)
						__magic_name__ :							Optional[int]		       =							a.reshape(lowerCamelCase    ,					-1    ,					1						).transpose(1    ,					2						)

						return a[:, 0, :]  # cls_token



