
import tensorflow as tf

from ...tf_utils import shape_list


class 		__magic_name__ (    tf.keras.layers.Layer     ):





		def __init__(				self     :						Union[str, Any]    ,	lowerCamelCase__     :						str    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						Optional[int]    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						List[Any]=1    ,	lowerCamelCase__     :						int=False    ,	**lowerCamelCase__     :						Optional[Any]					):
							super().__init__(**lowerCAmelCase__					)

							lowerCAmelCase		:       Dict							=  vocab_size
							lowerCAmelCase		:       int							=  d_embed
							lowerCAmelCase		:       Optional[int]							=  d_proj

							lowerCAmelCase		:       List[Any]							=  cutoffs + [vocab_size]
							lowerCAmelCase		:       Optional[int]							=  [0] + self.cutoffs
							lowerCAmelCase		:       Tuple							=  div_val

							lowerCAmelCase		:       Any							=  self.cutoffs[0]
							lowerCAmelCase		:       Tuple							=  len(self.cutoffs					) - 1
							lowerCAmelCase		:       Union[str, Any]							=  self.shortlist_size + self.n_clusters
							lowerCAmelCase		:       List[Any]							=  keep_order

							lowerCAmelCase		:       List[str]							=  []
							lowerCAmelCase		:       str							=  []





		def    _A				(				self     :						Optional[int]    ,	lowerCamelCase__     :						Tuple					):
							if self.n_clusters > 0:
												lowerCAmelCase		:       Dict							=  self.add_weight(
												    shape=(self.n_clusters, self.d_embed)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name='''cluster_weight'''					)
												lowerCAmelCase		:       int							=  self.add_weight(
												    shape=(self.n_clusters,)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name='''cluster_bias'''					)

							if self.div_val == 1:
												for i in range(len(self.cutoffs					)					):
																	if self.d_proj != self.d_embed:
																						lowerCAmelCase		:       str							=  self.add_weight(
																						    shape=(self.d_embed, self.d_proj)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_projs_._{i}'''    ,	)
																						self.out_projs.append(lowerCAmelCase__					)
																	else:
																						self.out_projs.append(lowerCAmelCase__					)
																	lowerCAmelCase		:       Union[str, Any]							=  self.add_weight(
																	    shape=(self.vocab_size, self.d_embed)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_layers_._{i}_._weight'''    ,	)
																	lowerCAmelCase		:       Optional[int]							=  self.add_weight(
																	    shape=(self.vocab_size,)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_layers_._{i}_._bias'''    ,	)
																	self.out_layers.append((weight, bias)					)
							else:
												for i in range(len(self.cutoffs					)					):
																	lowerCAmelCase						,				lowerCAmelCase		:       str							=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	lowerCAmelCase		:       Optional[int]							=  self.d_embed // (self.div_val**i)

																	lowerCAmelCase		:       int							=  self.add_weight(
																	    shape=(d_emb_i, self.d_proj)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_projs_._{i}'''					)
																	self.out_projs.append(lowerCAmelCase__					)
																	lowerCAmelCase		:       Optional[int]							=  self.add_weight(
																	    shape=(r_idx - l_idx, d_emb_i)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_layers_._{i}_._weight'''    ,	)
																	lowerCAmelCase		:       str							=  self.add_weight(
																	    shape=(r_idx - l_idx,)    ,	initializer='''zeros'''    ,	trainable=lowerCAmelCase__    ,	name=f'''out_layers_._{i}_._bias'''    ,	)
																	self.out_layers.append((weight, bias)					)
							super().build(lowerCAmelCase__					)





		@staticmethod
		def    _A				(				lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						int    ,	lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						Tuple=None					):
							lowerCAmelCase		:       Tuple							=  x
							if proj is not None:
												lowerCAmelCase		:       Tuple							=  tf.einsum('''ibd,ed->ibe'''    ,	lowerCAmelCase__    ,	lowerCAmelCase__					)
							return tf.einsum('''ibd,nd->ibn'''    ,	lowerCAmelCase__    ,	lowerCAmelCase__					) + b





		@staticmethod
		def    _A				(				lowerCamelCase__     :						Dict    ,	lowerCamelCase__     :						str					):
							lowerCAmelCase		:       List[str]							=  shape_list(lowerCAmelCase__					)
							lowerCAmelCase		:       Any							=  tf.range(lp_size[0]    ,	dtype=target.dtype					)
							lowerCAmelCase		:       Union[str, Any]							=  tf.stack([r, target]    ,	1					)
							return tf.gather_nd(lowerCAmelCase__    ,	lowerCAmelCase__					)





		def    _A				(				self     :						List[Any]    ,	lowerCamelCase__     :						Tuple    ,	lowerCamelCase__     :						int    ,	lowerCamelCase__     :						int=True    ,	lowerCamelCase__     :						int=False					):
							lowerCAmelCase		:       int							=  0
							if self.n_clusters == 0:
												lowerCAmelCase		:       Optional[Any]							=  self._logit(lowerCAmelCase__    ,	self.out_layers[0][0]    ,	self.out_layers[0][1]    ,	self.out_projs[0]					)
												if target is not None:
																	lowerCAmelCase		:       Tuple							=  tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCAmelCase__    ,	logits=lowerCAmelCase__					)
												lowerCAmelCase		:       List[Any]							=  tf.nn.log_softmax(lowerCAmelCase__    ,	axis=-1					)
							else:
												lowerCAmelCase		:       str							=  shape_list(lowerCAmelCase__					)
												lowerCAmelCase		:       str							=  []
												lowerCAmelCase		:       Dict							=  tf.zeros(hidden_sizes[:2]					)
												for i in range(len(self.cutoffs					)					):
																	lowerCAmelCase						,				lowerCAmelCase		:       Optional[int]							=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	if target is not None:
																						lowerCAmelCase		:       Tuple							=  (target >= l_idx) & (target < r_idx)
																						lowerCAmelCase		:       Tuple							=  tf.where(lowerCAmelCase__					)
																						lowerCAmelCase		:       Optional[int]							=  tf.boolean_mask(lowerCAmelCase__    ,	lowerCAmelCase__					) - l_idx

																	if self.div_val == 1:
																						lowerCAmelCase		:       List[Any]							=  self.out_layers[0][0][l_idx:r_idx]
																						lowerCAmelCase		:       Any							=  self.out_layers[0][1][l_idx:r_idx]
																	else:
																						lowerCAmelCase		:       List[str]							=  self.out_layers[i][0]
																						lowerCAmelCase		:       Dict							=  self.out_layers[i][1]

																	if i == 0:
																						lowerCAmelCase		:       List[Any]							=  tf.concat([cur_W, self.cluster_weight]    ,	0					)
																						lowerCAmelCase		:       Tuple							=  tf.concat([cur_b, self.cluster_bias]    ,	0					)

																						lowerCAmelCase		:       Optional[Any]							=  self._logit(lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__    ,	self.out_projs[0]					)
																						lowerCAmelCase		:       Tuple							=  tf.nn.log_softmax(lowerCAmelCase__					)
																						out.append(head_logprob[..., : self.cutoffs[0]]					)
																						if target is not None:
																											lowerCAmelCase		:       Optional[Any]							=  tf.boolean_mask(lowerCAmelCase__    ,	lowerCAmelCase__					)
																											lowerCAmelCase		:       Optional[int]							=  self._gather_logprob(lowerCAmelCase__    ,	lowerCAmelCase__					)
																	else:
																						lowerCAmelCase		:       Optional[Any]							=  self._logit(lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__    ,	self.out_projs[i]					)
																						lowerCAmelCase		:       int							=  tf.nn.log_softmax(lowerCAmelCase__					)
																						lowerCAmelCase		:       str							=  self.cutoffs[0] + i - 1  # No probability for the head cluster
																						lowerCAmelCase		:       Optional[int]							=  head_logprob[..., cluster_prob_idx, None] + tail_logprob
																						out.append(lowerCAmelCase__					)
																						if target is not None:
																											lowerCAmelCase		:       str							=  tf.boolean_mask(lowerCAmelCase__    ,	lowerCAmelCase__					)
																											lowerCAmelCase		:       List[str]							=  tf.boolean_mask(lowerCAmelCase__    ,	lowerCAmelCase__					)
																											lowerCAmelCase		:       Any							=  self._gather_logprob(lowerCAmelCase__    ,	lowerCAmelCase__					)
																											cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																	if target is not None:
																						loss += tf.scatter_nd(lowerCAmelCase__    ,	-cur_logprob    ,	shape_list(lowerCAmelCase__					)					)
												lowerCAmelCase		:       Optional[Any]							=  tf.concat(lowerCAmelCase__    ,	axis=-1					)

							if target is not None:
												if return_mean:
																	lowerCAmelCase		:       Optional[int]							=  tf.reduce_mean(lowerCAmelCase__					)
												# Add the training-time loss value to the layer using `self.add_loss()`.
												self.add_loss(lowerCAmelCase__					)

												# Log the loss as a metric (we could log arbitrary metrics,
												# including different metrics for training and inference.
												self.add_metric(lowerCAmelCase__    ,	name=self.name    ,	aggregation='''mean''' if return_mean else ''''''					)

							return out


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()

						parser.add_argument(
						    "--repo_path",
						    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.")

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						_snake_case      :							Any     	=    dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								_snake_case      :							Any     	=    config[key]
																								del config[key]

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												_snake_case      :							Any     	=    {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		_snake_case      :							List[Any]     	=    False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														_snake_case      :							Dict     	=    param_value
																														_snake_case      :							Any     	=    True
																		if not has_changed:
																								_snake_case      :							int     	=    param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))
