
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 		UpperCAmelCase     (      a_		)						->      List[Any]:







				"""simple docstring"""



				__A       =					os.path.join(args.tf_model_dir						,  "parameters.json"		)
				__A       =					json.loads(open(a_		).read()		)
				if not params:
								raise ValueError(
								    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''		)
				if not args.output.endswith(".pt"		):
								__A       =					args.output + ".pt"
				__A       =					OrderedDict()
				with tf.device("/CPU:0"		):
								__A       =					tf.train.load_checkpoint(args.tf_model_dir		)
								__A       =					reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												__A       =					reader.get_tensor(a_		).astype(np.floataa		)
												if key_name.endswith("/adam_m"		) or key_name.endswith("/adam_v"		):
																continue
												if key_name.startswith("pasts/"		):
																if key_name.startswith("pasts/mlp"		):
																				__A       =					int(key_name[9]		)
																elif key_name.startswith("pasts/out"		):
																				__A       =					8
																__A       =					"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/moe"		):
																__A       =					int(key_name[9:].split("/"		)[0]		)
																if key_name.endswith("/switch_gating/kernel"		):
																				__A       =					"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/softmlp/kernel"		):
																				__A       =					"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/wo/kernel"		) or key_name.endswith("/wi/kernel"		):
																				__A       =					key_name[-9:-7]
																				for i in range(1_6		):
																								__A       =					"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								__A       =					(
																								    vnp[i].transpose([1, 0]		).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/mlp"		):
																__A       =					int(key_name[9:].split("/"		)[0]		)
																if key_name.endswith("/p1/kernel"		):
																				__A       =					"model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/p1/bias"		):
																				__A       =					"model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/p2/kernel"		):
																				__A       =					"model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/p2/bias"		):
																				__A       =					"model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/ln"		):
																__A       =					int(key_name[8:].split("/"		)[0]		)
																if key_name.endswith("/b"		):
																				__A       =					"model.blocks.%d.feed_forward.norm.bias" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/g"		):
																				__A       =					"model.blocks.%d.feed_forward.norm.weight" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/att"		):
																__A       =					int(key_name[9:].split("/"		)[0]		)
																if key_name.endswith("/qkv/kernel"		):
																				__A       =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				__A       =					state[:, 0, :, :]
																				__A       =					state[:, 1, :, :]
																				__A       =					state[:, 2, :, :]
																				__A       =					(
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]		)
																				    .transpose([1, 0]		)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					(
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]		)
																				    .transpose([1, 0]		)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					(
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]		)
																				    .transpose([1, 0]		)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				__A       =					torch.tensor(a_		)
																				__A       =					"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				__A       =					torch.tensor(a_		)
																				__A       =					"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/o/kernel"		):
																				__A       =					"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				__A       =					(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]		).transpose([1, 0]		).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/an"		):
																__A       =					int(key_name[8:].split("/"		)[0]		)
																if key_name.endswith("/b"		):
																				__A       =					"model.blocks.%d.self_attn.norm.bias" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
																elif key_name.endswith("/g"		):
																				__A       =					"model.blocks.%d.self_attn.norm.weight" % player
																				__A       =					vnp.copy()  # same because it is one dimensional
																				__A       =					torch.tensor(a_		)
												elif (
												    key_name.startswith("model/wte"		)
												    or key_name.startswith("model/wpe"		)
												    or key_name.startswith("model/ete"		)
												):
																__A       =					{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																__A       =					"model.%s.weight" % nlayer
																__A       =					vnp.copy()  # same in embedded
																__A       =					torch.tensor(a_		)
																if key_name.startswith("model/wte"		):
																				__A       =					"lm_head.weight"
																				__A       =					vnp.copy()  # same in embedded
																				__A       =					torch.tensor(a_		)
												elif key_name.startswith("model/wob"		):
																__A       =					"final_logits_bias"
																__A       =					vnp.copy()  # same in embedded
																__A       =					state.reshape((1, -1)		)
																__A       =					torch.tensor(a_		)
												elif key_name == "model/dense/kernel":
																__A       =					"model.last_project.weight"
																__A       =					vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																__A       =					torch.tensor(a_		)
												elif key_name == "model/dense_1/bias":
																__A       =					"model.last_project.bias"
																__A       =					vnp.copy()  # same because it is one dimensional
																__A       =					torch.tensor(a_		)
				torch.save(a_						,  args.output		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:Optional[int]									=							argparse.ArgumentParser(
					    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
					parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
					SCREAMING_SNAKE_CASE				:Optional[Any]									=							parser.parse_args()
					convert_tf_gptsan_to_pt(args)



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings