
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							os.path.join(args.tf_model_dir							,						'''parameters.json'''       )
				A__  :	List[str]         =							json.loads(open(__UpperCamelCase       ).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__  :	Dict         =							args.output + '''.pt'''
				A__  :	int         =							OrderedDict()
				with tf.device('''/CPU:0'''       ):
								A__  :	Optional[Any]         =							tf.train.load_checkpoint(args.tf_model_dir       )
								A__  :	List[str]         =							reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												A__  :	int         =							reader.get_tensor(__UpperCamelCase       ).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__  :	Optional[Any]         =							int(key_name[9]       )
																elif key_name.startswith('''pasts/out'''       ):
																				A__  :	Tuple         =							8
																A__  :	Dict         =							'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																A__  :	Dict         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																A__  :	Dict         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/moe'''       ):
																A__  :	Union[str, Any]         =							int(key_name[9:].split('''/'''       )[0]       )
																if key_name.endswith('''/switch_gating/kernel'''       ):
																				A__  :	Optional[int]         =							'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																				A__  :	Union[str, Any]         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				A__  :	List[str]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/softmlp/kernel'''       ):
																				A__  :	List[Any]         =							'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																				A__  :	List[Any]         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				A__  :	Union[str, Any]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/wo/kernel'''       ) or key_name.endswith('''/wi/kernel'''       ):
																				A__  :	List[str]         =							key_name[-9:-7]
																				for i in range(16       ):
																								A__  :	Dict         =							'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																								A__  :	List[Any]         =							(
																								    vnp[i].transpose([1, 0]       ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								A__  :	List[Any]         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/mlp'''       ):
																A__  :	Tuple         =							int(key_name[9:].split('''/'''       )[0]       )
																if key_name.endswith('''/p1/kernel'''       ):
																				A__  :	List[str]         =							'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																				A__  :	Optional[Any]         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				A__  :	List[Any]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/p1/bias'''       ):
																				A__  :	List[Any]         =							'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																				A__  :	Any         =							vnp.copy()  # same because it is one dimensional
																				A__  :	Tuple         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/p2/kernel'''       ):
																				A__  :	str         =							'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																				A__  :	Dict         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				A__  :	Union[str, Any]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/p2/bias'''       ):
																				A__  :	List[Any]         =							'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																				A__  :	List[str]         =							vnp.copy()  # same because it is one dimensional
																				A__  :	Union[str, Any]         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/ln'''       ):
																A__  :	Optional[Any]         =							int(key_name[8:].split('''/'''       )[0]       )
																if key_name.endswith('''/b'''       ):
																				A__  :	Optional[Any]         =							'''model.blocks.%d.feed_forward.norm.bias''' % player
																				A__  :	Union[str, Any]         =							vnp.copy()  # same because it is one dimensional
																				A__  :	Optional[Any]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/g'''       ):
																				A__  :	List[str]         =							'''model.blocks.%d.feed_forward.norm.weight''' % player
																				A__  :	List[Any]         =							vnp.copy()  # same because it is one dimensional
																				A__  :	Tuple         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/att'''       ):
																A__  :	Any         =							int(key_name[9:].split('''/'''       )[0]       )
																if key_name.endswith('''/qkv/kernel'''       ):
																				A__  :	Union[str, Any]         =							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				A__  :	List[Any]         =							state[:, 0, :, :]
																				A__  :	str         =							state[:, 1, :, :]
																				A__  :	Optional[Any]         =							state[:, 2, :, :]
																				A__  :	List[Any]         =							(
																				    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__  :	Optional[int]         =							(
																				    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__  :	Optional[int]         =							(
																				    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__  :	Optional[Any]         =							'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																				A__  :	Optional[Any]         =							torch.tensor(__UpperCamelCase       )
																				A__  :	Optional[int]         =							'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																				A__  :	Optional[int]         =							torch.tensor(__UpperCamelCase       )
																				A__  :	Dict         =							'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																				A__  :	Tuple         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/o/kernel'''       ):
																				A__  :	Optional[int]         =							'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																				A__  :	Union[str, Any]         =							(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]       ).transpose([1, 0]       ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				A__  :	List[Any]         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/an'''       ):
																A__  :	Optional[Any]         =							int(key_name[8:].split('''/'''       )[0]       )
																if key_name.endswith('''/b'''       ):
																				A__  :	int         =							'''model.blocks.%d.self_attn.norm.bias''' % player
																				A__  :	Union[str, Any]         =							vnp.copy()  # same because it is one dimensional
																				A__  :	Union[str, Any]         =							torch.tensor(__UpperCamelCase       )
																elif key_name.endswith('''/g'''       ):
																				A__  :	int         =							'''model.blocks.%d.self_attn.norm.weight''' % player
																				A__  :	int         =							vnp.copy()  # same because it is one dimensional
																				A__  :	str         =							torch.tensor(__UpperCamelCase       )
												elif (
												    key_name.startswith('''model/wte'''       )
												    or key_name.startswith('''model/wpe'''       )
												    or key_name.startswith('''model/ete'''       )
												):
																A__  :	List[str]         =							{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																    key_name[-3:]
																]
																A__  :	Optional[Any]         =							'''model.%s.weight''' % nlayer
																A__  :	List[str]         =							vnp.copy()  # same in embedded
																A__  :	int         =							torch.tensor(__UpperCamelCase       )
																if key_name.startswith('''model/wte'''       ):
																				A__  :	Optional[int]         =							'''lm_head.weight'''
																				A__  :	Any         =							vnp.copy()  # same in embedded
																				A__  :	str         =							torch.tensor(__UpperCamelCase       )
												elif key_name.startswith('''model/wob'''       ):
																A__  :	Any         =							'''final_logits_bias'''
																A__  :	Union[str, Any]         =							vnp.copy()  # same in embedded
																A__  :	Dict         =							state.reshape((1, -1)       )
																A__  :	Optional[Any]         =							torch.tensor(__UpperCamelCase       )
												elif key_name == "model/dense/kernel":
																A__  :	Any         =							'''model.last_project.weight'''
																A__  :	str         =							vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
																A__  :	int         =							torch.tensor(__UpperCamelCase       )
												elif key_name == "model/dense_1/bias":
																A__  :	List[Any]         =							'''model.last_project.bias'''
																A__  :	Any         =							vnp.copy()  # same because it is one dimensional
																A__  :	int         =							torch.tensor(__UpperCamelCase       )
				torch.save(__UpperCamelCase							,						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    :      List[str]          =			parser.parse_args()
					convert_tf_gptsan_to_pt(args)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP