
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


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


if __name__ == "__main__":
			_A  =	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")
			_A  =	parser.parse_args()
			convert_tf_gptsan_to_pt(args)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''bloom'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''num_hidden_layers''': '''n_layer''',
				    '''num_attention_heads''': '''n_head''',
				}




				def __init__(   self							,							_UpperCamelCase=250_880							,							_UpperCamelCase=64							,							_UpperCamelCase=2							,							_UpperCamelCase=8							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=1							,							_UpperCamelCase=2							,							_UpperCamelCase=False							,							_UpperCamelCase=0.0							,							_UpperCamelCase=0.0							,							_UpperCamelCase=1							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> str:
							lowerCAmelCase_         =      vocab_size
							# Backward compatibility with n_embed kwarg
							lowerCAmelCase_         =      kwargs.pop("n_embed"							,							_UpperCamelCase	)
							lowerCAmelCase_         =      hidden_size if n_embed is None else n_embed
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      pretraining_tp
							lowerCAmelCase_         =      apply_residual_connection_post_layernorm
							lowerCAmelCase_         =      hidden_dropout
							lowerCAmelCase_         =      attention_dropout

							lowerCAmelCase_         =      bos_token_id
							lowerCAmelCase_         =      eos_token_id
							lowerCAmelCase_         =      slow_but_exact

							super().__init__(bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=version.parse('''1.12'''			)




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase = "default"							,							_UpperCamelCase = None							,							_UpperCamelCase = False							,							)							-> int:
							super().__init__(_UpperCamelCase							,							task=_UpperCamelCase							,							patching_specs=_UpperCamelCase							,							use_past=_UpperCamelCase	)
							if not getattr(self._config							,							"pad_token_id"							,							_UpperCamelCase	):
										# TODO: how to do that better?
										lowerCAmelCase_         =      0




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							lowerCAmelCase_         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
							if self.use_past:
										# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
										self.fill_with_past_key_values_(_UpperCamelCase							,							direction="inputs"							,							inverted_values_shape=_UpperCamelCase	)
										lowerCAmelCase_         =      {0: "batch", 1: "past_sequence + sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}

							return common_inputs




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_layer




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_head




				@property
				def 	__a		(   self	)							-> float:
							return 1e-3




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = -1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      super(_UpperCamelCase							,							self	).generate_dummy_inputs(
							    _UpperCamelCase							,							batch_size=_UpperCamelCase							,							seq_length=_UpperCamelCase							,							is_pair=_UpperCamelCase							,							framework=_UpperCamelCase	)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase_         =      OrderedDict({"input_ids": common_inputs["input_ids"]}	)

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
										else:
													import torch

													lowerCAmelCase_      ,    lowerCAmelCase_         =      common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													lowerCAmelCase_         =      seqlen + 2
													lowerCAmelCase_         =      self._config.hidden_size // self.num_attention_heads
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													lowerCAmelCase_         =      [
													    (torch.zeros(_UpperCamelCase	), torch.zeros(_UpperCamelCase	)) for _ in range(self.num_layers	)
													]

							lowerCAmelCase_         =      common_inputs["attention_mask"]
							if self.use_past:
										lowerCAmelCase_         =      ordered_inputs["attention_mask"].dtype
										lowerCAmelCase_         =      torch.cat(
										    [ordered_inputs["attention_mask"], torch.ones(_UpperCamelCase							,							_UpperCamelCase							,							dtype=_UpperCamelCase	)]							,							dim=1	)

							return ordered_inputs




				@property
				def 	__a		(   self	)							-> int:
							return 13



