






"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->List[Any]:
						A__						:  int		  =  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__						:  Union[str, Any]		  =  args.output + """.pt"""
						A__						:  Tuple		  =  OrderedDict()
						with tf.device("""/CPU:0"""      ):
												A__						:  int		  =  tf.train.load_checkpoint(args.tf_model_dir      )
												A__						:  Any		  =  reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		A__						:  Any		  =  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[int]		  =  int(key_name[9]      )
																								elif key_name.startswith("""pasts/out"""      ):
																														A__						:  Dict		  =  8
																								A__						:  Optional[Any]		  =  """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								A__						:  List[Any]		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								A__						:  List[str]		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/moe"""      ):
																								A__						:  Dict		  =  int(key_name[9:].split("""/"""      )[0]      )
																								if key_name.endswith("""/switch_gating/kernel"""      ):
																														A__						:  int		  =  """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														A__						:  int		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														A__						:  Tuple		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/softmlp/kernel"""      ):
																														A__						:  int		  =  """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														A__						:  List[str]		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														A__						:  Optional[int]		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/wo/kernel"""      ) or key_name.endswith("""/wi/kernel"""      ):
																														A__						:  Optional[Any]		  =  key_name[-9:-7]
																														for i in range(1_6      ):
																																				A__						:  Union[str, Any]		  =  """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				A__						:  Dict		  =  (
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				A__						:  Dict		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/mlp"""      ):
																								A__						:  Optional[int]		  =  int(key_name[9:].split("""/"""      )[0]      )
																								if key_name.endswith("""/p1/kernel"""      ):
																														A__						:  int		  =  """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														A__						:  Optional[int]		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														A__						:  List[Any]		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/p1/bias"""      ):
																														A__						:  Optional[Any]		  =  """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														A__						:  Dict		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  Dict		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/p2/kernel"""      ):
																														A__						:  Optional[int]		  =  """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														A__						:  Optional[Any]		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														A__						:  Tuple		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/p2/bias"""      ):
																														A__						:  Optional[int]		  =  """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														A__						:  Optional[Any]		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  Dict		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/ln"""      ):
																								A__						:  Tuple		  =  int(key_name[8:].split("""/"""      )[0]      )
																								if key_name.endswith("""/b"""      ):
																														A__						:  Any		  =  """model.blocks.%d.feed_forward.norm.bias""" % player
																														A__						:  List[Any]		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  Optional[int]		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/g"""      ):
																														A__						:  Optional[Any]		  =  """model.blocks.%d.feed_forward.norm.weight""" % player
																														A__						:  Any		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  List[str]		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/att"""      ):
																								A__						:  Any		  =  int(key_name[9:].split("""/"""      )[0]      )
																								if key_name.endswith("""/qkv/kernel"""      ):
																														A__						:  Tuple		  =  vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														A__						:  List[str]		  =  state[:, 0, :, :]
																														A__						:  Any		  =  state[:, 1, :, :]
																														A__						:  Dict		  =  state[:, 2, :, :]
																														A__						:  List[str]		  =  (
																														    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__						:  List[str]		  =  (
																														    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__						:  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[int]		  =  """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														A__						:  Tuple		  =  torch.tensor(UpperCAmelCase__      )
																														A__						:  str		  =  """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														A__						:  List[str]		  =  torch.tensor(UpperCAmelCase__      )
																														A__						:  List[Any]		  =  """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														A__						:  Dict		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/o/kernel"""      ):
																														A__						:  Union[str, Any]		  =  """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														A__						:  Optional[Any]		  =  (
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														A__						:  Union[str, Any]		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/an"""      ):
																								A__						:  List[Any]		  =  int(key_name[8:].split("""/"""      )[0]      )
																								if key_name.endswith("""/b"""      ):
																														A__						:  List[str]		  =  """model.blocks.%d.self_attn.norm.bias""" % player
																														A__						:  Optional[int]		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  Union[str, Any]		  =  torch.tensor(UpperCAmelCase__      )
																								elif key_name.endswith("""/g"""      ):
																														A__						:  Any		  =  """model.blocks.%d.self_attn.norm.weight""" % player
																														A__						:  str		  =  vnp.copy()  # same because it is one dimensional
																														A__						:  Optional[Any]		  =  torch.tensor(UpperCAmelCase__      )
																		elif (
																		    key_name.startswith("""model/wte"""      )
																		    or key_name.startswith("""model/wpe"""      )
																		    or key_name.startswith("""model/ete"""      )
																		):
																								A__						:  Union[str, Any]		  =  {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								A__						:  Dict		  =  """model.%s.weight""" % nlayer
																								A__						:  int		  =  vnp.copy()  # same in embedded
																								A__						:  Dict		  =  torch.tensor(UpperCAmelCase__      )
																								if key_name.startswith("""model/wte"""      ):
																														A__						:  Dict		  =  """lm_head.weight"""
																														A__						:  Any		  =  vnp.copy()  # same in embedded
																														A__						:  List[Any]		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name.startswith("""model/wob"""      ):
																								A__						:  str		  =  """final_logits_bias"""
																								A__						:  Any		  =  vnp.copy()  # same in embedded
																								A__						:  Tuple		  =  state.reshape((1, -1)      )
																								A__						:  List[Any]		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name == "model/dense/kernel":
																								A__						:  Optional[Any]		  =  """model.last_project.weight"""
																								A__						:  str		  =  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								A__						:  str		  =  torch.tensor(UpperCAmelCase__      )
																		elif key_name == "model/dense_1/bias":
																								A__						:  Tuple		  =  """model.last_project.bias"""
																								A__						:  int		  =  vnp.copy()  # same because it is one dimensional
																								A__						:  Union[str, Any]		  =  torch.tensor(UpperCAmelCase__      )
						torch.save(UpperCAmelCase__,						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)














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






