

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def   __UpperCAmelCase					(      __a :     int      )     ->	Tuple:

					"""simple docstring"""

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






import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       LxmertTokenizer
							UpperCAmelCase__			:		Optional[Any]             =       LxmertTokenizerFast
							UpperCAmelCase__			:		Any             =       True
							UpperCAmelCase__			:		Dict             =       True


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().setUp()

												_a      :       int   =						[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Tuple   =						'''UNwant\u00E9d,running'''
												_a      :       str   =						'''unwanted, running'''
												return input_text, output_text


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.tokenizer_class(self.vocab_file			)

												_a      :       str   =						tokenizer.tokenize('''UNwant\u00E9d,running'''			)
												self.assertListEqual(_a			,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a			)			,				[7, 4, 5, 1_0, 8, 9]			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.test_rust_tokenizer:
																	return

												_a      :       Optional[Any]   =						self.get_tokenizer()
												_a      :       str   =						self.get_rust_tokenizer()

												_a      :       Optional[Any]   =						'''I was born in 92000, and this is falsé.'''

												_a      :       Optional[Any]   =						tokenizer.tokenize(_a			)
												_a      :       List[Any]   =						rust_tokenizer.tokenize(_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       List[Any]   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
												_a      :       Any   =						rust_tokenizer.encode(_a			,				add_special_tokens=_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       Dict   =						self.get_rust_tokenizer()
												_a      :       Optional[int]   =						tokenizer.encode(_a			)
												_a      :       Dict   =						rust_tokenizer.encode(_a			)
												self.assertListEqual(_a			,				_a			)



