
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   str="attention"							)	->     List[Any]:
							lowerCamelCase_					=       lowerCamelCase_					=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]							)
							lowerCamelCase_					=       k_tmp.reshape(k_tmp.shape[0]      ,					k_tmp.shape[1] * k_tmp.shape[2]							)
							lowerCamelCase_					=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]							)
							lowerCamelCase_					=       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,					o_tmp.shape[2]							)
							lowerCamelCase_					=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]							)
							lowerCamelCase_					=       q_tmp.reshape(q_tmp.shape[0]      ,					q_tmp.shape[1] * q_tmp.shape[2]							)
							lowerCamelCase_					=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]							)
							lowerCamelCase_					=       v_tmp.reshape(v_tmp.shape[0]      ,					v_tmp.shape[1] * v_tmp.shape[2]							)
							return k, o, q, v



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Any=False							)	->     str:






							if split_mlp_wi:
														lowerCamelCase_					=       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
														lowerCamelCase_					=       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
														lowerCamelCase_					=       (wi_a, wi_a)
							else:
														lowerCamelCase_					=       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

							lowerCamelCase_					=       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
							return wi, wo



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Dict							)	->     Optional[int]:
							return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   dict      ,					*, UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   bool      ,					UpperCAmelCase__					:   bool = False							)	->     Optional[int]:
							lowerCamelCase_					=       traverse_util.flatten_dict(variables["""target"""]							)
							lowerCamelCase_					=       {"""/""".join(lowerCAmelCase__							): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							lowerCamelCase_					=       """encoder/encoder/mlp/wi_0/kernel""" in old
							print("""Split MLP:"""      ,					lowerCAmelCase__							)

							lowerCamelCase_					=       collections.OrderedDict()

							# Shared embeddings.
							lowerCamelCase_					=       old["""token_embedder/embedding"""]

							# Encoder.
							for i in range(lowerCAmelCase__							):
														# Block i, layer 0 (Self Attention).
														lowerCamelCase_					=       tax_layer_norm_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""encoder"""      ,					"""pre_attention_layer_norm"""							)
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       tax_attention_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""encoder"""      ,					"""attention"""							)
														lowerCamelCase_					=       layer_norm
														lowerCamelCase_					=       k.T
														lowerCamelCase_					=       o.T
														lowerCamelCase_					=       q.T
														lowerCamelCase_					=       v.T

														# Block i, layer 1 (MLP).
														lowerCamelCase_					=       tax_layer_norm_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""encoder"""      ,					"""pre_mlp_layer_norm"""							)
														lowerCamelCase_	, lowerCamelCase_					=       tax_mlp_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""encoder"""      ,					lowerCAmelCase__							)
														lowerCamelCase_					=       layer_norm
														if split_mlp_wi:
																					lowerCamelCase_					=       wi[0].T
																					lowerCamelCase_					=       wi[1].T
														else:
																					lowerCamelCase_					=       wi.T
														lowerCamelCase_					=       wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					lowerCamelCase_					=       tax_relpos_bias_lookup(
																					    lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""encoder"""							).T

							lowerCamelCase_					=       old["""encoder/encoder_norm/scale"""]

							if not scalable_attention:
														lowerCamelCase_					=       tax_relpos_bias_lookup(
														    lowerCAmelCase__      ,					0      ,					"""encoder"""							).T
														lowerCamelCase_					=       tax_relpos_bias_lookup(
														    lowerCAmelCase__      ,					0      ,					"""decoder"""							).T

							if not is_encoder_only:
														# Decoder.
														for i in range(lowerCAmelCase__							):
																					# Block i, layer 0 (Self Attention).
																					lowerCamelCase_					=       tax_layer_norm_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					"""pre_self_attention_layer_norm"""							)
																					lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       tax_attention_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					"""self_attention"""							)
																					lowerCamelCase_					=       layer_norm
																					lowerCamelCase_					=       k.T
																					lowerCamelCase_					=       o.T
																					lowerCamelCase_					=       q.T
																					lowerCamelCase_					=       v.T

																					# Block i, layer 1 (Cross Attention).
																					lowerCamelCase_					=       tax_layer_norm_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					"""pre_cross_attention_layer_norm"""							)
																					lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       tax_attention_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					"""encoder_decoder_attention"""							)
																					lowerCamelCase_					=       layer_norm
																					lowerCamelCase_					=       k.T
																					lowerCamelCase_					=       o.T
																					lowerCamelCase_					=       q.T
																					lowerCamelCase_					=       v.T

																					# Block i, layer 2 (MLP).
																					lowerCamelCase_					=       tax_layer_norm_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					"""pre_mlp_layer_norm"""							)
																					lowerCamelCase_	, lowerCamelCase_					=       tax_mlp_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""      ,					lowerCAmelCase__							)
																					lowerCamelCase_					=       layer_norm
																					if split_mlp_wi:
																												lowerCamelCase_					=       wi[0].T
																												lowerCamelCase_					=       wi[1].T
																					else:
																												lowerCamelCase_					=       wi.T
																					lowerCamelCase_					=       wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												lowerCamelCase_					=       tax_relpos_bias_lookup(lowerCAmelCase__      ,					lowerCAmelCase__      ,					"""decoder"""							).T

														lowerCamelCase_					=       old["""decoder/decoder_norm/scale"""]

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					lowerCamelCase_					=       old["""decoder/logits_dense/kernel"""].T

							return new



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   bool							)	->     int:
							lowerCamelCase_					=       collections.OrderedDict([(k, torch.from_numpy(v.copy()							)) for (k, v) in converted_params.items()]							)

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														lowerCamelCase_					=       state_dict["""shared.weight"""]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					lowerCamelCase_					=       state_dict["""shared.weight"""]

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print("""Using shared word embeddings as lm_head."""							)
																					lowerCamelCase_					=       state_dict["""shared.weight"""]

							return state_dict



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   int							)	->     str:
							lowerCamelCase_					=       checkpoints.load_tax_checkpoint(lowerCAmelCase__							)
							lowerCamelCase_					=       convert_tax_to_pytorch(
							    lowerCAmelCase__      ,					num_layers=config.num_layers      ,					is_encoder_only=lowerCAmelCase__      ,					scalable_attention=lowerCAmelCase__							)
							lowerCamelCase_					=       make_state_dict(lowerCAmelCase__      ,					lowerCAmelCase__							)
							model.load_state_dict(lowerCAmelCase__      ,					strict=lowerCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Optional[Any]      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   bool = False      ,					UpperCAmelCase__					:   bool = False      ,					)	->     Dict:
							lowerCamelCase_					=       MTaConfig.from_json_file(lowerCAmelCase__							)
							print(F'''Building PyTorch model from configuration: {config}'''							)
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														lowerCamelCase_					=       UMTaEncoderModel(lowerCAmelCase__							)
							else:
														lowerCamelCase_					=       UMTaForConditionalGeneration(lowerCAmelCase__							)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__							)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
							model.save_pretrained(lowerCAmelCase__							)

							# Verify that we can load the checkpoint.
							model.from_pretrained(lowerCAmelCase__							)
							print("""Done"""							)


if __name__ == "__main__":
		lowercase   =	argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
		# Required parameters
		parser.add_argument(
		    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
		)
		parser.add_argument(
		    '''--scalable_attention''',
		    action='''store_true''',
		    help='''Whether the model uses scaled attention (umt5 model)''',
		    default=False,
		)
		lowercase   =	parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path,
		    args.config_file,
		    args.pytorch_dump_path,
		    args.is_encoder_only,
		    args.scalable_attention,
		)










import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     GPTSanJapaneseTokenizer
				A_   :     Optional[int]								=     False
				A_   :     List[str]								=     {'do_clean_text': False, 'add_prefix_space': False}


				def __UpperCamelCase						(							self      :  Tuple       )		->     Any:
											super().setUp()

											# fmt: off
											A      						=    ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
											# fmt: on
											A      						=    {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['emoji_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
											with open(self.emoji_file						, 'w'       ) as emoji_writer:
																		emoji_writer.write(json.dumps(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Any						, **__UpperCamelCase      :  Any       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Dict       )		->     List[str]:
											A      						=    'こんにちは、世界。 \nこんばんは、㔺界。😀'
											A      						=    'こんにちは、世界。 \nこんばんは、世界。😀'
											return input_text, output_text


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Optional[Any]       )		->     List[str]:
											A						,   A      						=    self.get_input_output_texts(__UpperCamelCase       )
											A      						=    tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase						, clean_up_tokenization_spaces=__UpperCamelCase       )
											return text, ids


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[Any]:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											A      						=    self.get_tokenizer()

											# Testing tokenization
											A      						=    'こんにちは、世界。　こんばんは、㔺界。'
											A      						=    ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# Testing conversion to ids without special tokens
											A      						=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
											A      						=    tokenizer.convert_tokens_to_ids(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# Testing conversion to ids with special tokens
											A      						=    tokens + [tokenizer.unk_token]
											A      						=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
											A      						=    tokenizer.convert_tokens_to_ids(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    self.get_tokenizer()

											# Testing tokenization
											A      						=    'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
											A      						=    'こんにちは、、、、世界。こんばんは、、、、世界。'
											A      						=    tokenizer.encode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											# Testing tokenization
											A      						=    'こんにちは、世界。'
											A      						=    'こんばんは、㔺界。😀'
											A      						=    'こんにちは、世界。こんばんは、世界。😀'
											A      						=    tokenizer.encode(prefix_text + input_text       )
											A      						=    tokenizer.encode(''						, prefix_text=prefix_text + input_text       )
											A      						=    tokenizer.encode(__UpperCamelCase						, prefix_text=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											# Testing tokenization
											A      						=    'こんにちは、世界。'
											A      						=    'こんばんは、㔺界。😀'

											A      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 2
											A      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 2

											A      						=    [1] + [0] * (len_prefix + len_text + 1)
											A      						=    [1] * (len_prefix + len_text + 1) + [0]
											A      						=    [1] + [1] * (len_prefix) + [0] * (len_text + 1)

											A      						=    tokenizer(prefix_text + input_text       ).token_type_ids
											A      						=    tokenizer(''						, prefix_text=prefix_text + input_text       ).token_type_ids
											A      						=    tokenizer(__UpperCamelCase						, prefix_text=__UpperCamelCase       ).token_type_ids
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											A      						=    tokenizer.encode('あンいワ'       )
											A      						=    tokenizer.encode(''						, prefix_text='あンいワ'       )
											A      						=    tokenizer.encode('いワ'						, prefix_text='あン'       )

											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(x_token_a[1]						, x_token_a[-1]       )  # SEG token
											self.assertEqual(x_token_a[1]						, x_token_a[3]       )  # SEG token


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											A      						=    [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
											A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
											A      						=    tokenizer.batch_encode_plus(__UpperCamelCase						, padding=__UpperCamelCase       )

											# fmt: off
											A      						=    [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
											A      						=    [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
											A      						=    [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
											# fmt: on
											self.assertListEqual(x_token.input_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token.attention_mask						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.input_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.attention_mask						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											# Intentionally convert some words to accommodate character fluctuations unique to Japanese
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											# tokenizer has no padding token
											pass