



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_	(      lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	List[str]				)       ->							List[str]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str="attention"				)       ->							List[Any]:

							'''simple docstring'''






							A      						=    A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]				)
							A      						=    k_tmp.reshape(k_tmp.shape[0]			,	k_tmp.shape[1] * k_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]				)
							A      						=    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,	o_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]				)
							A      						=    q_tmp.reshape(q_tmp.shape[0]			,	q_tmp.shape[1] * q_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]				)
							A      						=    v_tmp.reshape(v_tmp.shape[0]			,	v_tmp.shape[1] * v_tmp.shape[2]				)
							return k, o, q, v






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Any=False				)       ->							str:

							'''simple docstring'''






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

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Dict				)       ->							Optional[int]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict			,	*, lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool			,	lowerCAmelCase__	:	bool = False				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    traverse_util.flatten_dict(variables['target']				)
							A      						=    {'/'.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
							A      						=    'encoder/encoder/mlp/wi_0/kernel' in old
							print('Split MLP:'			,	lowerCAmelCase__				)

							A      						=    collections.OrderedDict()

							# Shared embeddings.
							A      						=    old['token_embedder/embedding']

							# Encoder.
							for i in range(lowerCAmelCase__				):
														# Block i, layer 0 (Self Attention).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_attention_layer_norm'				)
														A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'attention'				)
														A      						=    layer_norm
														A      						=    k.T
														A      						=    o.T
														A      						=    q.T
														A      						=    v.T

														# Block i, layer 1 (MLP).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_mlp_layer_norm'				)
														A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	lowerCAmelCase__				)
														A      						=    layer_norm
														if split_mlp_wi:
																					A      						=    wi[0].T
																					A      						=    wi[1].T
														else:
																					A      						=    wi.T
														A      						=    wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					A      						=    tax_relpos_bias_lookup(
																					    lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'				).T

							A      						=    old['encoder/encoder_norm/scale']

							if not scalable_attention:
														A      						=    tax_relpos_bias_lookup(
														    lowerCAmelCase__			,	0			,	'encoder'				).T
														A      						=    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).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_self_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'self_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 1 (Cross Attention).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_cross_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'encoder_decoder_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 2 (MLP).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_mlp_layer_norm'				)
																					A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	lowerCAmelCase__				)
																					A      						=    layer_norm
																					if split_mlp_wi:
																												A      						=    wi[0].T
																												A      						=    wi[1].T
																					else:
																												A      						=    wi.T
																					A      						=    wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												A      						=    tax_relpos_bias_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'				).T

														A      						=    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:
																					A      						=    old['decoder/logits_dense/kernel'].T

							return new






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	bool				)       ->							int:

							'''simple docstring'''






							A      						=    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:
														A      						=    state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					A      						=    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.'				)
																					A      						=    state_dict['shared.weight']

							return state_dict






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	int				)       ->							str:

							'''simple docstring'''






							A      						=    checkpoints.load_tax_checkpoint(lowerCAmelCase__				)
							A      						=    convert_tax_to_pytorch(
							    lowerCAmelCase__			,	num_layers=config.num_layers			,	is_encoder_only=lowerCAmelCase__			,	scalable_attention=lowerCAmelCase__				)
							A      						=    make_state_dict(lowerCAmelCase__			,	lowerCAmelCase__				)
							model.load_state_dict(lowerCAmelCase__			,	strict=lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool = False			,	lowerCAmelCase__	:	bool = False			,	)       ->							Dict:

							'''simple docstring'''






							A      						=    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:
														A      						=    UMTaEncoderModel(lowerCAmelCase__				)
							else:
														A      						=    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__":
				__snake_case     :Any			       =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,
				)
				__snake_case     :Dict			       =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 import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     LEDTokenizer
				A_   :     Union[str, Any]								=     LEDTokenizerFast
				A_   :     Tuple								=     True


				def __UpperCamelCase						(							self      :  str       )		->     str:
											super().setUp()
											A      						=    [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


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


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  List[Any]       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Dict:
											return "lower newer", "lower newer"


				@cached_property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											return LEDTokenizer.from_pretrained('allenai/led-base-16384'       )


				@cached_property
				def __UpperCamelCase						(							self      :  str       )		->     str:
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'       )


				@require_torch
				def __UpperCamelCase						(							self      :  str       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											A      						=    [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, max_length=len(__UpperCamelCase       )						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )

																		self.assertEqual((2, 9)						, batch.input_ids.shape       )
																		self.assertEqual((2, 9)						, batch.attention_mask.shape       )
																		A      						=    batch.input_ids.tolist()[0]
																		self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIn('input_ids'						, __UpperCamelCase       )
																		self.assertIn('attention_mask'						, __UpperCamelCase       )
																		self.assertNotIn('labels'						, __UpperCamelCase       )
																		self.assertNotIn('decoder_attention_mask'						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[Any]       )		->     Union[str, Any]:
											A      						=    [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(text_target=__UpperCamelCase						, max_length=32						, padding='max_length'						, return_tensors='pt'       )
																		self.assertEqual(32						, targets['input_ids'].shape[1]       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(
																		    ['I am a small frog' * 1_024, 'I am a small frog']						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )
																		self.assertEqual(batch.input_ids.shape						, (2, 5_122)       )


				@require_torch
				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.']
											A      						=    [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    tokenizer(text_target=__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    inputs['input_ids']
																		A      						=    targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )


				@require_torch
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    ['Summary of the text.', 'Another summary.']
																		A      						=    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
																		A      						=    [[0] * len(__UpperCamelCase       ) for x in encoded_output['input_ids']]
																		A      						=    tokenizer.pad(__UpperCamelCase       )
																		self.assertSequenceEqual(outputs['global_attention_mask']						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									A      						=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    self.tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    'A, <mask> AllenNLP sentence.'
																									A      						=    tokenizer_r.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									A      						=    tokenizer_p.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									self.assertEqual(sum(tokens_r['token_type_ids']       )						, sum(tokens_p['token_type_ids']       )       )
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )						, sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )						, )

																									A      						=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																									A      						=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )
																									self.assertSequenceEqual(tokens_p['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
																									self.assertSequenceEqual(tokens_r['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )