
"""simple docstring"""




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



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



def        _lowerCamelCase ( __a,							__a,							__a,							__a=False    ):


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

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



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def        _lowerCamelCase ( __a,							*, __a,							__a,							__a = False    ):
		SCREAMING_SNAKE_CASE_						 = traverse_util.flatten_dict(variables['''target''']    )
		SCREAMING_SNAKE_CASE_						 = {"/".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
		SCREAMING_SNAKE_CASE_						 = "encoder/encoder/mlp/wi_0/kernel" in old
		print('''Split MLP:''',							lowerCamelCase__    )

		SCREAMING_SNAKE_CASE_						 = collections.OrderedDict()

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

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

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

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

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

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

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

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

				SCREAMING_SNAKE_CASE_						 = 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:
						SCREAMING_SNAKE_CASE_						 = old["decoder/logits_dense/kernel"].T

		return new



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = state_dict["shared.weight"]

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

		return state_dict



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = checkpoints.load_tax_checkpoint(lowerCamelCase__    )
		SCREAMING_SNAKE_CASE_						 = convert_tax_to_pytorch(
		    lowerCamelCase__,							num_layers=config.num_layers,							is_encoder_only=lowerCamelCase__,							scalable_attention=lowerCamelCase__    )
		SCREAMING_SNAKE_CASE_						 = make_state_dict(lowerCamelCase__,							lowerCamelCase__    )
		model.load_state_dict(lowerCamelCase__,							strict=lowerCamelCase__    )



def        _lowerCamelCase ( __a,							__a,							__a,							__a = False,							__a = False,							):
		SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = UMTaEncoderModel(lowerCamelCase__    )
		else:
				SCREAMING_SNAKE_CASE_						 = 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__":
						lowerCAmelCase__							=				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,
						)
						lowerCAmelCase__							=				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,
						)


"""simple docstring"""




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      TransfoXLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = [
									    '''<unk>''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''want''',
									    '''unwanted''',
									    '''wa''',
									    '''un''',
									    '''running''',
									    ''',''',
									    '''low''',
									    '''l''',
									]
									SCREAMING_SNAKE_CASE_						 = 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       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''<unk> UNwanted , running'''
									SCREAMING_SNAKE_CASE_						 = '''<unk> unwanted, running'''
									return input_text, output_text

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(vocab_file=self.vocab_file       ,       lower_case=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize('''<unk> UNwanted , running'''    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       ['''<unk>''', '''unwanted''', ''',''', '''running''']    )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       [0, 4, 8, 7]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
									SCREAMING_SNAKE_CASE_						 = [
									    '''Hello''',
									    '''(''',
									    '''bracket''',
									    ''')''',
									    '''and''',
									    '''side''',
									    '''@-@''',
									    '''scrolled''',
									    '''[''',
									    '''and''',
									    ''']''',
									    '''Henry''',
									    '''\'s''',
									    '''$''',
									    '''5''',
									    '''@,@''',
									    '''000''',
									    '''with''',
									    '''3''',
									    '''@.@''',
									    '''34''',
									    '''m''',
									    '''.''',
									    '''What''',
									    '''\'s''',
									    '''up''',
									    '''!''',
									    '''?''',
									]

									self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									self.assertEqual(tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizer()
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

									tokenizer.add_tokens(['''new1''', '''new2''']    )
									tokenizer.move_added_token('''new1'''       ,       1    )

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       original_len + 2    )
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode('''new1'''    )       ,       [1]    )
									self.assertEqual(tokenizer.decode([1]    )       ,       '''new1'''    )