


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       A    (   lowercase_					,				unittest.TestCase ):
							UpperCamelCase_   :							Optional[Any]       =LongformerTokenizer
							UpperCamelCase_   :							str       =True
							UpperCamelCase_   :							Optional[Any]       =LongformerTokenizerFast
							UpperCamelCase_   :							Any       =True


							def 			_A       (self					):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__lowercase=			[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									__lowercase=			dict(zip(__UpperCamelCase      ,				range(len(__UpperCamelCase					)					)					)					)
									__lowercase=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									__lowercase=			{'unk_token': '<unk>'}

									__lowercase=			os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']					)
									__lowercase=			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 			_A       (self      ,				**lowerCAmelCase					):
									kwargs.update(self.special_tokens_map					)
									return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**__UpperCamelCase					)


							def 			_A       (self      ,				**lowerCAmelCase					):
									kwargs.update(self.special_tokens_map					)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,				**__UpperCamelCase					)


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			'lower newer'
									__lowercase=			'lower newer'
									return input_text, output_text


							def 			_A       (self					):
									__lowercase=			self.tokenizer_class(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map					)
									__lowercase=			'lower newer'
									__lowercase=			['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									__lowercase=			tokenizer.tokenize(__UpperCamelCase					)  # , add_prefix_space=True)
									self.assertListEqual(__UpperCamelCase      ,				__UpperCamelCase					)

									__lowercase=			tokens + [tokenizer.unk_token]
									__lowercase=			[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase					)      ,				__UpperCamelCase					)


							def 			_A       (self					):
									__lowercase=			self.get_tokenizer()

									self.assertListEqual(tokenizer.encode('Hello world!'      ,				add_special_tokens=__UpperCamelCase					)      ,				[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]					)
									self.assertListEqual(
									    tokenizer.encode('Hello world! cécé herlolip 418'      ,				add_special_tokens=__UpperCamelCase					)      ,				[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]      ,				)


							@slow
							def 			_A       (self					):
									__lowercase=			self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

									__lowercase=			tokenizer.encode('sequence builders'      ,				add_special_tokens=__UpperCamelCase					)
									__lowercase=			tokenizer.encode('multi-sequence build'      ,				add_special_tokens=__UpperCamelCase					)

									__lowercase=			tokenizer.encode(
									    'sequence builders'      ,				add_special_tokens=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase					)
									__lowercase=			tokenizer.encode(
									    'sequence builders'      ,				'multi-sequence build'      ,				add_special_tokens=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase					)

									__lowercase=			tokenizer.build_inputs_with_special_tokens(__UpperCamelCase					)
									__lowercase=			tokenizer.build_inputs_with_special_tokens(__UpperCamelCase      ,				__UpperCamelCase					)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode


							def 			_A       (self					):
									__lowercase=			self.get_tokenizer()

									__lowercase=			'Encode this sequence.'
									__lowercase=			tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

									# Testing encoder arguments
									__lowercase=			tokenizer.encode(__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase					)
									__lowercase=			tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
									self.assertNotEqual(__UpperCamelCase      ,				__UpperCamelCase					)

									__lowercase=			tokenizer.encode(__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase					)
									__lowercase=			tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
									self.assertEqual(__UpperCamelCase      ,				__UpperCamelCase					)

									tokenizer.add_special_tokens({'bos_token': '<s>'}					)
									__lowercase=			tokenizer.encode(__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
									__lowercase=			tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
									self.assertNotEqual(__UpperCamelCase      ,				__UpperCamelCase					)

									# Testing spaces after special tokens
									__lowercase=			'<mask>'
									tokenizer.add_special_tokens(
									    {'mask_token': AddedToken(__UpperCamelCase      ,				lstrip=__UpperCamelCase      ,				rstrip=__UpperCamelCase					)}					)  # mask token has a left space
									__lowercase=			tokenizer.convert_tokens_to_ids(__UpperCamelCase					)

									__lowercase=			'Encode <mask> sequence'
									__lowercase=			'Encode <mask>sequence'

									__lowercase=			tokenizer.encode(__UpperCamelCase					)
									__lowercase=			encoded.index(__UpperCamelCase					)
									__lowercase=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
									self.assertEqual(__UpperCamelCase      ,				__UpperCamelCase					)

									__lowercase=			tokenizer.encode(__UpperCamelCase					)
									__lowercase=			encoded.index(__UpperCamelCase					)
									__lowercase=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
									self.assertNotEqual(__UpperCamelCase      ,				__UpperCamelCase					)


							def 			_A       (self					):
									pass


							def 			_A       (self					):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
													__lowercase=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase      ,				**__UpperCamelCase					)
													__lowercase=			self.tokenizer_class.from_pretrained(__UpperCamelCase      ,				**__UpperCamelCase					)
													__lowercase=			'A, <mask> AllenNLP sentence.'
													__lowercase=			tokenizer_r.encode_plus(__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase      ,				return_token_type_ids=__UpperCamelCase					)
													__lowercase=			tokenizer_p.encode_plus(__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase      ,				return_token_type_ids=__UpperCamelCase					)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['token_type_ids']					)      ,				sum(tokens_p['token_type_ids']					)					)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)      ,				sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)      ,				)

													__lowercase=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
													__lowercase=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['input_ids']      ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)
													self.assertSequenceEqual(tokens_r['input_ids']      ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)

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


							def 			_A       (self					):
									for trim_offsets, add_prefix_space in itertools.product([True, False]      ,				repeat=2					):
											__lowercase=			self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)

											__lowercase=			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											__lowercase=			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,				__UpperCamelCase					)

											self.assertEqual(post_processor_state['add_prefix_space']      ,				__UpperCamelCase					)
											self.assertEqual(post_processor_state['trim_offsets']      ,				__UpperCamelCase					)







							def 			_A       (self					):
									# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
									# `trim_offsets`
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
													__lowercase=			'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
													__lowercase=			f'{text_of_1_token} {text_of_1_token}'

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(len(__UpperCamelCase					) + 1, len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(len(__UpperCamelCase					) + 1, len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(len(__UpperCamelCase					), len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(len(__UpperCamelCase					), len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			f' {text}'

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(1, 1 + len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(1 + len(__UpperCamelCase					) + 1, 1 + len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, 1 + len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(1 + len(__UpperCamelCase					), 1 + len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)

													__lowercase=			self.rust_tokenizer_class.from_pretrained(
													    __UpperCamelCase      ,				use_fast=__UpperCamelCase      ,				add_prefix_space=__UpperCamelCase      ,				trim_offsets=__UpperCamelCase					)
													__lowercase=			tokenizer_r(__UpperCamelCase      ,				return_offsets_mapping=__UpperCamelCase      ,				add_special_tokens=__UpperCamelCase					)
													self.assertEqual(encoding.offset_mapping[0]      ,				(0, 1 + len(__UpperCamelCase					))					)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,				(1 + len(__UpperCamelCase					), 1 + len(__UpperCamelCase					) + 1 + len(__UpperCamelCase					))      ,				)













"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


