
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 __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						List[str]=				LongformerTokenizer
    _a		:						Dict=				True
    _a		:						List[str]=				LongformerTokenizerFast
    _a		:						int=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowercase							:     Dict       	=      [
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           lowercase							:     Any       	=      dict(zip(snake_case		,range(len(snake_case      )      )      )      )
           lowercase							:     List[Any]       	=      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           lowercase							:     Tuple       	=      {"""unk_token""": """<unk>"""}

           lowercase							:     Any       	=      os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]      )
           lowercase							:     Union[str, Any]       	=      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(snake_case      ) + """\n"""      )
           with open(self.merges_file		,"""w"""		,encoding="""utf-8"""      ) as fp:
                  fp.write("""\n""".join(snake_case      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map      )
           return self.tokenizer_class.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map      )
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      """lower newer"""
           lowercase							:     Optional[int]       	=      """lower newer"""
           return input_text, output_text
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.tokenizer_class(self.vocab_file		,self.merges_file		,**self.special_tokens_map      )
           lowercase							:     int       	=      """lower newer"""
           lowercase							:     Tuple       	=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           lowercase							:     Union[str, Any]       	=      tokenizer.tokenize(snake_case      )  # , add_prefix_space=True)
           self.assertListEqual(snake_case		,snake_case      )

           lowercase							:     List[Any]       	=      tokens + [tokenizer.unk_token]
           lowercase							:     Optional[int]       	=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case      )		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.get_tokenizer()

           self.assertListEqual(tokenizer.encode("""Hello world!"""		,add_special_tokens=snake_case      )		,[0, 31414, 232, 328, 2]      )
           self.assertListEqual(
               tokenizer.encode("""Hello world! cécé herlolip 418"""		,add_special_tokens=snake_case      )		,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,)
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""      )

           lowercase							:     int       	=      tokenizer.encode("""sequence builders"""		,add_special_tokens=snake_case      )
           lowercase							:     int       	=      tokenizer.encode("""multi-sequence build"""		,add_special_tokens=snake_case      )

           lowercase							:     List[str]       	=      tokenizer.encode(
               """sequence builders"""		,add_special_tokens=snake_case		,add_prefix_space=snake_case      )
           lowercase							:     int       	=      tokenizer.encode(
               """sequence builders"""		,"""multi-sequence build"""		,add_special_tokens=snake_case		,add_prefix_space=snake_case      )

           lowercase							:     List[Any]       	=      tokenizer.build_inputs_with_special_tokens(snake_case      )
           lowercase							:     List[Any]       	=      tokenizer.build_inputs_with_special_tokens(snake_case		,snake_case      )

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.get_tokenizer()

           lowercase							:     Optional[Any]       	=      """Encode this sequence."""
           lowercase							:     List[str]       	=      tokenizer.byte_encoder[""" """.encode("""utf-8"""      )[0]]

           # Testing encoder arguments
           lowercase							:     int       	=      tokenizer.encode(snake_case		,add_special_tokens=snake_case		,add_prefix_space=snake_case      )
           lowercase							:     str       	=      tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
           self.assertNotEqual(snake_case		,snake_case      )

           lowercase							:     Union[str, Any]       	=      tokenizer.encode(snake_case		,add_special_tokens=snake_case		,add_prefix_space=snake_case      )
           lowercase							:     Union[str, Any]       	=      tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
           self.assertEqual(snake_case		,snake_case      )

           tokenizer.add_special_tokens({"""bos_token""": """<s>"""}      )
           lowercase							:     Union[str, Any]       	=      tokenizer.encode(snake_case		,add_special_tokens=snake_case      )
           lowercase							:     Tuple       	=      tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
           self.assertNotEqual(snake_case		,snake_case      )

           # Testing spaces after special tokens
           lowercase							:     Optional[int]       	=      """<mask>"""
           tokenizer.add_special_tokens(
               {"""mask_token""": AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case      )}      )  # mask token has a left space
           lowercase							:     List[Any]       	=      tokenizer.convert_tokens_to_ids(snake_case      )

           lowercase							:     Dict       	=      """Encode <mask> sequence"""
           lowercase							:     List[Any]       	=      """Encode <mask>sequence"""

           lowercase							:     str       	=      tokenizer.encode(snake_case      )
           lowercase							:     Optional[int]       	=      encoded.index(snake_case      )
           lowercase							:     Any       	=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
           self.assertEqual(snake_case		,snake_case      )

           lowercase							:     Any       	=      tokenizer.encode(snake_case      )
           lowercase							:     Union[str, Any]       	=      encoded.index(snake_case      )
           lowercase							:     Tuple       	=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
           self.assertNotEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                         lowercase							:     Union[str, Any]       	=      self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case      )
                         lowercase							:     str       	=      self.tokenizer_class.from_pretrained(snake_case		,**snake_case      )
                         lowercase							:     Dict       	=      """A, <mask> AllenNLP sentence."""
                         lowercase							:     int       	=      tokenizer_r.encode_plus(snake_case		,add_special_tokens=snake_case		,return_token_type_ids=snake_case      )
                         lowercase							:     Optional[Any]       	=      tokenizer_p.encode_plus(snake_case		,add_special_tokens=snake_case		,return_token_type_ids=snake_case      )

                         # 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							:     Optional[int]       	=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
                         lowercase							:     Any       	=      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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )
                         self.assertSequenceEqual(tokens_r["""input_ids"""]		,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )

                         self.assertSequenceEqual(
                             snake_case		,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
                         self.assertSequenceEqual(
                             snake_case		,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for trim_offsets, add_prefix_space in itertools.product([True, False]		,repeat=2      ):
                  lowercase							:     Optional[Any]       	=      self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )

                  lowercase							:     Optional[int]       	=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
                  lowercase							:     Dict       	=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

                  self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,snake_case      )

                  self.assertEqual(post_processor_state["""add_prefix_space"""]		,snake_case      )
                  self.assertEqual(post_processor_state["""trim_offsets"""]		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                         lowercase							:     Optional[int]       	=      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                         lowercase							:     List[str]       	=      f"{text_of_1_token} {text_of_1_token}"

                         lowercase							:     int       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     Dict       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(len(snake_case      ) + 1, len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     Union[str, Any]       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     Union[str, Any]       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(len(snake_case      ) + 1, len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     str       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     Dict       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(len(snake_case      ), len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     Any       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     List[Any]       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(len(snake_case      ), len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     Optional[Any]       	=      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							:     str       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     Union[str, Any]       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(1, 1 + len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(1 + len(snake_case      ) + 1, 1 + len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     Union[str, Any]       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     Dict       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, 1 + len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(1 + len(snake_case      ), 1 + len(snake_case      ) + 1 + len(snake_case      ))		,)

                         lowercase							:     str       	=      self.rust_tokenizer_class.from_pretrained(
                             snake_case		,use_fast=snake_case		,add_prefix_space=snake_case		,trim_offsets=snake_case      )
                         lowercase							:     List[str]       	=      tokenizer_r(snake_case		,return_offsets_mapping=snake_case		,add_special_tokens=snake_case      )
                         self.assertEqual(encoding.offset_mapping[0]		,(0, 1 + len(snake_case      ))      )
                         self.assertEqual(
                             encoding.offset_mapping[1]		,(1 + len(snake_case      ), 1 + len(snake_case      ) + 1 + len(snake_case      ))		,)






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""projector.weight"""]
	a										=							downstream_dict["""projector.bias"""]
	a										=							downstream_dict["""model.post_net.linear.weight"""]
	a										=							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""connector.weight"""]
	a										=							downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
		a										=							downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		a										=							downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	a										=							downstream_dict["""objective.W"""]
	return model





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

	a										=							hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""    ):
		a										=							convert_classification(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForAudioFrameClassification"""    ):
		a										=							convert_diarization(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForXVector"""    ):
		a										=							convert_xvector(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'    )

	if hf_config.use_weighted_layer_sum:
		a										=							checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(__lowerCamelCase    )
	hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      argparse.ArgumentParser()
		parser.add_argument(
		    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
		)
		parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
		parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

