





'''simple docstring'''







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 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					LEDTokenizer
     _UpperCAmelCase										=					LEDTokenizerFast
     _UpperCAmelCase										=					True







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

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







     def     UpperCamelCase			( self    ,   **A__    ) ->	int:
          kwargs.update(self.special_tokens_map    )
          return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **A__    )







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







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
          return "lower newer", "lower newer"







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







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
          return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	str:
          snake_case				=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          snake_case				=			[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

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







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







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







     @require_torch
     def     UpperCamelCase			( self    ) ->	List[str]:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               snake_case				=			tokenizer(
                   ['''I am a small frog''' * 10_24, '''I am a small frog''']    ,   padding=A__    ,   truncation=A__    ,   return_tensors='''pt'''    )
               self.assertIsInstance(A__    ,   A__    )
               self.assertEqual(batch.input_ids.shape    ,   (2, 51_22)    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
          snake_case				=			['''A long paragraph for summarization.''']
          snake_case				=			[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               snake_case				=			tokenizer(A__    ,   return_tensors='''pt'''    )
               snake_case				=			tokenizer(text_target=A__    ,   return_tensors='''pt'''    )
               snake_case				=			inputs['''input_ids''']
               snake_case				=			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    ) ->	int:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               snake_case				=			['''Summary of the text.''', '''Another summary.''']
               snake_case				=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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







     def     UpperCamelCase			( self    ) ->	Tuple:
          pass







     def     UpperCamelCase			( self    ) ->	Dict:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                    snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )
                    snake_case				=			self.tokenizer_class.from_pretrained(A__    ,   **A__    )
                    snake_case				=			'''A, <mask> AllenNLP sentence.'''
                    snake_case				=			tokenizer_r.encode_plus(A__    ,   add_special_tokens=A__    ,   return_token_type_ids=A__    )
                    snake_case				=			tokenizer_p.encode_plus(A__    ,   add_special_tokens=A__    ,   return_token_type_ids=A__    )
                    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''']    )    ,   )

                    snake_case				=			tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
                    snake_case				=			tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )
                    self.assertSequenceEqual(tokens_p['''input_ids''']    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
                    self.assertSequenceEqual(tokens_r['''input_ids''']    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

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








'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 snake_case				=			[
     '''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(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

 snake_case				=			state_dict['''decoder.embed_tokens.weight'''].shape[0]

 snake_case				=			args.share_decoder_input_output_embed

 snake_case				=			[int(a     ) for i in args.conv_kernel_sizes.split(''','''     )]
 snake_case				=			SpeechaTextConfig(
     vocab_size=a					,   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(a     )					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=a					,   num_beams=5					,   max_length=200					,   use_cache=a					,   decoder_start_token_id=2					,   early_stopping=a					,   )

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "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:
  snake_case				=			make_linear_from_emb(model.model.decoder.embed_tokens     )
 else:
  snake_case				=			lm_head_weights

 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			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.')
   _lowercase       						=			parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

