'''simple docstring'''


import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
       from test_module.custom_tokenization_fast import CustomTokenizerFast





class       _a    (		unittest.TestCase      ):


  """simple docstring"""

  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       0




  @slow
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
      self.assertIsNotNone(lowercase_      )
      self.assertIsInstance(lowercase_   ,				(BertTokenizer, BertTokenizerFast)      )
      self.assertGreater(len(lowercase_      )   ,				0      )

    for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
      self.assertIsNotNone(lowercase_      )
      self.assertIsInstance(lowercase_   ,				(GPTaTokenizer, GPTaTokenizerFast)      )
      self.assertGreater(len(lowercase_      )   ,				0      )




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
    self.assertIsInstance(lowercase_   ,				(BertTokenizer, BertTokenizerFast)      )
    self.assertEqual(tokenizer.vocab_size   ,				12      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
    self.assertIsInstance(lowercase_   ,				(RobertaTokenizer, RobertaTokenizerFast)      )
    self.assertEqual(tokenizer.vocab_size   ,				20      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       AutoConfig.from_pretrained(lowercase_      )
    self.assertIsInstance(lowercase_   ,				lowercase_      )
    # Check that tokenizer_type ≠ model_type
    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				config=lowercase_      )
    self.assertIsInstance(lowercase_   ,				(BertTokenizer, BertTokenizerFast)      )
    self.assertEqual(tokenizer.vocab_size   ,				12      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    with tempfile.TemporaryDirectory() as tmp_dir:
      shutil.copy("""./tests/fixtures/vocab.txt"""   ,				os.path.join(lowercase_   ,				"""vocab.txt"""      )      )

      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				tokenizer_type="""bert"""   ,				use_fast=lowercase_      )
      self.assertIsInstance(lowercase_   ,				lowercase_      )

    with tempfile.TemporaryDirectory() as tmp_dir:
      shutil.copy("""./tests/fixtures/vocab.json"""   ,				os.path.join(lowercase_   ,				"""vocab.json"""      )      )
      shutil.copy("""./tests/fixtures/merges.txt"""   ,				os.path.join(lowercase_   ,				"""merges.txt"""      )      )

      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				tokenizer_type="""gpt2"""   ,				use_fast=lowercase_      )
      self.assertIsInstance(lowercase_   ,				lowercase_      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    with tempfile.TemporaryDirectory() as tmp_dir:
      shutil.copy("""./tests/fixtures/vocab.txt"""   ,				os.path.join(lowercase_   ,				"""vocab.txt"""      )      )

      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				tokenizer_type="""bert"""      )
      self.assertIsInstance(lowercase_   ,				lowercase_      )

    with tempfile.TemporaryDirectory() as tmp_dir:
      shutil.copy("""./tests/fixtures/vocab.json"""   ,				os.path.join(lowercase_   ,				"""vocab.json"""      )      )
      shutil.copy("""./tests/fixtures/merges.txt"""   ,				os.path.join(lowercase_   ,				"""merges.txt"""      )      )

      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				tokenizer_type="""gpt2"""      )
      self.assertIsInstance(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    with pytest.raises(lowercase_      ):
      AutoTokenizer.from_pretrained("""./"""   ,				tokenizer_type="""xxx"""      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
      lowercase_     =       tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""      )
      self.assertIsInstance(lowercase_   ,				(BertTokenizer, BertTokenizerFast)      )

      if isinstance(lowercase_   ,				lowercase_      ):
        self.assertEqual(tokenizer.basic_tokenizer.do_lower_case   ,				lowercase_      )
      else:
        self.assertEqual(tokenizer.do_lower_case   ,				lowercase_      )

      self.assertEqual(tokenizer.model_max_length   ,				512      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
      with self.assertRaisesRegex(
          lowercase_   ,				"""julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"""   ,				):
        lowercase_     =       tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""      )




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       TOKENIZER_MAPPING.values()
    lowercase_     =       []

    for slow_tok, fast_tok in tokenizers:
      if slow_tok is not None:
        tokenizer_names.append(slow_tok.__name__      )

      if fast_tok is not None:
        tokenizer_names.append(fast_tok.__name__      )

    for tokenizer_name in tokenizer_names:
      # must find the right class
      tokenizer_class_from_name(lowercase_      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""   ,				use_fast=lowercase_      )   ,				lowercase_      )
    self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""      )   ,				lowercase_      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained("""distilbert-base-uncased"""   ,				do_lower_case=lowercase_      )
    lowercase_     =       """Hello, world. How are you?"""
    lowercase_     =       tokenizer.tokenize(lowercase_      )
    self.assertEqual("""[UNK]"""   ,				tokens[0]      )

    lowercase_     =       AutoTokenizer.from_pretrained("""microsoft/mpnet-base"""   ,				do_lower_case=lowercase_      )
    lowercase_     =       tokenizer.tokenize(lowercase_      )
    self.assertEqual("""[UNK]"""   ,				tokens[0]      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""      )
    self.assertEqual(type(lowercase_      )   ,				lowercase_      )
    self.assertEqual(tokenizer.model_max_length   ,				512      )
    self.assertEqual(tokenizer.vocab_size   ,				30_000      )
    self.assertEqual(tokenizer.unk_token   ,				"""[UNK]"""      )
    self.assertEqual(tokenizer.padding_side   ,				"""right"""      )
    self.assertEqual(tokenizer.truncation_side   ,				"""right"""      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
    self.assertIsInstance(lowercase_   ,				(BertTokenizer, BertTokenizerFast)      )
    with tempfile.TemporaryDirectory() as tmp_dir:
      tokenizer.save_pretrained(lowercase_      )
      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )

    self.assertIsInstance(lowercase_   ,				tokenizer.__class__      )
    self.assertEqual(tokenizera.vocab_size   ,				12      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained("""ctrl"""      )
    # There is no fast CTRL so this always gives us a slow tokenizer.
    self.assertIsInstance(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       get_tokenizer_config("""bert-base-cased"""      )
    lowercase_     =       config.pop("""_commit_hash"""   ,				lowercase_      )
    # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
    self.assertEqual(lowercase_   ,				{"""do_lower_case""": False}      )

    # This model does not have a tokenizer_config so we get back an empty dict.
    lowercase_     =       get_tokenizer_config(lowercase_      )
    self.assertDictEqual(lowercase_   ,				{}      )

    # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
    lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
    with tempfile.TemporaryDirectory() as tmp_dir:
      tokenizer.save_pretrained(lowercase_      )
      lowercase_     =       get_tokenizer_config(lowercase_      )

    # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
    self.assertEqual(config["""tokenizer_class"""]   ,				"""BertTokenizer"""      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    try:
      AutoConfig.register("""custom"""   ,				lowercase_      )

      AutoTokenizer.register(lowercase_   ,				slow_tokenizer_class=lowercase_      )
      # Trying to register something existing in the Transformers library will raise an error
      with self.assertRaises(lowercase_      ):
        AutoTokenizer.register(lowercase_   ,				slow_tokenizer_class=lowercase_      )

      lowercase_     =       CustomTokenizer.from_pretrained(lowercase_      )
      with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(lowercase_      )

        lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
        self.assertIsInstance(lowercase_   ,				lowercase_      )

    finally:
      if "custom" in CONFIG_MAPPING._extra_content:
        del CONFIG_MAPPING._extra_content["custom"]
      if CustomConfig in TOKENIZER_MAPPING._extra_content:
        del TOKENIZER_MAPPING._extra_content[CustomConfig]




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    try:
      AutoConfig.register("""custom"""   ,				lowercase_      )

      # Can register in two steps
      AutoTokenizer.register(lowercase_   ,				slow_tokenizer_class=lowercase_      )
      self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,				(CustomTokenizer, None)      )
      AutoTokenizer.register(lowercase_   ,				fast_tokenizer_class=lowercase_      )
      self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,				(CustomTokenizer, CustomTokenizerFast)      )

      del TOKENIZER_MAPPING._extra_content[CustomConfig]
      # Can register in one step
      AutoTokenizer.register(
          lowercase_   ,				slow_tokenizer_class=lowercase_   ,				fast_tokenizer_class=lowercase_      )
      self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,				(CustomTokenizer, CustomTokenizerFast)      )

      # Trying to register something existing in the Transformers library will raise an error
      with self.assertRaises(lowercase_      ):
        AutoTokenizer.register(lowercase_   ,				fast_tokenizer_class=lowercase_      )

      # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
      # and that model does not have a tokenizer.json
      with tempfile.TemporaryDirectory() as tmp_dir:
        lowercase_     =       BertTokenizerFast.from_pretrained(lowercase_      )
        bert_tokenizer.save_pretrained(lowercase_      )
        lowercase_     =       CustomTokenizerFast.from_pretrained(lowercase_      )

      with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(lowercase_      )

        lowercase_     =       AutoTokenizer.from_pretrained(lowercase_      )
        self.assertIsInstance(lowercase_   ,				lowercase_      )

        lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				use_fast=lowercase_      )
        self.assertIsInstance(lowercase_   ,				lowercase_      )

    finally:
      if "custom" in CONFIG_MAPPING._extra_content:
        del CONFIG_MAPPING._extra_content["custom"]
      if CustomConfig in TOKENIZER_MAPPING._extra_content:
        del TOKENIZER_MAPPING._extra_content[CustomConfig]




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    with self.assertRaises(lowercase_      ):
      lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""      )
    # If remote code is disabled, we can't load this config.
    with self.assertRaises(lowercase_      ):
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_      )

    lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_      )
    self.assertTrue(tokenizer.special_attribute_present      )
    # Test tokenizer can be reloaded.
    with tempfile.TemporaryDirectory() as tmp_dir:
      tokenizer.save_pretrained(lowercase_      )
      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				trust_remote_code=lowercase_      )
    self.assertTrue(reloaded_tokenizer.special_attribute_present      )

    if is_tokenizers_available():
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )
      self.assertEqual(reloaded_tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )

      # Test we can also load the slow version
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_   ,				use_fast=lowercase_      )
      self.assertTrue(tokenizer.special_attribute_present      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      # Test tokenizer can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(lowercase_      )
        lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				trust_remote_code=lowercase_   ,				use_fast=lowercase_      )
      self.assertEqual(reloaded_tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      self.assertTrue(reloaded_tokenizer.special_attribute_present      )
    else:
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      self.assertEqual(reloaded_tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )




  @require_tokenizers
  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    class       _a    (		__a      ):


      """simple docstring"""

      A_    							=						False






    class       _a    (		__a      ):


      """simple docstring"""

      A_    							=						NewTokenizer
      A_    							=						False

    try:
      AutoConfig.register("""custom"""   ,				lowercase_      )
      AutoTokenizer.register(lowercase_   ,				slow_tokenizer_class=lowercase_      )
      AutoTokenizer.register(lowercase_   ,				fast_tokenizer_class=lowercase_      )
      # If remote code is not set, the default is to use local
      lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )
      self.assertFalse(tokenizer.special_attribute_present      )
      lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""   ,				use_fast=lowercase_      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      self.assertFalse(tokenizer.special_attribute_present      )

      # If remote code is disabled, we load the local one.
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )
      self.assertFalse(tokenizer.special_attribute_present      )
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_   ,				use_fast=lowercase_      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      self.assertFalse(tokenizer.special_attribute_present      )

      # If remote is enabled, we load from the Hub
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )
      self.assertTrue(tokenizer.special_attribute_present      )
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer"""   ,				trust_remote_code=lowercase_   ,				use_fast=lowercase_      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
      self.assertTrue(tokenizer.special_attribute_present      )

    finally:
      if "custom" in CONFIG_MAPPING._extra_content:
        del CONFIG_MAPPING._extra_content["custom"]
      if CustomConfig in TOKENIZER_MAPPING._extra_content:
        del TOKENIZER_MAPPING._extra_content[CustomConfig]




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained(
        """hf-internal-testing/test_dynamic_tokenizer_legacy"""   ,				trust_remote_code=lowercase_      )
    self.assertTrue(tokenizer.special_attribute_present      )
    if is_tokenizers_available():
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizerFast"""      )

      # Test we can also load the slow version
      lowercase_     =       AutoTokenizer.from_pretrained(
          """hf-internal-testing/test_dynamic_tokenizer_legacy"""   ,				trust_remote_code=lowercase_   ,				use_fast=lowercase_      )
      self.assertTrue(tokenizer.special_attribute_present      )
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )
    else:
      self.assertEqual(tokenizer.__class__.__name__   ,				"""NewTokenizer"""      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    with self.assertRaisesRegex(
        lowercase_   ,				"""bert-base is not a local folder and is not a valid model identifier"""      ):
      lowercase_     =       AutoTokenizer.from_pretrained("""bert-base"""      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    with self.assertRaisesRegex(
        lowercase_   ,				R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""      ):
      lowercase_     =       AutoTokenizer.from_pretrained(lowercase_   ,				revision="""aaaaaa"""      )





  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""      )
    with RequestCounter() as counter:
      lowercase_     =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""      )
      self.assertEqual(counter.get_request_count   ,				0      )
      self.assertEqual(counter.head_request_count   ,				1      )
      self.assertEqual(counter.other_request_count   ,				0      )


'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

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

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	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(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

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

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


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

