'''simple docstring'''

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

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


_A			:      Optional[Any]					      =				get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
_A			:      Optional[Any]					      =				get_tests_dir('''fixtures/vocab.json''')
_A			:      int					      =				get_tests_dir('''fixtures''')







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
 def a					(      self   :	str							)  ->    Any:
  __lowerCAmelCase											=						0
 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)
  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple							)  ->    List[str]:







  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaConfig()
   __lowerCAmelCase											=						AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)

   # save in new folder
   model_config.save_pretrained(SCREAMING_SNAKE_CASE__							)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Any							)  ->    Dict:







  with tempfile.TemporaryDirectory() as tmpdirname:
   # copy relevant files
   copyfile(SCREAMING_SNAKE_CASE__      ,       os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
   copyfile(SCREAMING_SNAKE_CASE__      ,       os.path.join(SCREAMING_SNAKE_CASE__      ,       """vocab.json"""							)							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Union[str, Any]							)  ->    Optional[Any]:







  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaFeatureExtractor()
   __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

   __lowerCAmelCase											=						WavaVecaProcessor(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   # save in new folder
   processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

   # drop `processor_class` in tokenizer
   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       """r"""							) as f:
    __lowerCAmelCase											=						json.load(SCREAMING_SNAKE_CASE__							)
    config_dict.pop("""processor_class"""							)

   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       """w"""							) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__							)							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Tuple:







  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaFeatureExtractor()
   __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

   __lowerCAmelCase											=						WavaVecaProcessor(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   # save in new folder
   processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

   # drop `processor_class` in feature extractor
   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       """r"""							) as f:
    __lowerCAmelCase											=						json.load(SCREAMING_SNAKE_CASE__							)
    config_dict.pop("""processor_class"""							)

   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       """w"""							) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__							)							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    Tuple:







  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaConfig(processor_class="""Wav2Vec2Processor"""							)
   model_config.save_pretrained(SCREAMING_SNAKE_CASE__							)
   # copy relevant files
   copyfile(SCREAMING_SNAKE_CASE__      ,       os.path.join(SCREAMING_SNAKE_CASE__      ,       """vocab.json"""							)							)
   # create emtpy sample processor
   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       """w"""							) as f:
    f.write("""{}"""							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Tuple:







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

  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=SCREAMING_SNAKE_CASE__							)
  self.assertTrue(processor.special_attribute_present							)
  self.assertEqual(processor.__class__.__name__      ,       """NewProcessor"""							)

  __lowerCAmelCase											=						processor.feature_extractor
  self.assertTrue(feature_extractor.special_attribute_present							)
  self.assertEqual(feature_extractor.__class__.__name__      ,       """NewFeatureExtractor"""							)

  __lowerCAmelCase											=						processor.tokenizer
  self.assertTrue(tokenizer.special_attribute_present							)
  if is_tokenizers_available():
   self.assertEqual(tokenizer.__class__.__name__      ,       """NewTokenizerFast"""							)

   # Test we can also load the slow version
   __lowerCAmelCase											=						AutoProcessor.from_pretrained(
       """hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=SCREAMING_SNAKE_CASE__      ,       use_fast=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						new_processor.tokenizer
   self.assertTrue(new_tokenizer.special_attribute_present							)
   self.assertEqual(new_tokenizer.__class__.__name__      ,       """NewTokenizer"""							)
  else:
   self.assertEqual(tokenizer.__class__.__name__      ,       """NewTokenizer"""							)
 def a					(      self   :	Union[str, Any]							)  ->    Optional[int]:







  try:
   AutoConfig.register("""custom"""      ,       SCREAMING_SNAKE_CASE__							)
   AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   AutoTokenizer.register(SCREAMING_SNAKE_CASE__      ,       slow_tokenizer_class=SCREAMING_SNAKE_CASE__							)
   AutoProcessor.register(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   # Trying to register something existing in the Transformers library will raise an error
   with self.assertRaises(SCREAMING_SNAKE_CASE__							):
    AutoProcessor.register(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   # Now that the config is registered, it can be used as any other config with the auto-API
   __lowerCAmelCase											=						CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__							)

   with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase											=						os.path.join(SCREAMING_SNAKE_CASE__      ,       """vocab.txt"""							)
    with open(SCREAMING_SNAKE_CASE__      ,       """w"""      ,       encoding="""utf-8"""							) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
    __lowerCAmelCase											=						CustomTokenizer(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						CustomProcessor(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   with tempfile.TemporaryDirectory() as tmp_dir:
    processor.save_pretrained(SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase											=						AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)
    self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

  finally:
   if "custom" in CONFIG_MAPPING._extra_content:
    del CONFIG_MAPPING._extra_content["custom"]
   if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
    del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
   if CustomConfig in TOKENIZER_MAPPING._extra_content:
    del TOKENIZER_MAPPING._extra_content[CustomConfig]
   if CustomConfig in PROCESSOR_MAPPING._extra_content:
    del PROCESSOR_MAPPING._extra_content[CustomConfig]
 def a					(      self   :	List[Any]							)  ->    Optional[int]:







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					False







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       List[Any]      	=					"AutoFeatureExtractor"
   _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"AutoTokenizer"
   _SCREAMING_SNAKE_CASE     :       str      	=					False

  try:
   AutoConfig.register("""custom"""      ,       SCREAMING_SNAKE_CASE__							)
   AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   AutoTokenizer.register(SCREAMING_SNAKE_CASE__      ,       slow_tokenizer_class=SCREAMING_SNAKE_CASE__							)
   AutoProcessor.register(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   # If remote code is not set, the default is to use local classes.
   __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""							)
   self.assertEqual(processor.__class__.__name__      ,       """NewProcessor"""							)
   self.assertFalse(processor.special_attribute_present							)
   self.assertFalse(processor.feature_extractor.special_attribute_present							)
   self.assertFalse(processor.tokenizer.special_attribute_present							)

   # If remote code is disabled, we load the local ones.
   __lowerCAmelCase											=						AutoProcessor.from_pretrained(
       """hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=SCREAMING_SNAKE_CASE__							)
   self.assertEqual(processor.__class__.__name__      ,       """NewProcessor"""							)
   self.assertFalse(processor.special_attribute_present							)
   self.assertFalse(processor.feature_extractor.special_attribute_present							)
   self.assertFalse(processor.tokenizer.special_attribute_present							)

   # If remote is enabled, we load from the Hub.
   __lowerCAmelCase											=						AutoProcessor.from_pretrained(
       """hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=SCREAMING_SNAKE_CASE__							)
   self.assertEqual(processor.__class__.__name__      ,       """NewProcessor"""							)
   self.assertTrue(processor.special_attribute_present							)
   self.assertTrue(processor.feature_extractor.special_attribute_present							)
   self.assertTrue(processor.tokenizer.special_attribute_present							)

  finally:
   if "custom" in CONFIG_MAPPING._extra_content:
    del CONFIG_MAPPING._extra_content["custom"]
   if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
    del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
   if CustomConfig in TOKENIZER_MAPPING._extra_content:
    del TOKENIZER_MAPPING._extra_content[CustomConfig]
   if CustomConfig in PROCESSOR_MAPPING._extra_content:
    del PROCESSOR_MAPPING._extra_content[CustomConfig]
 def a					(      self   :	Tuple							)  ->    List[str]:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
  self.assertEqual(processor.__class__.__name__      ,       """BertTokenizerFast"""							)





 def a					(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""							)
  self.assertEqual(processor.__class__.__name__      ,       """ConvNextImageProcessor"""							)







@is_staging_test
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict      	=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
 @classmethod
 def a					(      cls   :	Optional[Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						TOKEN
  HfFolder.save_token(SCREAMING_SNAKE_CASE__							)
 @classmethod
 def a					(      cls   :	str							)  ->    str:







  try:
   delete_repo(token=cls._token      ,       repo_id="""test-processor"""							)
  except HTTPError:
   pass

  try:
   delete_repo(token=cls._token      ,       repo_id="""valid_org/test-processor-org"""							)
  except HTTPError:
   pass

  try:
   delete_repo(token=cls._token      ,       repo_id="""test-dynamic-processor"""							)
  except HTTPError:
   pass
 def a					(      self   :	Dict							)  ->    str:
  __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)
  with tempfile.TemporaryDirectory() as tmp_dir:
   processor.save_pretrained(
       os.path.join(SCREAMING_SNAKE_CASE__      ,       """test-processor"""							)      ,       push_to_hub=SCREAMING_SNAKE_CASE__      ,       use_auth_token=self._token							)

   __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""							)
   for k, v in processor.feature_extractor.__dict__.items():
    self.assertEqual(SCREAMING_SNAKE_CASE__      ,       getattr(new_processor.feature_extractor      ,       SCREAMING_SNAKE_CASE__							)							)
   self.assertDictEqual(new_processor.tokenizer.get_vocab()      ,       processor.tokenizer.get_vocab()							)
 def a					(      self   :	int							)  ->    int:
  __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  with tempfile.TemporaryDirectory() as tmp_dir:
   processor.save_pretrained(
       os.path.join(SCREAMING_SNAKE_CASE__      ,       """test-processor-org"""							)      ,       push_to_hub=SCREAMING_SNAKE_CASE__      ,       use_auth_token=self._token      ,       organization="""valid_org"""      ,       )

   __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""							)
   for k, v in processor.feature_extractor.__dict__.items():
    self.assertEqual(SCREAMING_SNAKE_CASE__      ,       getattr(new_processor.feature_extractor      ,       SCREAMING_SNAKE_CASE__							)							)
   self.assertDictEqual(new_processor.tokenizer.get_vocab()      ,       processor.tokenizer.get_vocab()							)





 def a					(      self   :	Union[str, Any]							)  ->    Tuple:
  CustomFeatureExtractor.register_for_auto_class()
  CustomTokenizer.register_for_auto_class()
  CustomProcessor.register_for_auto_class()

  __lowerCAmelCase											=						CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__							)

  with tempfile.TemporaryDirectory() as tmp_dir:
   __lowerCAmelCase											=						os.path.join(SCREAMING_SNAKE_CASE__      ,       """vocab.txt"""							)
   with open(SCREAMING_SNAKE_CASE__      ,       """w"""      ,       encoding="""utf-8"""							) as vocab_writer:
    vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
   __lowerCAmelCase											=						CustomTokenizer(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						CustomProcessor(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

  with tempfile.TemporaryDirectory() as tmp_dir:
   create_repo(f"""{USER}/test-dynamic-processor"""      ,       token=self._token							)
   __lowerCAmelCase											=						Repository(SCREAMING_SNAKE_CASE__      ,       clone_from=f"""{USER}/test-dynamic-processor"""      ,       token=self._token							)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

   # This has added the proper auto_map field to the feature extractor config
   self.assertDictEqual(
       processor.feature_extractor.auto_map      ,       {
           """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
           """AutoProcessor""": """custom_processing.CustomProcessor""",
       }      ,       )

   # This has added the proper auto_map field to the tokenizer config
   with open(os.path.join(SCREAMING_SNAKE_CASE__      ,       """tokenizer_config.json"""							)							) as f:
    __lowerCAmelCase											=						json.load(SCREAMING_SNAKE_CASE__							)
   self.assertDictEqual(
       tokenizer_config["""auto_map"""]      ,       {
           """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
           """AutoProcessor""": """custom_processing.CustomProcessor""",
       }      ,       )

   # The code has been copied from fixtures
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__      ,       """custom_feature_extraction.py"""							)							)							)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__      ,       """custom_tokenization.py"""							)							)							)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__      ,       """custom_processing.py"""							)							)							)

   repo.push_to_hub()

  __lowerCAmelCase											=						AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""      ,       trust_remote_code=SCREAMING_SNAKE_CASE__							)
  # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
  self.assertEqual(new_processor.__class__.__name__      ,       """CustomProcessor"""							)








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
