

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
lowercase_     =       logging.getLogger(__name__)

lowercase_     =       int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

lowercase_     =       {
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
lowercase_     =       'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'
def 					a				(      A__     :       Any					)   ->				Optional[Any]:





      """simple docstring"""



      np.random.seed(args.seed					)
      torch.manual_seed(args.seed					)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed					)
def 					a				(      A__     :       Optional[Any]				,       A__     :       List[str]				,       A__     :       str				,       A__     :       Union[str, Any]					)   ->				Optional[int]:





      """simple docstring"""



      if args.temperature > 0.7:
            logger.info('CTRL typically works better with lower temperatures (and lower top_k).'					)

      _lowercase     	=tokenizer.encode(A__				,       add_special_tokens=A__					)
      if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()					):
            logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'					)
      return prompt_text
def 					a				(      A__     :       Dict				,       A__     :       Any				,       A__     :       List[Any]				,       A__     :       Tuple					)   ->				int:





      """simple docstring"""



      _lowercase     	=hasattr(model.config				,       'use_lang_emb'					) and model.config.use_lang_emb
      if hasattr(model.config				,       'lang2id'					) and use_lang_emb:
            _lowercase     	=model.config.langaid.keys()
            if args.xlm_language in available_languages:
                  _lowercase     	=args.xlm_language
            else:
                  _lowercase     	=None
                  while language not in available_languages:
                        _lowercase     	=input('Using XLM. Select language in ' + str(list(A__					)					) + ' >>> '					)

            _lowercase     	=model.config.langaid[language]
            # kwargs["language"] = tokenizer.lang2id[language]

      # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
      # XLM masked-language modeling (MLM) models need masked token
      # is_xlm_mlm = "mlm" in args.model_name_or_path
      # if is_xlm_mlm:
      #     kwargs["mask_token_id"] = tokenizer.mask_token_id

      return prompt_text
def 					a				(      A__     :       int				,       A__     :       List[str]				,       A__     :       Tuple				,       A__     :       Tuple					)   ->				List[str]:





      """simple docstring"""



      _lowercase     	=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      _lowercase     	=prefix + prompt_text
      return prompt_text
def 					a				(      A__     :       int				,       A__     :       Optional[int]				,       A__     :       int				,       A__     :       int					)   ->				List[str]:





      """simple docstring"""



      _lowercase     	=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      _lowercase     	=prefix + prompt_text
      return prompt_text


lowercase_     =       {
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}
def 					a				(      A__     :       Tuple				,       A__     :       Dict					)   ->				Union[str, Any]:





      """simple docstring"""



      if length < 0 and max_sequence_length > 0:
            _lowercase     	=max_sequence_length
      elif 0 < max_sequence_length < length:
            _lowercase     	=max_sequence_length  # No generation bigger than model size
      elif length < 0:
            _lowercase     	=MAX_LENGTH  # avoid infinite loop
      return length
def 					a				(      A__     :       Union[str, Any]					)   ->				int:





      """simple docstring"""



      _lowercase     	=None
      if hasattr(A__				,       'hidden_size'					):
            _lowercase     	=model_config.hidden_size
      elif hasattr(A__				,       'n_embed'					):
            _lowercase     	=model_config.n_embed
      elif hasattr(A__				,       'n_embd'					):
            _lowercase     	=model_config.n_embd

      _lowercase     	=None
      if hasattr(A__				,       'num_attention_heads'					):
            _lowercase     	=model_config.num_attention_heads
      elif hasattr(A__				,       'n_head'					):
            _lowercase     	=model_config.n_head

      if embedding_size is None or num_head is None or num_head == 0:
            raise ValueError('Check the model config'					)

      _lowercase     	=int(embedding_size / num_head					)
      if hasattr(A__				,       'n_layer'					):
            _lowercase     	=model_config.n_layer
      elif hasattr(A__				,       'num_hidden_layers'					):
            _lowercase     	=model_config.num_hidden_layers
      else:
            raise ValueError('Number of hidden layers couldn\'t be determined from the model config'					)

      return num_layer, num_head, num_embedding_size_per_head
def 					a				(      A__     :       Union[str, Any]				,       A__     :       int				,       A__     :       List[Any]					)   ->				Optional[int]:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=sparse_model_config(model.config					)
      if model.config.model_type == "bloom":
            _lowercase     	=tuple(
                (
                    torch.empty(int(num_attention_heads * batch_size					)				,       A__				,       A__					)
                    .to(model.dtype					)
                    .to(model.device					),
                    torch.empty(int(num_attention_heads * batch_size					)				,       A__				,       A__					)
                    .to(model.dtype					)
                    .to(model.device					),
                )
                for _ in range(A__					)					)
      else:
            _lowercase     	=tuple(
                (
                    torch.empty(A__				,       A__				,       A__				,       A__					)
                    .to(model.dtype					)
                    .to(model.device					),
                    torch.empty(A__				,       A__				,       A__				,       A__					)
                    .to(model.dtype					)
                    .to(model.device					),
                )
                for _ in range(A__					)					)
      return past_key_values
def 					a				(      A__     :       List[str]				,       A__     :       str				,       A__     :       int					)   ->				Dict:





      """simple docstring"""



      _lowercase     	=len(A__					)
      _lowercase     	=tokenizer.batch_encode_plus(A__				,       return_tensors='pt'					)
      _lowercase     	=dummy_input.to(model.device					)
      if model.config.use_cache:
            _lowercase     	=generate_past_key_values(A__				,       A__				,       1					)
      _lowercase     	=torch.cat(
          [
              torch.zeros(dummy_input['attention_mask'].shape[0]				,       1					)
              .to(dummy_input['attention_mask'].dtype					)
              .to(model.device					),
              dummy_input['attention_mask'],
          ]				,       -1				,       )
      return dummy_input





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):
  _a						      =  ("""_optimized""", """_default""")




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=optimized
        _lowercase     	=default




  def __call__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			str:
        '''simple docstring'''






        if kwargs["past_key_values"] is None and self._default.config.use_cache:
              _lowercase     	=generate_past_key_values(self._default							,       kwargs['input_ids'].shape[0]							,       0 )
        kwargs.pop('position_ids'							,       lowerCAmelCase )
        for k in list(kwargs.keys() ):
              if kwargs[k] is None or isinstance(kwargs[k]							,       lowerCAmelCase ):
                    kwargs.pop(lowerCAmelCase )
        _lowercase     	=self._optimized(**lowerCAmelCase )
        _lowercase     	=outputs[0]
        _lowercase     	=outputs[1]
        _lowercase     	=CausalLMOutputWithPast(
            loss=lowerCAmelCase							,       logits=lowerCAmelCase							,       past_key_values=lowerCAmelCase							,       hidden_states=lowerCAmelCase							,       attentions=lowerCAmelCase							,       )
        return fixed_output




  def __getattr__(    self							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        return getattr(self._default							,       lowerCAmelCase )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=None							,       lowerCAmelCase=None							,       lowerCAmelCase=None							,       **lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        return self._default.prepare_inputs_for_generation(
            lowerCAmelCase							,       past_key_values=lowerCAmelCase							,       inputs_embeds=lowerCAmelCase							,       use_cache=lowerCAmelCase							,       **lowerCAmelCase )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Tuple[Tuple[torch.Tensor]]:
        '''simple docstring'''






        return self._default._reorder_cache(lowerCAmelCase							,       lowerCAmelCase )
def 					a				(      )   ->				Dict:





      """simple docstring"""



      _lowercase     	=argparse.ArgumentParser()
      parser.add_argument(
          '--model_type'				,       default=A__				,       type=A__				,       required=A__				,       help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()					)				,       )
      parser.add_argument(
          '--model_name_or_path'				,       default=A__				,       type=A__				,       required=A__				,       help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()					)				,       )

      parser.add_argument('--prompt'				,       type=A__				,       default=''					)
      parser.add_argument('--length'				,       type=A__				,       default=20					)
      parser.add_argument('--stop_token'				,       type=A__				,       default=A__				,       help='Token at which text generation is stopped'					)

      parser.add_argument(
          '--temperature'				,       type=A__				,       default=1.0				,       help='temperature of 1.0 has no effect, lower tend toward greedy sampling'				,       )
      parser.add_argument(
          '--repetition_penalty'				,       type=A__				,       default=1.0				,       help='primarily useful for CTRL model; in that case, use 1.2'					)
      parser.add_argument('--k'				,       type=A__				,       default=0					)
      parser.add_argument('--p'				,       type=A__				,       default=0.9					)

      parser.add_argument('--prefix'				,       type=A__				,       default=''				,       help='Text added prior to input.'					)
      parser.add_argument('--padding_text'				,       type=A__				,       default=''				,       help='Deprecated, the use of `--prefix` is preferred.'					)
      parser.add_argument('--xlm_language'				,       type=A__				,       default=''				,       help='Optional language when used with the XLM model.'					)

      parser.add_argument('--seed'				,       type=A__				,       default=42				,       help='random seed for initialization'					)
      parser.add_argument('--no_cuda'				,       action='store_true'				,       help='Avoid using CUDA when available'					)
      parser.add_argument('--num_return_sequences'				,       type=A__				,       default=1				,       help='The number of samples to generate.'					)
      parser.add_argument(
          '--fp16'				,       action='store_true'				,       help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'				,       )
      parser.add_argument('--jit'				,       action='store_true'				,       help='Whether or not to use jit trace to accelerate inference'					)
      _lowercase     	=parser.parse_args()

      _lowercase     	=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
      _lowercase     	=0 if args.no_cuda else torch.cuda.device_count()

      logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''					)

      set_seed(A__					)

      # Initialize the model and tokenizer
      try:
            _lowercase     	=args.model_type.lower()
            _lowercase	,	_lowercase     	=MODEL_CLASSES[args.model_type]
      except KeyError:
            raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'					)

      _lowercase     	=tokenizer_class.from_pretrained(args.model_name_or_path					)
      if tokenizer.pad_token is None:
            _lowercase     	=tokenizer.eos_token
      _lowercase     	=model_class.from_pretrained(args.model_name_or_path					)
      model.to(args.device					)

      if args.fpaa:
            model.half()
      _lowercase     	=getattr(model.config				,       'max_position_embeddings'				,       0					)
      _lowercase     	=adjust_length_to_model(args.length				,       max_sequence_length=A__					)
      logger.info(A__					)

      _lowercase     	=args.prompt if args.prompt else input('Model prompt >>> '					)

      # Different models need different input formatting and/or extra arguments
      _lowercase     	=args.model_type in PREPROCESSING_FUNCTIONS.keys()
      if requires_preprocessing:
            _lowercase     	=PREPROCESSING_FUNCTIONS.get(args.model_type					)
            _lowercase     	=prepare_input(A__				,       A__				,       A__				,       A__					)

            if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                  _lowercase     	={'add_space_before_punct_symbol': True}
            else:
                  _lowercase     	={}

            _lowercase     	=tokenizer.encode(
                A__				,       add_special_tokens=A__				,       return_tensors='pt'				,       **A__					)
      else:
            _lowercase     	=args.prefix if args.prefix else args.padding_text
            _lowercase     	=tokenizer.encode(prefix + prompt_text				,       add_special_tokens=A__				,       return_tensors='pt'					)
      _lowercase     	=encoded_prompt.to(args.device					)

      if encoded_prompt.size()[-1] == 0:
            _lowercase     	=None
      else:
            _lowercase     	=encoded_prompt

      if args.jit:
            _lowercase     	=['enable jit']
            _lowercase     	=prepare_jit_inputs(A__				,       A__				,       A__					)
            torch._C._jit_set_texpr_fuser_enabled(A__					)
            _lowercase     	=False
            if hasattr(A__				,       'forward'					):
                  _lowercase     	=inspect.signature(model.forward					)
            else:
                  _lowercase     	=inspect.signature(model.__call__					)
            _lowercase     	=tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(A__				,       A__					) is not None					)
            _lowercase     	=torch.jit.trace(A__				,       A__				,       strict=A__					)
            _lowercase     	=torch.jit.freeze(traced_model.eval()					)
            traced_model(*A__					)
            traced_model(*A__					)

            _lowercase     	=_ModelFallbackWrapper(A__				,       A__					)

      _lowercase     	=model.generate(
          input_ids=A__				,       max_length=args.length + len(encoded_prompt[0]					)				,       temperature=args.temperature				,       top_k=args.k				,       top_p=args.p				,       repetition_penalty=args.repetition_penalty				,       do_sample=A__				,       num_return_sequences=args.num_return_sequences				,       )

      # Remove the batch dimension when returning multiple sequences
      if len(output_sequences.shape					) > 2:
            output_sequences.squeeze_()

      _lowercase     	=[]

      for generated_sequence_idx, generated_sequence in enumerate(A__					):
            print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''					)
            _lowercase     	=generated_sequence.tolist()

            # Decode text
            _lowercase     	=tokenizer.decode(A__				,       clean_up_tokenization_spaces=A__					)

            # Remove all text after the stop token
            _lowercase     	=text[: text.find(args.stop_token					) if args.stop_token else None]

            # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
            _lowercase     	=(
                prompt_text + text[len(tokenizer.decode(encoded_prompt[0]				,       clean_up_tokenization_spaces=A__					)					) :]
            )

            generated_sequences.append(A__					)
            print(A__					)

      return generated_sequences


if __name__ == "__main__":
  main()





from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=7							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=99							,       lowerCAmelCase=32							,       lowerCAmelCase=2							,       lowerCAmelCase=4							,       lowerCAmelCase=37							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=512							,       lowerCAmelCase=16							,       lowerCAmelCase=2							,       lowerCAmelCase=0.02							,       lowerCAmelCase=3							,       lowerCAmelCase=4							,       lowerCAmelCase=None							,       )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=parent
        _lowercase     	=13
        _lowercase     	=7
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=99
        _lowercase     	=32
        _lowercase     	=2
        _lowercase     	=4
        _lowercase     	=37
        _lowercase     	='gelu'
        _lowercase     	=0.1
        _lowercase     	=0.1
        _lowercase     	=512
        _lowercase     	=16
        _lowercase     	=False
        _lowercase     	=-1
        _lowercase     	=True
        _lowercase     	=2
        _lowercase     	=0.02
        _lowercase     	=3
        _lowercase     	=4
        _lowercase     	=None




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size )

        _lowercase     	=None
        if self.use_input_mask:
              _lowercase     	=random_attention_mask([self.batch_size, self.seq_length] )

        _lowercase     	=None
        if self.use_token_type_ids:
              _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.type_vocab_size )

        _lowercase     	=None
        _lowercase     	=None
        _lowercase     	=None
        if self.use_labels:
              _lowercase     	=ids_tensor([self.batch_size]							,       self.type_sequence_label_size )
              _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels )

        _lowercase     	=DebertaConfig(
            vocab_size=self.vocab_size							,       hidden_size=self.hidden_size							,       num_hidden_layers=self.num_hidden_layers							,       num_attention_heads=self.num_attention_heads							,       intermediate_size=self.intermediate_size							,       hidden_act=self.hidden_act							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       type_vocab_size=self.type_vocab_size							,       relative_attention=self.relative_attention							,       max_relative_positions=self.max_relative_positions							,       position_biased_input=self.position_biased_input							,       initializer_range=self.initializer_range							,       return_dict=lowerCAmelCase							,       )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel(config=lowerCAmelCase )
        _lowercase     	={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _lowercase     	=[input_ids, input_mask]
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=model(lowerCAmelCase )

        self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForMaskedLM(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForSequenceClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForTokenClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForQuestionAnswering(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.start_logits.shape							,       (self.batch_size, self.seq_length) )
        self.parent.assertEqual(result.end_logits.shape							,       (self.batch_size, self.seq_length) )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        (
            (
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	
        )     	=config_and_inputs
        _lowercase     	={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_tf
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {
          """feature-extraction""": TFDebertaModel,
          """fill-mask""": TFDebertaForMaskedLM,
          """question-answering""": TFDebertaForQuestionAnswering,
          """text-classification""": TFDebertaForSequenceClassification,
          """token-classification""": TFDebertaForTokenClassification,
          """zero-shot""": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       hidden_size=37 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        self.assertIsNotNone(lowerCAmelCase )





@require_tf
class 			__lowerCAmelCase					( unittest.TestCase							):




  @unittest.skip(reason='Model not available yet' )
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        pass




  @slow
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        _lowercase     	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
        _lowercase     	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
        _lowercase     	=model(lowerCAmelCase							,       attention_mask=lowerCAmelCase )[0]

        _lowercase     	=tf.constant(
            [
                [
                    [-0.59855896, -0.80552566, -0.8462135],
                    [1.4484025, -0.93483794, -0.80593085],
                    [0.3122741, 0.00316059, -1.4131377],
                ]
            ] )
        tf.debugging.assert_near(output[:, 1:4, 1:4]							,       lowerCAmelCase							,       atol=1e-4 )


