

'''simple docstring'''






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,
)
snake_case_       =       logging.getLogger(__name__)

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

snake_case_       =       {
    """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
snake_case_       =       """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Dict    )							->   Union[str, 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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   Optional[Any]:
      """simple docstring"""

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

      SCREAMING_SNAKE_CASE_      :			int     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]  ,  SCREAMING_SNAKE_CASE_     :	List[Any]    )							->   Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       hasattr(model.config  ,  "use_lang_emb"    ) and model.config.use_lang_emb
      if hasattr(model.config  ,  "lang2id"    ) and use_lang_emb:
            SCREAMING_SNAKE_CASE_      :			str     =       model.config.langaid.keys()
            if args.xlm_language in available_languages:
                  SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       args.xlm_language
            else:
                  SCREAMING_SNAKE_CASE_      :			str     =       None
                  while language not in available_languages:
                        SCREAMING_SNAKE_CASE_      :			Optional[int]     =       input("Using XLM. Select language in " + str(list(a__    )    ) + " >>> "    )

            SCREAMING_SNAKE_CASE_      :			List[Any]     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	List[str]  ,  SCREAMING_SNAKE_CASE_     :	Any    )							->   Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			str     =       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      SCREAMING_SNAKE_CASE_      :			Any     =       prefix + prompt_text
      return prompt_text


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Dict  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	List[str]  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      SCREAMING_SNAKE_CASE_      :			List[str]     =       prefix + prompt_text
      return prompt_text


snake_case_       =       {
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	int    )							->   Dict:
      """simple docstring"""

      if length < 0 and max_sequence_length > 0:
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max_sequence_length
      elif 0 < max_sequence_length < length:
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       max_sequence_length  # No generation bigger than model size
      elif length < 0:
            SCREAMING_SNAKE_CASE_      :			int     =       MAX_LENGTH  # avoid infinite loop
      return length


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       None
      if hasattr(a__  ,  "hidden_size"    ):
            SCREAMING_SNAKE_CASE_      :			str     =       model_config.hidden_size
      elif hasattr(a__  ,  "n_embed"    ):
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       model_config.n_embed
      elif hasattr(a__  ,  "n_embd"    ):
            SCREAMING_SNAKE_CASE_      :			Dict     =       model_config.n_embd

      SCREAMING_SNAKE_CASE_      :			Any     =       None
      if hasattr(a__  ,  "num_attention_heads"    ):
            SCREAMING_SNAKE_CASE_      :			str     =       model_config.num_attention_heads
      elif hasattr(a__  ,  "n_head"    ):
            SCREAMING_SNAKE_CASE_      :			List[str]     =       model_config.n_head

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

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       int(embedding_size / num_head    )
      if hasattr(a__  ,  "n_layer"    ):
            SCREAMING_SNAKE_CASE_      :			Dict     =       model_config.n_layer
      elif hasattr(a__  ,  "num_hidden_layers"    ):
            SCREAMING_SNAKE_CASE_      :			int     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	Tuple    )							->   int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       sparse_model_config(model.config    )
      if model.config.model_type == "bloom":
            SCREAMING_SNAKE_CASE_      :			Dict     =       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:
            SCREAMING_SNAKE_CASE_      :			str     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Tuple    )							->   Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[str]     =       len(a__    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       tokenizer.batch_encode_plus(a__  ,  return_tensors="pt"    )
      SCREAMING_SNAKE_CASE_      :			str     =       dummy_input.to(model.device    )
      if model.config.use_cache:
            SCREAMING_SNAKE_CASE_      :			Tuple     =       generate_past_key_values(a__  ,  a__  ,  1    )
      SCREAMING_SNAKE_CASE_      :			str     =       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 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							("""_optimized""", """_default""")




    def __init__(					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       optimized
          SCREAMING_SNAKE_CASE_      :			int     =       default




    def __call__(					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          if kwargs["past_key_values"] is None and self._default.config.use_cache:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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__			)
          SCREAMING_SNAKE_CASE_      :			int     =       self._optimized(**lowerCamelCase__			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       outputs[0]
          SCREAMING_SNAKE_CASE_      :			Dict     =       outputs[1]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       CausalLMOutputWithPast(
              loss=lowerCamelCase__				,     logits=lowerCamelCase__				,     past_key_values=lowerCamelCase__				,     hidden_states=lowerCamelCase__				,     attentions=lowerCamelCase__				,     )
          return fixed_output




    def __getattr__(					self				,     lowercase__			):






          """simple docstring"""






          return getattr(self._default				,     lowerCamelCase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     **lowercase__			):






          """simple docstring"""






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







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          return self._default._reorder_cache(lowerCamelCase__				,     lowerCamelCase__			)






def 					__lowerCamelCase			(      )							->   Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[str]     =       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=2_0    )
      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=4_2  ,  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"    )
      SCREAMING_SNAKE_CASE_      :			int     =       parser.parse_args()

      SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       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:
            SCREAMING_SNAKE_CASE_      :			Any     =       args.model_type.lower()
            SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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 :)"    )

      SCREAMING_SNAKE_CASE_      :			Tuple     =       tokenizer_class.from_pretrained(args.model_name_or_path    )
      if tokenizer.pad_token is None:
            SCREAMING_SNAKE_CASE_      :			str     =       tokenizer.eos_token
      SCREAMING_SNAKE_CASE_      :			Dict     =       model_class.from_pretrained(args.model_name_or_path    )
      model.to(args.device    )

      if args.fpaa:
            model.half()
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       getattr(model.config  ,  "max_position_embeddings"  ,  0    )
      SCREAMING_SNAKE_CASE_      :			str     =       adjust_length_to_model(args.length  ,  max_sequence_length=a__    )
      logger.info(a__    )

      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.prompt if args.prompt else input("Model prompt >>> "    )

      # Different models need different input formatting and/or extra arguments
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       args.model_type in PREPROCESSING_FUNCTIONS.keys()
      if requires_preprocessing:
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       PREPROCESSING_FUNCTIONS.get(args.model_type    )
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       prepare_input(a__  ,  a__  ,  a__  ,  a__    )

            if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       {"add_space_before_punct_symbol": True}
            else:
                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       {}

            SCREAMING_SNAKE_CASE_      :			List[str]     =       tokenizer.encode(
                a__  ,  add_special_tokens=a__  ,  return_tensors="pt"  ,  **a__    )
      else:
            SCREAMING_SNAKE_CASE_      :			Dict     =       args.prefix if args.prefix else args.padding_text
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       tokenizer.encode(prefix + prompt_text  ,  add_special_tokens=a__  ,  return_tensors="pt"    )
      SCREAMING_SNAKE_CASE_      :			int     =       encoded_prompt.to(args.device    )

      if encoded_prompt.size()[-1] == 0:
            SCREAMING_SNAKE_CASE_      :			int     =       None
      else:
            SCREAMING_SNAKE_CASE_      :			Any     =       encoded_prompt

      if args.jit:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       ["enable jit"]
            SCREAMING_SNAKE_CASE_      :			List[str]     =       prepare_jit_inputs(a__  ,  a__  ,  a__    )
            torch._C._jit_set_texpr_fuser_enabled(a__    )
            SCREAMING_SNAKE_CASE_      :			int     =       False
            if hasattr(a__  ,  "forward"    ):
                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       inspect.signature(model.forward    )
            else:
                  SCREAMING_SNAKE_CASE_      :			Dict     =       inspect.signature(model.__call__    )
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(a__  ,  a__    ) is not None    )
            SCREAMING_SNAKE_CASE_      :			Any     =       torch.jit.trace(a__  ,  a__  ,  strict=a__    )
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.jit.freeze(traced_model.eval()    )
            traced_model(*a__    )
            traced_model(*a__    )

            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       _ModelFallbackWrapper(a__  ,  a__    )

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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_()

      SCREAMING_SNAKE_CASE_      :			List[str]     =       []

      for generated_sequence_idx, generated_sequence in enumerate(a__    ):
            print(F"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="    )
            SCREAMING_SNAKE_CASE_      :			str     =       generated_sequence.tolist()

            # Decode text
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       tokenizer.decode(a__  ,  clean_up_tokenization_spaces=a__    )

            # Remove all text after the stop token
            SCREAMING_SNAKE_CASE_      :			List[str]     =       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
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       (
                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()







'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




