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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blenderbot-small"""
  _SCREAMING_SNAKE_CASE     					=      ["""past_key_values"""]
  _SCREAMING_SNAKE_CASE     					=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=5_02_65  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[str]=8  ,				__SCREAMING_SNAKE_CASE			:   Any=20_48  ,				__SCREAMING_SNAKE_CASE			:   int=16  ,				__SCREAMING_SNAKE_CASE			:   List[str]=8  ,				__SCREAMING_SNAKE_CASE			:   Tuple=20_48  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   Any=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Tuple=5_12  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.1  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=False  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   Any=1  ,				__SCREAMING_SNAKE_CASE			:   Any=2  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=2  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   __a							=	vocab_size
   __a							=	max_position_embeddings
   __a							=	d_model
   __a							=	encoder_ffn_dim
   __a							=	encoder_layers
   __a							=	encoder_attention_heads
   __a							=	decoder_ffn_dim
   __a							=	decoder_layers
   __a							=	decoder_attention_heads
   __a							=	dropout
   __a							=	attention_dropout
   __a							=	activation_dropout
   __a							=	activation_function
   __a							=	init_std
   __a							=	encoder_layerdrop
   __a							=	decoder_layerdrop
   __a							=	use_cache
   __a							=	encoder_layers
   __a							=	scale_embedding  # scale factor will be sqrt(d_model) if True

   super().__init__(
       pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,				decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,				forced_eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   int							):
   if self.task in ["default", "seq2seq-lm"]:
    __a							=	OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
        ]							)

    if self.use_past:
     __a							=	{0: "batch"}
     __a							=	{0: "batch", 1: "past_decoder_sequence + sequence"}
    else:
     __a							=	{0: "batch", 1: "decoder_sequence"}
     __a							=	{0: "batch", 1: "decoder_sequence"}

    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,				direction="inputs"							)
   elif self.task == "causal-lm":
    # TODO: figure this case out.
    __a							=	OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
        ]							)
    if self.use_past:
     __a							,  __a							=	self.num_layers
     for i in range(__SCREAMING_SNAKE_CASE							):
      __a							=	{0: "batch", 2: "past_sequence + sequence"}
      __a							=	{0: "batch", 2: "past_sequence + sequence"}
   else:
    __a							=	OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
            ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
        ]							)

   return common_inputs

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   if self.task in ["default", "seq2seq-lm"]:
    __a							=	super().outputs
   else:
    __a							=	super(__SCREAMING_SNAKE_CASE  ,				self							).outputs
    if self.use_past:
     __a							,  __a							=	self.num_layers
     for i in range(__SCREAMING_SNAKE_CASE							):
      __a							=	{0: "batch", 2: "past_sequence + sequence"}
      __a							=	{0: "batch", 2: "past_sequence + sequence"}
   return common_outputs

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   PreTrainedTokenizer  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				):
   __a							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # Generate decoder inputs
   __a							=	seq_length if not self.use_past else 1
   __a							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
   __a							=	dict(**__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if self.use_past:
    if not is_torch_available():
     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
    else:
     import torch
    __a							,  __a							=	common_inputs["input_ids"].shape
    __a							=	common_inputs["decoder_input_ids"].shape[1]
    __a							,  __a							=	self.num_attention_heads
    __a							=	(
        batch,
        num_encoder_attention_heads,
        encoder_seq_length,
        self._config.hidden_size // num_encoder_attention_heads,
    )
    __a							=	decoder_seq_length + 3
    __a							=	(
        batch,
        num_decoder_attention_heads,
        decoder_past_length,
        self._config.hidden_size // num_decoder_attention_heads,
    )

    __a							=	torch.cat(
        [common_inputs["decoder_attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)]  ,				dim=1							)

    __a							=	[]
    # If the number of encoder and decoder layers are present in the model configuration, both are considered
    __a							,  __a							=	self.num_layers
    __a							=	min(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    __a							=	max(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) - min_num_layers
    __a							=	"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

    for _ in range(__SCREAMING_SNAKE_CASE							):
     common_inputs["past_key_values"].append(
         (
             torch.zeros(__SCREAMING_SNAKE_CASE							),
             torch.zeros(__SCREAMING_SNAKE_CASE							),
             torch.zeros(__SCREAMING_SNAKE_CASE							),
             torch.zeros(__SCREAMING_SNAKE_CASE							),
         )							)
    # TODO: test this.
    __a							=	encoder_shape if remaining_side_name == "encoder" else decoder_shape
    for _ in range(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
     common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE							), torch.zeros(__SCREAMING_SNAKE_CASE							))							)
   return common_inputs

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   PreTrainedTokenizer  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				):
   __a							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   if self.use_past:
    if not is_torch_available():
     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
    else:
     import torch
    __a							,  __a							=	common_inputs["input_ids"].shape
    # Not using the same length for past_key_values
    __a							=	seqlen + 2
    __a							,  __a							=	self.num_layers
    __a							,  __a							=	self.num_attention_heads
    __a							=	(
        batch,
        num_encoder_attention_heads,
        past_key_values_length,
        self._config.hidden_size // num_encoder_attention_heads,
    )

    __a							=	common_inputs["attention_mask"].dtype
    __a							=	torch.cat(
        [common_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				dtype=__SCREAMING_SNAKE_CASE							)]  ,				dim=1							)
    __a							=	[
        (torch.zeros(__SCREAMING_SNAKE_CASE							), torch.zeros(__SCREAMING_SNAKE_CASE							)) for _ in range(__SCREAMING_SNAKE_CASE							)
    ]
   return common_inputs

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   PreTrainedTokenizer  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				):
   # Copied from OnnxConfig.generate_dummy_inputs
   # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
   __a							=	compute_effective_axis_dimension(
       __SCREAMING_SNAKE_CASE  ,				fixed_dimension=OnnxConfig.default_fixed_batch  ,				num_token_to_add=0							)

   # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
   __a							=	tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE							)
   __a							=	compute_effective_axis_dimension(
       __SCREAMING_SNAKE_CASE  ,				fixed_dimension=OnnxConfig.default_fixed_sequence  ,				num_token_to_add=__SCREAMING_SNAKE_CASE							)

   # Generate dummy inputs according to compute batch and sequence
   __a							=	[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
   __a							=	dict(tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE							)							)
   return common_inputs

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   PreTrainedTokenizer  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				):
   if self.task in ["default", "seq2seq-lm"]:
    __a							=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
        __SCREAMING_SNAKE_CASE  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				is_pair=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)

   elif self.task == "causal-lm":
    __a							=	self._generate_dummy_inputs_for_causal_lm(
        __SCREAMING_SNAKE_CASE  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				is_pair=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        __SCREAMING_SNAKE_CASE  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				is_pair=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)

   return common_inputs

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   if self.task in ["default", "seq2seq-lm"]:
    __a							=	super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	super(__SCREAMING_SNAKE_CASE  ,				self							)._flatten_past_key_values_(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)



import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_logger(__name__)
class  A_			(							enum.Enum			):
  _SCREAMING_SNAKE_CASE     					=      """all_checks"""
  _SCREAMING_SNAKE_CASE     					=      """basic_checks"""
  _SCREAMING_SNAKE_CASE     					=      """no_checks"""
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A				,					_A=None       ):



 """simple docstring"""


 if expected_checksums is None:
  logger.info("Unable to verify checksums."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreDownloadedFiles(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedDownloadedFile(str(set(_A       ) - set(_A       )       )       )
 __a							=	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
 __a							=	" for " + verification_name if verification_name is not None else ""
 if len(_A       ) > 0:
  raise NonMatchingChecksumError(
      f"""Checksums didn't match{for_verification_name}:\n"""
      f"""{bad_urls}\n"""
      "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"       )
 logger.info("All the checksums matched successfully" + for_verification_name       )
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 if expected_splits is None:
  logger.info("Unable to verify splits sizes."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreSplits(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedSplits(str(set(_A       ) - set(_A       )       )       )
 __a							=	[
     {"expected": expected_splits[name], "recorded": recorded_splits[name]}
     for name in expected_splits
     if expected_splits[name].num_examples != recorded_splits[name].num_examples
 ]
 if len(_A       ) > 0:
  raise NonMatchingSplitsSizesError(str(_A       )       )
 logger.info("All the splits matched successfully."       )



def 	__A		(      _A				,					_A = True       ):



 """simple docstring"""


 if record_checksum:
  __a							=	shaaaa()
  with open(_A				,					"rb"       ) as f:
   for chunk in iter(lambda: f.read(1 << 20       )				,					B""       ):
    m.update(_A       )
   __a							=	m.hexdigest()
 else:
  __a							=	None
 return {"num_bytes": os.path.getsize(_A       ), "checksum": checksum}






def 	__A		(      _A       ):



 """simple docstring"""


 if dataset_size and config.IN_MEMORY_MAX_SIZE:
  return dataset_size < config.IN_MEMORY_MAX_SIZE
 else:
  return False


