



'''simple docstring'''



import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_lowercase       :						Optional[Any]						=threading.Lock()
_lowercase       :						Optional[logging.Handler]						=None

_lowercase       :						Union[str, Any]						={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_lowercase       :						Union[str, Any]						=logging.WARNING

_lowercase       :						Optional[Any]						=True


def 							__UpperCAmelCase				(				)		->						Optional[Any]:
				snake_case__      :   Union[str, Any]      =    os.getenv('''DIFFUSERS_VERBOSITY'''   ,				UpperCamelCase__			)
				if env_level_str:
								if env_level_str in log_levels:
												return log_levels[env_level_str]
								else:
												logging.getLogger().warning(
												    F'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
												    F'''has to be one of: { ", ".join(log_levels.keys()			) }'''			)
				return _default_log_level


def 							__UpperCAmelCase				(				)		->						str:
				return __name__.split('''.'''			)[0]


def 							__UpperCAmelCase				(				)		->						logging.Logger:
				return logging.getLogger(_get_library_name()			)


def 							__UpperCAmelCase				(				)		->						None:
				global _default_handler

				with _lock:
								if _default_handler:
												# This library has already configured the library root logger.
												return
								snake_case__      :   List[str]      =    logging.StreamHandler()  # Set sys.stderr as stream.
								snake_case__      :   Dict      =    sys.stderr.flush

								# Apply our default configuration to the library root logger.
								snake_case__      :   List[Any]      =    _get_library_root_logger()
								library_root_logger.addHandler(_default_handler			)
								library_root_logger.setLevel(_get_default_logging_level()			)
								snake_case__      :   Any      =    False


def 							__UpperCAmelCase				(				)		->						None:
				global _default_handler

				with _lock:
								if not _default_handler:
												return

								snake_case__      :   Any      =    _get_library_root_logger()
								library_root_logger.removeHandler(_default_handler			)
								library_root_logger.setLevel(logging.NOTSET			)
								snake_case__      :   Any      =    None


def 							__UpperCAmelCase				(				)		->						Optional[int]:
				return log_levels


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[str] = None			)		->						logging.Logger:

				if name is None:
								snake_case__      :   Any      =    _get_library_name()

				_configure_library_root_logger()
				return logging.getLogger(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						int:
				_configure_library_root_logger()
				return _get_library_root_logger().getEffectiveLevel()


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						None:
				_configure_library_root_logger()
				_get_library_root_logger().setLevel(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						Optional[Any]:
				return set_verbosity(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						Union[str, Any]:
				return set_verbosity(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						Dict:
				return set_verbosity(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						Any:
				return set_verbosity(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						None:
				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().removeHandler(_default_handler			)


def 							__UpperCAmelCase				(				)		->						None:
				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().addHandler(_default_handler			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:logging.Handler			)		->						None:
				_configure_library_root_logger()

				assert handler is not None
				_get_library_root_logger().addHandler(UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:logging.Handler			)		->						None:
				_configure_library_root_logger()

				assert handler is not None and handler not in _get_library_root_logger().handlers
				_get_library_root_logger().removeHandler(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						None:
				_configure_library_root_logger()
				snake_case__      :   Any      =    False


def 							__UpperCAmelCase				(				)		->						None:
				_configure_library_root_logger()
				snake_case__      :   Dict      =    True


def 							__UpperCAmelCase				(				)		->						None:
				snake_case__      :   List[str]      =    _get_library_root_logger().handlers

				for handler in handlers:
								snake_case__      :   Union[str, Any]      =    logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''			)
								handler.setFormatter(UpperCamelCase__			)


def 							__UpperCAmelCase				(				)		->						None:
				snake_case__      :   Dict      =    _get_library_root_logger().handlers

				for handler in handlers:
								handler.setFormatter(UpperCamelCase__			)


def 							__UpperCAmelCase				(				self				:Optional[Any]   ,				*UpperCamelCase__				:Optional[Any]   ,				**UpperCamelCase__				:Dict			)		->						Dict:
				snake_case__      :   List[Any]      =    os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS'''   ,				UpperCamelCase__			)
				if no_advisory_warnings:
								return
				self.warning(*UpperCamelCase__   ,				**UpperCamelCase__			)


_lowercase       :						Dict						=warning_advice






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[Any]    ,			*__UpperCamelCase		:						Any    ,			**__UpperCamelCase		:						Optional[Any]			)      ->      Union[str, Any]:  # pylint: disable=unused-argument
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    args[0] if args else None



			def __iter__(     self		:						Optional[int]			)      ->      List[Any]:
							"""simple docstring"""





							return iter(self._iterator			)



			def __getattr__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						str			)      ->      Optional[Any]:
							"""simple docstring"""





							def empty_fn(*__UpperCamelCase		:						int    ,			**__UpperCamelCase		:						Optional[int]			):  # pylint: disable=unused-argument
											return

							return empty_fn



			def __enter__(     self		:						int			)      ->      List[str]:
							"""simple docstring"""





							return self







			def __exit__(     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Tuple			)      ->      List[str]:
							"""simple docstring"""





							return






class 			_SCREAMING_SNAKE_CASE				:



			def __call__(     self		:						List[Any]    ,			*__UpperCamelCase		:						str    ,			**__UpperCamelCase		:						List[Any]			)      ->      Optional[Any]:
							"""simple docstring"""





							if _tqdm_active:
											return tqdm_lib.tqdm(*__UpperCamelCase    ,			**__UpperCamelCase			)
							else:
											return EmptyTqdm(*__UpperCamelCase    ,			**__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						int    ,			*__UpperCamelCase		:						Union[str, Any]    ,			**__UpperCamelCase		:						str			)      ->      Any:
							"""simple docstring"""





							snake_case__      :   Dict      =    None
							if _tqdm_active:
											return tqdm_lib.tqdm.set_lock(*__UpperCamelCase    ,			**__UpperCamelCase			)







			def 				lowerCAmelCase (     self		:						int			)      ->      Optional[Any]:
							"""simple docstring"""





							if _tqdm_active:
											return tqdm_lib.tqdm.get_lock()



_lowercase       :						Optional[int]						=_tqdm_cls()


def 							__UpperCAmelCase				(				)		->						bool:
				global _tqdm_active
				return bool(_tqdm_active			)


def 							__UpperCAmelCase				(				)		->						List[str]:
				global _tqdm_active
				snake_case__      :   Dict      =    True



def 							__UpperCAmelCase				(				)		->						str:
				global _tqdm_active
				snake_case__      :   Any      =    False






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


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      'blenderbot'
      a		:List[str]						 =      ['past_key_values']
      a		:Any						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=8_008					,					_UpperCAmelCase=128					,					_UpperCAmelCase=2					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=24					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=2_560					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=1					,					_UpperCAmelCase=False					,					_UpperCAmelCase=0					,					_UpperCAmelCase=1					,					_UpperCAmelCase=2					,					_UpperCAmelCase=3					,					_UpperCAmelCase=2					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      d_model
            lowerCAmelCase_						=      encoder_ffn_dim
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      encoder_attention_heads
            lowerCAmelCase_						=      decoder_ffn_dim
            lowerCAmelCase_						=      decoder_layers
            lowerCAmelCase_						=      decoder_attention_heads
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      attention_dropout
            lowerCAmelCase_						=      activation_dropout
            lowerCAmelCase_						=      activation_function
            lowerCAmelCase_						=      init_std
            lowerCAmelCase_						=      encoder_layerdrop
            lowerCAmelCase_						=      decoder_layerdrop
            lowerCAmelCase_						=      use_cache
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=_UpperCAmelCase					,					bos_token_id=_UpperCAmelCase					,					eos_token_id=_UpperCAmelCase					,					is_encoder_decoder=_UpperCAmelCase					,					decoder_start_token_id=_UpperCAmelCase					,					encoder_no_repeat_ngram_size=_UpperCAmelCase					,					forced_eos_token_id=_UpperCAmelCase					,					**_UpperCAmelCase					,					)


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      @property
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_						=      {0: '''batch'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                  else:
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                  if self.use_past:
                        self.fill_with_past_key_values_(_UpperCAmelCase					,					direction='''inputs''')
            elif self.task == "causal-lm":
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            else:
                  lowerCAmelCase_						=      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
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super().outputs
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self).outputs
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            return common_outputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            # Generate decoder inputs
            lowerCAmelCase_						=      seq_length if not self.use_past else 1
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowerCAmelCase_						=      dict(**_UpperCAmelCase					,					**_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      decoder_seq_length
                  lowerCAmelCase_						=      (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )
                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      []
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

                  for _ in range(_UpperCAmelCase):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                            ))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      seqlen
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      common_inputs['''attention_mask'''].dtype
                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      [
                      (torch.zeros(_UpperCAmelCase), torch.zeros(_UpperCAmelCase)) for _ in range(_UpperCAmelCase)
                  ]
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = 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
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					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
            lowerCAmelCase_						=      tokenizer.num_special_tokens_to_add(_UpperCAmelCase)
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					fixed_dimension=OnnxConfig.default_fixed_sequence					,					num_token_to_add=_UpperCAmelCase)

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)

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

            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super()._flatten_past_key_values_(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self)._flatten_past_key_values_(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if direction not in ["inputs", "outputs"]:
                  raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

            lowerCAmelCase_						=      '''past_key_values''' if direction == '''inputs''' else '''present'''
            lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

            lowerCAmelCase_						=      '''past_encoder_sequence'''
            lowerCAmelCase_						=      '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

            for i in range(_UpperCAmelCase):
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}



