
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


_snake_case		  =   threading.Lock()
_snake_case		  =   None

_snake_case		  =   {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case		  =   logging.WARNING

_snake_case		  =   True
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      os.getenv('''DIFFUSERS_VERBOSITY'''		,					A						)
      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 lowerCamelCase_       (			):




      """simple docstring"""
      return __name__.split('''.'''						)[0]
def lowerCamelCase_       (			):




      """simple docstring"""
      return logging.getLogger(_get_library_name()						)
def lowerCamelCase_       (			):




      """simple docstring"""
      global _default_handler

      with _lock:
            if _default_handler:
                  # This library has already configured the library root logger.
                  return
            lowerCAmelCase_						=      logging.StreamHandler()  # Set sys.stderr as stream.
            lowerCAmelCase_						=      sys.stderr.flush

            # Apply our default configuration to the library root logger.
            lowerCAmelCase_						=      _get_library_root_logger()
            library_root_logger.addHandler(_default_handler						)
            library_root_logger.setLevel(_get_default_logging_level()						)
            lowerCAmelCase_						=      False
def lowerCamelCase_       (			):




      """simple docstring"""
      global _default_handler

      with _lock:
            if not _default_handler:
                  return

            lowerCAmelCase_						=      _get_library_root_logger()
            library_root_logger.removeHandler(_default_handler						)
            library_root_logger.setLevel(logging.NOTSET						)
            lowerCAmelCase_						=      None
def lowerCamelCase_       (			):




      """simple docstring"""
      return log_levels
def lowerCamelCase_       (			A			:      Optional[str] = None						):




      """simple docstring"""

      if name is None:
            lowerCAmelCase_						=      _get_library_name()

      _configure_library_root_logger()
      return logging.getLogger(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      _configure_library_root_logger()
      return _get_library_root_logger().getEffectiveLevel()
def lowerCamelCase_       (			A			:      int						):




      """simple docstring"""
      _configure_library_root_logger()
      _get_library_root_logger().setLevel(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      return set_verbosity(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      return set_verbosity(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      return set_verbosity(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      return set_verbosity(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().removeHandler(_default_handler						)
def lowerCamelCase_       (			):




      """simple docstring"""
      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().addHandler(_default_handler						)
def lowerCamelCase_       (			A			:      logging.Handler						):




      """simple docstring"""
      _configure_library_root_logger()

      assert handler is not None
      _get_library_root_logger().addHandler(A						)
def lowerCamelCase_       (			A			:      logging.Handler						):




      """simple docstring"""
      _configure_library_root_logger()

      assert handler is not None and handler not in _get_library_root_logger().handlers
      _get_library_root_logger().removeHandler(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      _configure_library_root_logger()
      lowerCAmelCase_						=      False
def lowerCamelCase_       (			):




      """simple docstring"""
      _configure_library_root_logger()
      lowerCAmelCase_						=      True
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      _get_library_root_logger().handlers

      for handler in handlers:
            lowerCAmelCase_						=      logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''						)
            handler.setFormatter(A						)
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      _get_library_root_logger().handlers

      for handler in handlers:
            handler.setFormatter(A						)
def lowerCamelCase_       (			self			:      Tuple		,					*A			:      Tuple		,					**A			:      str						):




      """simple docstring"""
      lowerCAmelCase_						=      os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS'''		,					A						)
      if no_advisory_warnings:
            return
      self.warning(*A		,					**A						)


_snake_case		  =   warning_advice


class 			UpperCamelCase_				:




      '''simple docstring'''







      def __init__( self					,					*_UpperCAmelCase					,					**_UpperCAmelCase):  # pylint: disable=unused-argument
            lowerCAmelCase_						=      args[0] if args else None







      def __iter__( self):
            return iter(self._iterator)







      def __getattr__( self					,					_UpperCAmelCase):

            def empty_fn(*_UpperCAmelCase					,					**_UpperCAmelCase):  # pylint: disable=unused-argument
                  return

            return empty_fn







      def __enter__( self):
            return self







      def __exit__( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            return


class 			UpperCamelCase_				:




      '''simple docstring'''







      def __call__( self					,					*_UpperCAmelCase					,					**_UpperCAmelCase):
            if _tqdm_active:
                  return tqdm_lib.tqdm(*_UpperCAmelCase					,					**_UpperCAmelCase)
            else:
                  return EmptyTqdm(*_UpperCAmelCase					,					**_UpperCAmelCase)







      def lowercase__		( self					,					*_UpperCAmelCase					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      None
            if _tqdm_active:
                  return tqdm_lib.tqdm.set_lock(*_UpperCAmelCase					,					**_UpperCAmelCase)







      def lowercase__		( self):
            if _tqdm_active:
                  return tqdm_lib.tqdm.get_lock()
_snake_case		  =   _tqdm_cls()
def lowerCamelCase_       (			):




      """simple docstring"""
      global _tqdm_active
      return bool(_tqdm_active						)
def lowerCamelCase_       (			):




      """simple docstring"""
      global _tqdm_active
      lowerCAmelCase_						=      True




def lowerCamelCase_       (			):




      """simple docstring"""
      global _tqdm_active
      lowerCAmelCase_						=      False





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

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


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Optional[int]						 =      'mbart'
      a		:List[Any]						 =      ['past_key_values']
      a		:Optional[Any]						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=50_265					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=False					,					_UpperCAmelCase=1					,					_UpperCAmelCase=0					,					_UpperCAmelCase=2					,					_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_						=      classifier_dropout
            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					,					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":
                  # TODO: figure this case out.
                  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
      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 + 3
                  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_						=      []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_						=      min(_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      max(_UpperCAmelCase					,					_UpperCAmelCase) - min_num_layers
                  lowerCAmelCase_						=      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                  for _ in range(_UpperCAmelCase):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                            ))
                  # TODO: test this.
                  lowerCAmelCase_						=      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                  for _ in range(_UpperCAmelCase					,					_UpperCAmelCase):
                        common_inputs["past_key_values"].append((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
                  # Not using the same length for past_key_values
                  lowerCAmelCase_						=      seqlen + 2
                  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)



