
'''simple docstring'''

import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
	import tensorflow as tf

	tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset


_UpperCAmelCase   :      int 						=  5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def 						snake_case__	(      UpperCamelCase	) ->      List[str]:
		return PTtoTFCommand(
		    args.model_name			,args.local_dir			,args.max_error			,args.new_weights			,args.no_pr			,args.push			,args.extra_commit_description			,args.override_model_class			,)


class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		@staticmethod
		def _lowercase  (    _snake_case     )				->    Any:
				_UpperCamelCase       : Any    =      parser.add_parser(
				    '''pt-to-tf'''  ,					help=(
				        '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
				        ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
				    )  ,					)
				train_parser.add_argument(
				    '''--model-name'''  ,					type=_snake_case  ,					required=_snake_case  ,					help='''The model name, including owner/organization, as seen on the hub.'''  ,					)
				train_parser.add_argument(
				    '''--local-dir'''  ,					type=_snake_case  ,					default=''''''  ,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''  ,					)
				train_parser.add_argument(
				    '''--max-error'''  ,					type=_snake_case  ,					default=_snake_case  ,					help=(
				        F'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
				    )  ,					)
				train_parser.add_argument(
				    '''--new-weights'''  ,					action='''store_true'''  ,					help='''Optional flag to create new TensorFlow weights, even if they already exist.'''  ,					)
				train_parser.add_argument(
				    '''--no-pr'''  ,					action='''store_true'''  ,					help='''Optional flag to NOT open a PR with converted weights.'''     )
				train_parser.add_argument(
				    '''--push'''  ,					action='''store_true'''  ,					help='''Optional flag to push the weights directly to `main` (requires permissions)'''  ,					)
				train_parser.add_argument(
				    '''--extra-commit-description'''  ,					type=_snake_case  ,					default=''''''  ,					help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).'''  ,					)
				train_parser.add_argument(
				    '''--override-model-class'''  ,					type=_snake_case  ,					default=_snake_case  ,					help='''If you think you know better than the auto-detector, you can specify the model class here. '''
				    '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'''  ,					)
				train_parser.set_defaults(func=_snake_case     )
		@staticmethod
		def _lowercase  (    _snake_case  ,					_snake_case     )				->    List[str]:
				_UpperCamelCase       : Any    =      set(pt_outputs.keys()     )
				_UpperCamelCase       : Dict    =      set(tf_outputs.keys()     )
				if pt_out_attrs != tf_out_attrs:
						raise ValueError(
						    F'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
						    F''' {tf_out_attrs})'''     )

				# 2. For each output attribute, computes the difference
				def _find_pt_tf_differences(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case=""     ):
						# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
						# recursivelly, keeping the name of the attribute.
						if isinstance(_snake_case  ,					torch.Tensor     ):
								_UpperCamelCase       : Optional[int]    =      np.max(np.abs(pt_out.numpy() - tf_out.numpy()     )     )
								_UpperCamelCase       : str    =      tensor_difference
						else:
								_UpperCamelCase       : List[Any]    =      attr_name
								for i, pt_item in enumerate(_snake_case     ):
										# If it is a named attribute, we keep the name. Otherwise, just its index.
										if isinstance(_snake_case  ,					_snake_case     ):
												_UpperCamelCase       : Dict    =      root_name + pt_item
												_UpperCamelCase       : str    =      tf_out[pt_item]
												_UpperCamelCase       : List[str]    =      pt_out[pt_item]
										else:
												_UpperCamelCase       : List[str]    =      root_name + F'''[{i}]'''
												_UpperCamelCase       : List[str]    =      tf_out[i]
										_UpperCamelCase       : List[Any]    =      _find_pt_tf_differences(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

						return differences

				return _find_pt_tf_differences(_snake_case  ,					_snake_case  ,					{}     )
		def __init__(    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					*_snake_case  ,					)				->    Tuple:
				_UpperCamelCase       : int    =      logging.get_logger('''transformers-cli/pt_to_tf'''     )
				_UpperCamelCase       : List[Any]    =      model_name
				_UpperCamelCase       : Any    =      local_dir if local_dir else os.path.join('''/tmp'''  ,					_snake_case     )
				_UpperCamelCase       : Optional[int]    =      max_error
				_UpperCamelCase       : Optional[int]    =      new_weights
				_UpperCamelCase       : Dict    =      no_pr
				_UpperCamelCase       : Tuple    =      push
				_UpperCamelCase       : Tuple    =      extra_commit_description
				_UpperCamelCase       : List[Any]    =      override_model_class
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:

				def _get_audio_input():
						_UpperCamelCase       : Dict    =      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''  ,					'''clean'''  ,					split='''validation'''     )
						_UpperCamelCase       : Union[str, Any]    =      ds.sort('''id'''     ).select(range(2     )     )[:2]['''audio''']
						_UpperCamelCase       : Dict    =      [x['''array'''] for x in speech_samples]
						return raw_samples

				_UpperCamelCase       : Optional[int]    =      type(pt_model.config     )
				if model_config_class in PROCESSOR_MAPPING:
						_UpperCamelCase       : Optional[Any]    =      AutoProcessor.from_pretrained(self._local_dir     )
						if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
								_UpperCamelCase       : List[Any]    =      processor.tokenizer.eos_token
				elif model_config_class in IMAGE_PROCESSOR_MAPPING:
						_UpperCamelCase       : Optional[Any]    =      AutoImageProcessor.from_pretrained(self._local_dir     )
				elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
						_UpperCamelCase       : Any    =      AutoFeatureExtractor.from_pretrained(self._local_dir     )
				elif model_config_class in TOKENIZER_MAPPING:
						_UpperCamelCase       : Any    =      AutoTokenizer.from_pretrained(self._local_dir     )
						if processor.pad_token is None:
								_UpperCamelCase       : Optional[Any]    =      processor.eos_token
				else:
						raise ValueError(F'''Unknown data processing type (model config type: {model_config_class})'''     )

				_UpperCamelCase       : Optional[int]    =      set(inspect.signature(pt_model.forward     ).parameters.keys()     )
				_UpperCamelCase       : Optional[int]    =      {}
				if "input_ids" in model_forward_signature:
						processor_inputs.update(
						    {
						        '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
						        '''padding''': True,
						        '''truncation''': True,
						    }     )
				if "pixel_values" in model_forward_signature:
						_UpperCamelCase       : Optional[int]    =      load_dataset('''cifar10'''  ,					'''plain_text'''  ,					split='''test'''     )[:2]['''img''']
						processor_inputs.update({'''images''': sample_images}     )
				if "input_features" in model_forward_signature:
						_UpperCamelCase       : Optional[Any]    =      inspect.signature(processor.feature_extractor     ).parameters
						# Pad to the largest input length by default but take feature extractor default
						# padding value if it exists e.g. "max_length" and is not False or None
						if "padding" in feature_extractor_signature:
								_UpperCamelCase       : Any    =      feature_extractor_signature['''padding'''].default
								if default_strategy is not False and default_strategy is not None:
										_UpperCamelCase       : int    =      default_strategy
								else:
										_UpperCamelCase       : Any    =      True
						else:
								_UpperCamelCase       : int    =      True
						processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}     )
				if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
						processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}     )
				_UpperCamelCase       : Optional[int]    =      processor(**_snake_case  ,					return_tensors='''pt'''     )
				_UpperCamelCase       : List[str]    =      processor(**_snake_case  ,					return_tensors='''tf'''     )

				# Extra input requirements, in addition to the input modality
				if (
				    config.is_encoder_decoder
				    or (hasattr(_snake_case  ,					'''encoder'''     ) and hasattr(_snake_case  ,					'''decoder'''     ))
				    or "decoder_input_ids" in tf_dummy_inputs
				):
						_UpperCamelCase       : Dict    =      np.asarray([[1], [1]]  ,					dtype=_snake_case     ) * (pt_model.config.decoder_start_token_id or 0)
						pt_input.update({'''decoder_input_ids''': torch.tensor(_snake_case     )}     )
						tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(_snake_case     )}     )

				return pt_input, tf_input




		def _lowercase  (    self     )				->    Optional[int]:
				# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
				if version.parse(huggingface_hub.__version__     ) < version.parse('''0.9.0'''     ):
						raise ImportError(
						    '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
						    ''' installation.'''     )
				else:
						from huggingface_hub import Repository, create_commit
						from huggingface_hub._commit_api import CommitOperationAdd

				# Fetch remote data
				_UpperCamelCase       : Dict    =      Repository(local_dir=self._local_dir  ,					clone_from=self._model_name     )

				# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
				_UpperCamelCase       : int    =      AutoConfig.from_pretrained(self._local_dir     )
				_UpperCamelCase       : int    =      config.architectures
				if self._override_model_class is not None:
						if self._override_model_class.startswith('''TF'''     ):
								_UpperCamelCase       : str    =      [self._override_model_class[2:]]
						else:
								_UpperCamelCase       : int    =      [self._override_model_class]
						try:
								_UpperCamelCase       : Any    =      getattr(import_module('''transformers'''     )  ,					architectures[0]     )
						except AttributeError:
								raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''     )
						try:
								_UpperCamelCase       : Union[str, Any]    =      getattr(import_module('''transformers'''     )  ,					'''TF''' + architectures[0]     )
						except AttributeError:
								raise ValueError(F'''TF model class TF{self._override_model_class} not found in transformers.'''     )
				elif architectures is None:  # No architecture defined -- use auto classes
						_UpperCamelCase       : List[str]    =      getattr(import_module('''transformers'''     )  ,					'''AutoModel'''     )
						_UpperCamelCase       : Tuple    =      getattr(import_module('''transformers'''     )  ,					'''TFAutoModel'''     )
						self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''     )
				else:  # Architecture defined -- use it
						if len(_snake_case     ) > 1:
								raise ValueError(F'''More than one architecture was found, aborting. (architectures = {architectures})'''     )
						self._logger.warning(F'''Detected architecture: {architectures[0]}'''     )
						_UpperCamelCase       : Tuple    =      getattr(import_module('''transformers'''     )  ,					architectures[0]     )
						try:
								_UpperCamelCase       : List[str]    =      getattr(import_module('''transformers'''     )  ,					'''TF''' + architectures[0]     )
						except AttributeError:
								raise AttributeError(F'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''     )

        # Check the TF dummy inputs to see what keys we need in the forward pass
				_UpperCamelCase       : Tuple    =      tf_class.from_config(_snake_case     )
				_UpperCamelCase       : List[Any]    =      tf_from_pt_model.dummy_inputs

				del tf_from_pt_model  # Try to keep only one model in memory at a time

				# Load the model and get some basic inputs
				_UpperCamelCase       : Any    =      pt_class.from_pretrained(self._local_dir     )
				pt_model.eval()

				_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.get_inputs(_snake_case  ,					_snake_case  ,					_snake_case     )

				with torch.no_grad():
						_UpperCamelCase       : List[Any]    =      pt_model(**_snake_case  ,					output_hidden_states=_snake_case     )
				del pt_model  # will no longer be used, and may have a large memory footprint

				_UpperCamelCase       : Union[str, Any]    =      tf_class.from_pretrained(self._local_dir  ,					from_pt=_snake_case     )
				_UpperCamelCase       : Tuple    =      tf_from_pt_model(**_snake_case  ,					output_hidden_states=_snake_case  ,					training=_snake_case     )

				# Confirms that cross loading PT weights into TF worked.
				_UpperCamelCase       : Any    =      self.find_pt_tf_differences(_snake_case  ,					_snake_case     )
				_UpperCamelCase       : List[str]    =      {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
				_UpperCamelCase       : Optional[int]    =      {k: v for k, v in crossload_differences.items() if '''hidden''' in k}
				if len(_snake_case     ) == 0 and architectures is not None:
						raise ValueError(
						    F'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
						    ''' output was found. All outputs start with \'hidden\''''     )
				_UpperCamelCase       : Union[str, Any]    =      max(output_differences.values()     ) if output_differences else 0.0
				_UpperCamelCase       : int    =      max(hidden_differences.values()     )
				if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
						raise ValueError(
						    '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
						    + F'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
						    + '''\n'''.join([F'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]     )
						    + F'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
						    + '''\n'''.join([F'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]     )     )

				# Save the weights in a TF format (if needed) and confirms that the results are still good
				_UpperCamelCase       : Dict    =      os.path.join(self._local_dir  ,					_snake_case     )
				_UpperCamelCase       : Union[str, Any]    =      os.path.join(self._local_dir  ,					_snake_case     )
				if (not os.path.exists(_snake_case     ) and not os.path.exists(_snake_case     )) or self._new_weights:
						tf_from_pt_model.save_pretrained(self._local_dir     )
				del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

				_UpperCamelCase       : Optional[int]    =      tf_class.from_pretrained(self._local_dir     )
				_UpperCamelCase       : Any    =      tf_model(**_snake_case  ,					output_hidden_states=_snake_case     )

				_UpperCamelCase       : int    =      self.find_pt_tf_differences(_snake_case  ,					_snake_case     )
				_UpperCamelCase       : str    =      {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
				_UpperCamelCase       : List[str]    =      {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
				if len(_snake_case     ) == 0 and architectures is not None:
						raise ValueError(
						    F'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
						    ''' output was found. All outputs start with \'hidden\''''     )
				_UpperCamelCase       : Any    =      max(output_differences.values()     ) if output_differences else 0.0
				_UpperCamelCase       : Union[str, Any]    =      max(hidden_differences.values()     )
				if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
						raise ValueError(
						    '''The converted TensorFlow model has different outputs, something went wrong!\n'''
						    + F'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
						    + '''\n'''.join([F'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]     )
						    + F'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
						    + '''\n'''.join([F'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]     )     )

				_UpperCamelCase       : Dict    =      '''Update TF weights''' if self._new_weights else '''Add TF weights'''
				if self._push:
						repo.git_add(auto_lfs_track=_snake_case     )
						repo.git_commit(_snake_case     )
						repo.git_push(blocking=_snake_case     )  # this prints a progress bar with the upload
						self._logger.warning(F'''TF weights pushed into {self._model_name}'''     )
				elif not self._no_pr:
						self._logger.warning('''Uploading the weights into a new PR...'''     )
						_UpperCamelCase       : int    =      (
						    '''Model converted by the [`transformers`\' `pt_to_tf`'''
						    ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
						    '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
						    F'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
						    F'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
						    F'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
						    F'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
						)
						if self._max_error > MAX_ERROR:
								commit_descrition += (
								    F'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
								)
						if self._extra_commit_description:
								commit_descrition += "\n\n" + self._extra_commit_description

						# sharded model -> adds all related files (index and .h5 shards)
						if os.path.exists(_snake_case     ):
								_UpperCamelCase       : int    =      [
								    CommitOperationAdd(path_in_repo=_snake_case  ,					path_or_fileobj=_snake_case     )
								]
								for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''     ):
										operations += [
										    CommitOperationAdd(path_in_repo=os.path.basename(_snake_case     )  ,					path_or_fileobj=_snake_case     )
										]
						else:
								_UpperCamelCase       : str    =      [CommitOperationAdd(path_in_repo=_snake_case  ,					path_or_fileobj=_snake_case     )]

						_UpperCamelCase       : List[Any]    =      create_commit(
						    repo_id=self._model_name  ,					operations=_snake_case  ,					commit_message=_snake_case  ,					commit_description=_snake_case  ,					repo_type='''model'''  ,					create_pr=_snake_case  ,					).pr_url
						self._logger.warning(F'''PR open in {hub_pr_url}'''     )







'''simple docstring'''

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


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

_UpperCAmelCase   :      Any 						=  {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		A__  :   List[str]          =    'bigbird_pegasus'
		A__  :   Union[str, Any]          =    ['past_key_values']
		A__  :   Optional[int]          =    {
		    'num_attention_heads': 'encoder_attention_heads',
		    'hidden_size': 'd_model',
		    'attention_probs_dropout_prob': 'attention_dropout',
		}
		def __init__(    self  ,					_snake_case=96103  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu_new"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=1  ,					_snake_case="block_sparse"  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=False  ,					**_snake_case  ,					)				->    Optional[Any]:
				_UpperCamelCase       : str    =      vocab_size
				_UpperCamelCase       : Tuple    =      max_position_embeddings
				_UpperCamelCase       : int    =      d_model
				_UpperCamelCase       : List[str]    =      encoder_ffn_dim
				_UpperCamelCase       : str    =      encoder_layers
				_UpperCamelCase       : Dict    =      encoder_attention_heads
				_UpperCamelCase       : str    =      decoder_ffn_dim
				_UpperCamelCase       : List[str]    =      decoder_layers
				_UpperCamelCase       : Any    =      decoder_attention_heads
				_UpperCamelCase       : str    =      dropout
				_UpperCamelCase       : int    =      attention_dropout
				_UpperCamelCase       : Optional[int]    =      activation_dropout
				_UpperCamelCase       : Optional[Any]    =      activation_function
				_UpperCamelCase       : Tuple    =      init_std
				_UpperCamelCase       : Any    =      encoder_layerdrop
				_UpperCamelCase       : List[str]    =      decoder_layerdrop
				_UpperCamelCase       : Any    =      classifier_dropout
				_UpperCamelCase       : int    =      use_cache
				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
				_UpperCamelCase       : str    =      scale_embedding  # scale factor will be sqrt(d_model) if True

				# extra config
				_UpperCamelCase       : str    =      attention_type
				_UpperCamelCase       : Tuple    =      block_size
				_UpperCamelCase       : Optional[Any]    =      num_random_blocks
				_UpperCamelCase       : Dict    =      use_bias

				super().__init__(
				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					decoder_start_token_id=_snake_case  ,					**_snake_case  ,					)







class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		@property
		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : str    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]     )

						if self.use_past:
								_UpperCamelCase       : List[Any]    =      {0: '''batch'''}
								_UpperCamelCase       : Tuple    =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
								self.fill_with_past_key_values_(_snake_case  ,					direction='''inputs'''     )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						_UpperCamelCase       : Optional[int]    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]     )
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : List[str]    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Dict    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						_UpperCamelCase       : Optional[int]    =      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     )				->    Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : int    =      super().outputs
				else:
						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     ).outputs
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : int    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

				# Generate decoder inputs
				_UpperCamelCase       : Optional[int]    =      seq_length if not self.use_past else 1
				_UpperCamelCase       : Optional[int]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
				_UpperCamelCase       : Optional[int]    =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				_UpperCamelCase       : Any    =      dict(**_snake_case  ,					**_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
						_UpperCamelCase,							_UpperCamelCase       : Dict    =      common_inputs['''input_ids'''].shape
						_UpperCamelCase       : Dict    =      common_inputs['''decoder_input_ids'''].shape[1]
						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_attention_heads
						_UpperCamelCase       : List[Any]    =      (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						_UpperCamelCase       : int    =      decoder_seq_length + 3
						_UpperCamelCase       : Union[str, Any]    =      (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						_UpperCamelCase       : Any    =      torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )

						_UpperCamelCase       : Any    =      []
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						_UpperCamelCase,							_UpperCamelCase       : int    =      self.num_layers
						_UpperCamelCase       : Union[str, Any]    =      min(_snake_case  ,					_snake_case     )
						_UpperCamelCase       : Union[str, Any]    =      max(_snake_case  ,					_snake_case     ) - min_num_layers
						_UpperCamelCase       : Optional[int]    =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

						for _ in range(_snake_case     ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								    )     )
						# TODO: test this.
						_UpperCamelCase       : Optional[Any]    =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
						for _ in range(_snake_case  ,					_snake_case     ):
								common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : Dict    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_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
						_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      common_inputs['''input_ids'''].shape
						# Not using the same length for past_key_values
						_UpperCamelCase       : str    =      seqlen + 2
						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_layers
						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_attention_heads
						_UpperCamelCase       : List[str]    =      (
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						_UpperCamelCase       : Optional[int]    =      common_inputs['''attention_mask'''].dtype
						_UpperCamelCase       : Union[str, Any]    =      torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
						_UpperCamelCase       : int    =      [
						    (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
						]
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				# 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
				_UpperCamelCase       : Union[str, Any]    =      compute_effective_axis_dimension(
				    _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
				_UpperCamelCase       : List[Any]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
				_UpperCamelCase       : List[str]    =      compute_effective_axis_dimension(
				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=_snake_case     )

				# Generate dummy inputs according to compute batch and sequence
				_UpperCamelCase       : Optional[Any]    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
				_UpperCamelCase       : Any    =      dict(tokenizer(_snake_case  ,					return_tensors=_snake_case     )     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )

				elif self.task == "causal-lm":
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_causal_lm(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
				else:
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )

				return common_inputs




		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
				else:
						_UpperCamelCase       : int    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )





