





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   :							List[Any]     =	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 						_lowerCAmelCase(						lowercase_):

		"""simple docstring"""







		lowerCamelCase__      					=   '''bigbird_pegasus'''
		lowerCamelCase__      					=   ['''past_key_values''']
		lowerCamelCase__      					=   {
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''hidden_size''': '''d_model''',
		    '''attention_probs_dropout_prob''': '''attention_dropout''',
		}

		def __init__( self  ,    UpperCAmelCase=9_61_03  ,    UpperCAmelCase=40_96  ,    UpperCAmelCase=16  ,    UpperCAmelCase=40_96  ,    UpperCAmelCase=16  ,    UpperCAmelCase=16  ,    UpperCAmelCase=40_96  ,    UpperCAmelCase=16  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase="gelu_new"  ,    UpperCAmelCase=10_24  ,    UpperCAmelCase=0.1  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=2  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=True  ,    UpperCAmelCase=0  ,    UpperCAmelCase=2  ,    UpperCAmelCase=1  ,    UpperCAmelCase="block_sparse"  ,    UpperCAmelCase=64  ,    UpperCAmelCase=3  ,    UpperCAmelCase=False  ,    **UpperCAmelCase  ,    )-> Union[str, Any]:
					__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				 =       classifier_dropout
					__A				 =       use_cache
					__A				 =       encoder_layers
					__A				 =       scale_embedding  # scale factor will be sqrt(d_model) if True

					# extra config
					__A				 =       attention_type
					__A				 =       block_size
					__A				 =       num_random_blocks
					__A				 =       use_bias

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







class 						_lowerCAmelCase(						lowercase_):

		"""simple docstring"""







		@property
		def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:



					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_(UpperCamelCase_  ,    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(UpperCamelCase_     ):
														__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 		SCREAMING_SNAKE_CASE__	( self     )-> int:



					if self.task in ["default", "seq2seq-lm"]:
								__A				 =       super().outputs
					else:
								__A				 =       super(UpperCamelCase_  ,    self     ).outputs
								if self.use_past:
											__A ,						__A				 =       self.num_layers
											for i in range(UpperCamelCase_     ):
														__A				 =       {0: '''batch''', 2: '''past_sequence + sequence'''}
														__A				 =       {0: '''batch''', 2: '''past_sequence + sequence'''}
					return common_outputs

		def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = False  ,    UpperCAmelCase = None  ,    )-> List[str]:
					__A				 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_     )

					# Generate decoder inputs
					__A				 =       seq_length if not self.use_past else 1
					__A				 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_     )
					__A				 =       {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
					__A				 =       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
								__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(UpperCamelCase_  ,    UpperCamelCase_     )]  ,    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(UpperCamelCase_  ,    UpperCamelCase_     )
								__A				 =       max(UpperCamelCase_  ,    UpperCamelCase_     ) - min_num_layers
								__A				 =       '''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.
								__A				 =       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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = False  ,    UpperCAmelCase = None  ,    )-> Dict:
					__A				 =       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
								__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(UpperCamelCase_  ,    UpperCamelCase_  ,    dtype=UpperCamelCase_     )]  ,    dim=1     )
								__A				 =       [
								    (torch.zeros(UpperCamelCase_     ), torch.zeros(UpperCamelCase_     )) for _ in range(UpperCamelCase_     )
								]
					return common_inputs

		def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = False  ,    UpperCAmelCase = None  ,    )-> int:
					__A				 =       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
					__A				 =       tokenizer.num_special_tokens_to_add(UpperCamelCase_     )
					__A				 =       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
					__A				 =       [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
					__A				 =       dict(tokenizer(UpperCamelCase_  ,    return_tensors=UpperCamelCase_     )     )
					return common_inputs

		def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = -1  ,    UpperCAmelCase = False  ,    UpperCAmelCase = None  ,    )-> List[Any]:



					if self.task in ["default", "seq2seq-lm"]:
								__A				 =       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":
								__A				 =       self._generate_dummy_inputs_for_causal_lm(
								    UpperCamelCase_  ,    batch_size=UpperCamelCase_  ,    seq_length=UpperCamelCase_  ,    is_pair=UpperCamelCase_  ,    framework=UpperCamelCase_     )
					else:
								__A				 =       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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Tuple:



					if self.task in ["default", "seq2seq-lm"]:
								__A				 =       super()._flatten_past_key_values_(UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_     )
					else:
								__A				 =       super(UpperCamelCase_  ,    self     )._flatten_past_key_values_(
								    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_     )








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

   for k in range(t - 1     ):
      # select a column of modified matrix A':
      __A				 =       r[k:, [k]]
      # construct first basis vector
      __A				 =       np.zeros_like(snake_case     )
      __A				 =       1.0
      # determine scaling factor
      __A				 =       np.linalg.norm(snake_case     )
      # construct vector v for Householder reflection
      __A				 =       x + np.sign(x[0]     ) * alpha * ea
      v /= np.linalg.norm(snake_case     )

      # construct the Householder matrix
      __A				 =       np.eye(m - k     ) - 2.0 * v @ v.T
      # pad with ones and zeros as necessary
      __A				 =       np.block([[np.eye(snake_case     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

