




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


__SCREAMING_SNAKE_CASE    :   List[str]       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   int       ={
    """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 			A			(		__UpperCAmelCase		):
     """simple docstring"""

     _snake_case:	List[Any]     ="bigbird_pegasus"
     _snake_case:	Tuple     =["past_key_values"]
     _snake_case:	Optional[Any]     ={
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
     }




     def __init__(    self  :   Optional[int]	,  A__  :   str=9_6_1_0_3	,  A__  :   Optional[Any]=4_0_9_6	,  A__  :   int=1_6	,  A__  :   Optional[int]=4_0_9_6	,  A__  :   int=1_6	,  A__  :   int=1_6	,  A__  :   List[str]=4_0_9_6	,  A__  :   int=1_6	,  A__  :   Any=0.0	,  A__  :   List[str]=0.0	,  A__  :   List[str]=True	,  A__  :   Optional[int]=True	,  A__  :   List[Any]="gelu_new"	,  A__  :   str=1_0_2_4	,  A__  :   int=0.1	,  A__  :   List[str]=0.0	,  A__  :   Tuple=0.0	,  A__  :   Union[str, Any]=0.02	,  A__  :   List[Any]=2	,  A__  :   Union[str, Any]=0.0	,  A__  :   int=True	,  A__  :   Optional[int]=0	,  A__  :   List[Any]=2	,  A__  :   Optional[Any]=1	,  A__  :   Union[str, Any]="block_sparse"	,  A__  :   Dict=6_4	,  A__  :   str=3	,  A__  :   Tuple=False	,  **A__  :   Union[str, Any]	,  )				->       Tuple:
           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

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

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



class 			A			(		__UpperCAmelCase		):
     """simple docstring"""

     @property
     def  __snake_case   (    self  :   Optional[int]			)				->       Mapping[str, Mapping[int, str]]:

           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_(lowercase_	,  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__        =				self.num_layers
                       for i in range(lowercase_			):
                             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  __snake_case   (    self  :   Dict			)				->       Mapping[str, Mapping[int, str]]:

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




     def  __snake_case   (    self  :   str	,  A__  :   List[str]	,  A__  :   Union[str, Any] = -1	,  A__  :   Union[str, Any] = -1	,  A__  :   Optional[int] = False	,  A__  :   Tuple = None	,  )				->       Mapping[str, Any]:
           lowerCamelCase__        =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_	,  lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

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

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
                 else:
                       import torch
                 lowerCamelCase__        =				common_inputs['''input_ids'''].shape
                 lowerCamelCase__        =				common_inputs['''decoder_input_ids'''].shape[1]
                 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(lowercase_	,  lowercase_			)]	,  dim=1			)

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

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




     def  __snake_case   (    self  :   Dict	,  A__  :   int	,  A__  :   List[Any] = -1	,  A__  :   Tuple = -1	,  A__  :   List[Any] = False	,  A__  :   Optional[int] = None	,  )				->       Mapping[str, Any]:
           lowerCamelCase__        =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_	,  lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
                 else:
                       import torch
                 lowerCamelCase__        =				common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 lowerCamelCase__        =				seqlen + 2
                 lowerCamelCase__        =				self.num_layers
                 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(lowercase_	,  lowercase_	,  dtype=lowercase_			)]	,  dim=1			)
                 lowerCamelCase__        =				[
                     (torch.zeros(lowercase_			), torch.zeros(lowercase_			)) for _ in range(lowercase_			)
                 ]
           return common_inputs




     def  __snake_case   (    self  :   Any	,  A__  :   Tuple	,  A__  :   Union[str, Any] = -1	,  A__  :   Tuple = -1	,  A__  :   Union[str, Any] = False	,  A__  :   Optional[Any] = None	,  )				->       Mapping[str, Any]:
           lowerCamelCase__        =				compute_effective_axis_dimension(
               lowercase_	,  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(lowercase_			)
           lowerCamelCase__        =				compute_effective_axis_dimension(
               lowercase_	,  fixed_dimension=OnnxConfig.default_fixed_sequence	,  num_token_to_add=lowercase_			)

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




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   Dict	,  A__  :   str = -1	,  A__  :   Tuple = -1	,  A__  :   Tuple = False	,  A__  :   Optional[Any] = None	,  )				->       Mapping[str, Any]:

           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__        =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     lowercase_	,  batch_size=lowercase_	,  seq_length=lowercase_	,  is_pair=lowercase_	,  framework=lowercase_			)

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

           return common_inputs




     def  __snake_case   (    self  :   Any	,  A__  :   List[str]	,  A__  :   List[str]	,  A__  :   Any	,  A__  :   List[str]			)				->       Union[str, Any]:

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





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)