


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					=					{
    '''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_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				int							=		"bigbird_pegasus"
   __A			:				Union[str, Any]							=		["past_key_values"]
   __A			:				int							=		{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
   }






   def __init__(		self						,    __A=9_6103						,    __A=4096						,    __A=16						,    __A=4096						,    __A=16						,    __A=16						,    __A=4096						,    __A=16						,    __A=0.0						,    __A=0.0						,    __A=True						,    __A=True						,    __A="gelu_new"						,    __A=1024						,    __A=0.1						,    __A=0.0						,    __A=0.0						,    __A=0.02						,    __A=2						,    __A=0.0						,    __A=True						,    __A=0						,    __A=2						,    __A=1						,    __A="block_sparse"						,    __A=64						,    __A=3						,    __A=False						,    **__A						,    ):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    vocab_size
     lowerCamelCase			: Any   =    max_position_embeddings
     lowerCamelCase			: str   =    d_model
     lowerCamelCase			: Dict   =    encoder_ffn_dim
     lowerCamelCase			: List[str]   =    encoder_layers
     lowerCamelCase			: Union[str, Any]   =    encoder_attention_heads
     lowerCamelCase			: Optional[int]   =    decoder_ffn_dim
     lowerCamelCase			: Tuple   =    decoder_layers
     lowerCamelCase			: List[str]   =    decoder_attention_heads
     lowerCamelCase			: int   =    dropout
     lowerCamelCase			: Any   =    attention_dropout
     lowerCamelCase			: Optional[Any]   =    activation_dropout
     lowerCamelCase			: int   =    activation_function
     lowerCamelCase			: List[str]   =    init_std
     lowerCamelCase			: Tuple   =    encoder_layerdrop
     lowerCamelCase			: List[Any]   =    decoder_layerdrop
     lowerCamelCase			: Optional[int]   =    classifier_dropout
     lowerCamelCase			: Optional[Any]   =    use_cache
     lowerCamelCase			: Tuple   =    encoder_layers
     lowerCamelCase			: Optional[int]   =    scale_embedding  # scale factor will be sqrt(d_model) if True

     # extra config
     lowerCamelCase			: List[Any]   =    attention_type
     lowerCamelCase			: int   =    block_size
     lowerCamelCase			: Tuple   =    num_random_blocks
     lowerCamelCase			: Optional[Any]   =    use_bias

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



class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   @property
   def _snake_case   (		self			):






     """simple docstring"""





     if self.task in ["default", "seq2seq-lm"]:
       lowerCamelCase			: Optional[int]   =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]			)

       if self.use_past:
         lowerCamelCase			: Optional[Any]   =    {0: "batch"}
         lowerCamelCase			: List[str]   =    {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
         lowerCamelCase			: Optional[Any]   =    {0: "batch", 1: "decoder_sequence"}
         lowerCamelCase			: Tuple   =    {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
         self.fill_with_past_key_values_(__A						,    direction="inputs"			)
     elif self.task == "causal-lm":
       # TODO: figure this case out.
       lowerCamelCase			: str   =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]			)
       if self.use_past:
         lowerCamelCase   ,     lowerCamelCase			: int   =    self.num_layers
         for i in range(__A			):
           lowerCamelCase			: Optional[int]   =    {0: "batch", 2: "past_sequence + sequence"}
           lowerCamelCase			: List[str]   =    {0: "batch", 2: "past_sequence + sequence"}
     else:
       lowerCamelCase			: str   =    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			):






     """simple docstring"""





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






   def _snake_case   (		self						,    __A						,    __A = -1						,    __A = -1						,    __A = False						,    __A = None						,    ):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A						,    __A						,    __A						,    __A						,    __A			)

     # Generate decoder inputs
     lowerCamelCase			: Tuple   =    seq_length if not self.use_past else 1
     lowerCamelCase			: Any   =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A						,    __A						,    __A						,    __A						,    __A			)
     lowerCamelCase			: List[str]   =    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     lowerCamelCase			: Optional[Any]   =    dict(**__A						,    **__A			)

     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			: str   =    common_inputs["input_ids"].shape
       lowerCamelCase			: List[str]   =    common_inputs["decoder_input_ids"].shape[1]
       lowerCamelCase   ,     lowerCamelCase			: List[str]   =    self.num_attention_heads
       lowerCamelCase			: Tuple   =    (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       lowerCamelCase			: Tuple   =    decoder_seq_length + 3
       lowerCamelCase			: Optional[int]   =    (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       lowerCamelCase			: Optional[int]   =    torch.cat(
           [common_inputs["decoder_attention_mask"], torch.ones(__A						,    __A			)]						,    dim=1			)

       lowerCamelCase			: Dict   =    []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       lowerCamelCase   ,     lowerCamelCase			: List[str]   =    self.num_layers
       lowerCamelCase			: List[Any]   =    min(__A						,    __A			)
       lowerCamelCase			: List[Any]   =    max(__A						,    __A			) - min_num_layers
       lowerCamelCase			: List[str]   =    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

       for _ in range(__A			):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__A			),
                 torch.zeros(__A			),
                 torch.zeros(__A			),
                 torch.zeros(__A			),
             )			)
       # TODO: test this.
       lowerCamelCase			: Union[str, Any]   =    encoder_shape if remaining_side_name == "encoder" else decoder_shape
       for _ in range(__A						,    __A			):
         common_inputs["past_key_values"].append((torch.zeros(__A			), torch.zeros(__A			))			)
     return common_inputs






   def _snake_case   (		self						,    __A						,    __A = -1						,    __A = -1						,    __A = False						,    __A = None						,    ):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A						,    __A						,    __A						,    __A						,    __A			)

     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			: int   =    common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       lowerCamelCase			: Optional[Any]   =    seqlen + 2
       lowerCamelCase   ,     lowerCamelCase			: Optional[Any]   =    self.num_layers
       lowerCamelCase   ,     lowerCamelCase			: Tuple   =    self.num_attention_heads
       lowerCamelCase			: Optional[Any]   =    (
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       lowerCamelCase			: List[Any]   =    common_inputs["attention_mask"].dtype
       lowerCamelCase			: Tuple   =    torch.cat(
           [common_inputs["attention_mask"], torch.ones(__A						,    __A						,    dtype=__A			)]						,    dim=1			)
       lowerCamelCase			: List[Any]   =    [
           (torch.zeros(__A			), torch.zeros(__A			)) for _ in range(__A			)
       ]
     return common_inputs






   def _snake_case   (		self						,    __A						,    __A = -1						,    __A = -1						,    __A = False						,    __A = None						,    ):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    compute_effective_axis_dimension(
         __A						,    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			: Union[str, Any]   =    tokenizer.num_special_tokens_to_add(__A			)
     lowerCamelCase			: List[Any]   =    compute_effective_axis_dimension(
         __A						,    fixed_dimension=OnnxConfig.default_fixed_sequence						,    num_token_to_add=__A			)

     # Generate dummy inputs according to compute batch and sequence
     lowerCamelCase			: Optional[int]   =    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
     lowerCamelCase			: Any   =    dict(tokenizer(__A						,    return_tensors=__A			)			)
     return common_inputs






   def _snake_case   (		self						,    __A						,    __A = -1						,    __A = -1						,    __A = False						,    __A = None						,    ):






     """simple docstring"""





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

     elif self.task == "causal-lm":
       lowerCamelCase			: Dict   =    self._generate_dummy_inputs_for_causal_lm(
           __A						,    batch_size=__A						,    seq_length=__A						,    is_pair=__A						,    framework=__A			)
     else:
       lowerCamelCase			: Union[str, Any]   =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __A						,    batch_size=__A						,    seq_length=__A						,    is_pair=__A						,    framework=__A			)

     return common_inputs






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A			):






     """simple docstring"""





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







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    tempfile.mkdtemp()

     lowerCamelCase			: List[Any]   =    CLIPImageProcessor()
     lowerCamelCase			: str   =    BertTokenizer.from_pretrained(
         "hf-internal-testing/tiny-random-BertModel"						,    model_input_names=["input_ids", "attention_mask"]			)

     lowerCamelCase			: List[str]   =    GitProcessor(__A						,    __A			)

     processor.save_pretrained(self.tmpdirname			)






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).tokenizer






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).image_processor






   def _snake_case   (		self			):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    [np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta			)]

     lowerCamelCase			: Any   =    [Image.fromarray(np.moveaxis(__A						,    0						,    -1			)			) for x in image_inputs]

     return image_inputs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase			: Optional[Any]   =    self.get_tokenizer(bos_token="(BOS)"						,    eos_token="(EOS)"			)
     lowerCamelCase			: List[str]   =    self.get_image_processor(do_normalize=__A						,    padding_value=1.0			)

     lowerCamelCase			: Dict   =    GitProcessor.from_pretrained(
         self.tmpdirname						,    bos_token="(BOS)"						,    eos_token="(EOS)"						,    do_normalize=__A						,    padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer						,    __A			)

     self.assertEqual(processor.image_processor.to_json_string()						,    image_processor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.image_processor						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Any   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: Optional[Any]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: List[str]   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    image_processor(__A						,    return_tensors="np"			)
     lowerCamelCase			: Union[str, Any]   =    processor(images=__A						,    return_tensors="np"			)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1e-2			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    self.get_image_processor()
     lowerCamelCase			: Dict   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: int   =    "lower newer"

     lowerCamelCase			: str   =    processor(text=__A			)

     lowerCamelCase			: Union[str, Any]   =    tokenizer(__A						,    return_token_type_ids=__A			)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: List[str]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: str   =    "lower newer"
     lowerCamelCase			: Tuple   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    processor(text=__A						,    images=__A			)

     self.assertListEqual(list(inputs.keys()			)						,    ["input_ids", "attention_mask", "pixel_values"]			)

     # test if it raises when no input is passed
     with pytest.raises(__A			):
       processor()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.get_image_processor()
     lowerCamelCase			: Tuple   =    self.get_tokenizer()

     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase			: str   =    processor.batch_decode(__A			)
     lowerCamelCase			: int   =    tokenizer.batch_decode(__A			)

     self.assertListEqual(__A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.get_image_processor()
     lowerCamelCase			: int   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    "lower newer"
     lowerCamelCase			: List[Any]   =    self.prepare_image_inputs()

     lowerCamelCase			: Optional[int]   =    processor(text=__A						,    images=__A			)

     # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
     self.assertListEqual(list(inputs.keys()			)						,    ["input_ids", "attention_mask", "pixel_values"]			)



