



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				:							Union[str, Any]								=logging.get_logger(__name__)

UpperCAmelCase				:							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  _lowercase				(lowerCAmelCase__ ):
    '''simple docstring'''







    lowercase__				  =  """bigbird_pegasus"""
    lowercase__				  =  ["""past_key_values"""]
    lowercase__				  =  {
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }

    def __init__(	self       ,	snake_case__=9_6103       ,	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__       ,	):

         '''simple docstring'''


         UpperCamelCase_											=				vocab_size
         UpperCamelCase_											=				max_position_embeddings
         UpperCamelCase_											=				d_model
         UpperCamelCase_											=				encoder_ffn_dim
         UpperCamelCase_											=				encoder_layers
         UpperCamelCase_											=				encoder_attention_heads
         UpperCamelCase_											=				decoder_ffn_dim
         UpperCamelCase_											=				decoder_layers
         UpperCamelCase_											=				decoder_attention_heads
         UpperCamelCase_											=				dropout
         UpperCamelCase_											=				attention_dropout
         UpperCamelCase_											=				activation_dropout
         UpperCamelCase_											=				activation_function
         UpperCamelCase_											=				init_std
         UpperCamelCase_											=				encoder_layerdrop
         UpperCamelCase_											=				decoder_layerdrop
         UpperCamelCase_											=				classifier_dropout
         UpperCamelCase_											=				use_cache
         UpperCamelCase_											=				encoder_layers
         UpperCamelCase_											=				scale_embedding  # scale factor will be sqrt(d_model) if True

         # extra config
         UpperCamelCase_											=				attention_type
         UpperCamelCase_											=				block_size
         UpperCamelCase_											=				num_random_blocks
         UpperCamelCase_											=				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  _lowercase				(lowerCAmelCase__ ):
    '''simple docstring'''







    @property
    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

              if self.use_past:
                   UpperCamelCase_											=				{0: '''batch'''}
                   UpperCamelCase_											=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
              else:
                   UpperCamelCase_											=				{0: '''batch''', 1: '''decoder_sequence'''}
                   UpperCamelCase_											=				{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.
              UpperCamelCase_											=				OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                      ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ]      )
              if self.use_past:
                   UpperCamelCase_											=				self.num_layers
                   for i in range(_A      ):
                        UpperCamelCase_											=				{0: '''batch''', 2: '''past_sequence + sequence'''}
                        UpperCamelCase_											=				{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
              UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = -1       ,	snake_case__ = -1       ,	snake_case__ = False       ,	snake_case__ = None       ,	):

         '''simple docstring'''


         UpperCamelCase_											=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A       ,	_A       ,	_A       ,	_A       ,	_A      )

         # Generate decoder inputs
         UpperCamelCase_											=				seq_length if not self.use_past else 1
         UpperCamelCase_											=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A       ,	_A       ,	_A       ,	_A       ,	_A      )
         UpperCamelCase_											=				{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_											=				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
              UpperCamelCase_											=				common_inputs['''input_ids'''].shape
              UpperCamelCase_											=				common_inputs['''decoder_input_ids'''].shape[1]
              UpperCamelCase_											=				self.num_attention_heads
              UpperCamelCase_											=				(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase_											=				decoder_seq_length + 3
              UpperCamelCase_											=				(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCamelCase_											=				torch.cat(
                  [common_inputs["decoder_attention_mask"], torch.ones(_A       ,	_A      )]       ,	dim=1      )

              UpperCamelCase_											=				[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase_											=				self.num_layers
              UpperCamelCase_											=				min(_A       ,	_A      )
              UpperCamelCase_											=				max(_A       ,	_A      ) - min_num_layers
              UpperCamelCase_											=				'''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.
              UpperCamelCase_											=				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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = -1       ,	snake_case__ = -1       ,	snake_case__ = False       ,	snake_case__ = None       ,	):

         '''simple docstring'''


         UpperCamelCase_											=				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
              UpperCamelCase_											=				common_inputs['''input_ids'''].shape
              # Not using the same length for past_key_values
              UpperCamelCase_											=				seqlen + 2
              UpperCamelCase_											=				self.num_layers
              UpperCamelCase_											=				self.num_attention_heads
              UpperCamelCase_											=				(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCamelCase_											=				common_inputs['''attention_mask'''].dtype
              UpperCamelCase_											=				torch.cat(
                  [common_inputs["attention_mask"], torch.ones(_A       ,	_A       ,	dtype=_A      )]       ,	dim=1      )
              UpperCamelCase_											=				[
                  (torch.zeros(_A      ), torch.zeros(_A      )) for _ in range(_A      )
              ]
         return common_inputs

    def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = -1       ,	snake_case__ = -1       ,	snake_case__ = False       ,	snake_case__ = None       ,	):

         '''simple docstring'''


         UpperCamelCase_											=				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
         UpperCamelCase_											=				tokenizer.num_special_tokens_to_add(_A      )
         UpperCamelCase_											=				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
         UpperCamelCase_											=				[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
         UpperCamelCase_											=				dict(tokenizer(_A       ,	return_tensors=_A      )      )
         return common_inputs

    def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = -1       ,	snake_case__ = -1       ,	snake_case__ = False       ,	snake_case__ = None       ,	):

         '''simple docstring'''


         if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase_											=				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":
              UpperCamelCase_											=				self._generate_dummy_inputs_for_causal_lm(
                  _A       ,	batch_size=_A       ,	seq_length=_A       ,	is_pair=_A       ,	framework=_A      )
         else:
              UpperCamelCase_											=				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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

         '''simple docstring'''


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





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "      )
									UpperCamelCase_											=				dict(zip(snake_case__       ,	range(len(snake_case__      )      )      )      )

									UpperCamelCase_											=				{
									    "pad_token": "<pad>",
									    "unk_token": "<unk>",
									    "bos_token": "<s>",
									    "eos_token": "</s>",
									}
									UpperCamelCase_											=				{
									    "feature_size": 1,
									    "padding_value": 0.0,
									    "sampling_rate": 1_6000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}

									UpperCamelCase_											=				tempfile.mkdtemp()
									UpperCamelCase_											=				os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["vocab_file"]      )
									UpperCamelCase_											=				os.path.join(self.tmpdirname       ,	snake_case__      )
									with open(self.vocab_file       ,	"w"       ,	encoding="utf-8"      ) as fp:
														fp.write(json.dumps(snake_case__      ) + "\n"      )

									with open(self.feature_extraction_file       ,	"w"       ,	encoding="utf-8"      ) as fp:
														fp.write(json.dumps(snake_case__      ) + "\n"      )

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.add_kwargs_tokens_map.copy()
									kwargs.update(snake_case__      )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,	**snake_case__      )

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,	**snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									shutil.rmtree(self.tmpdirname      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_tokenizer()
									UpperCamelCase_											=				self.get_feature_extractor()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCamelCase_											=				WavaVecaProcessor.from_pretrained(self.tmpdirname      )

									self.assertEqual(processor.tokenizer.get_vocab()       ,	tokenizer.get_vocab()      )
									self.assertIsInstance(processor.tokenizer       ,	snake_case__      )

									self.assertEqual(processor.feature_extractor.to_json_string()       ,	feature_extractor.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCamelCase_											=				self.get_tokenizer(bos_token="(BOS)"       ,	eos_token="(EOS)"      )
									UpperCamelCase_											=				self.get_feature_extractor(do_normalize=snake_case__       ,	padding_value=1.0      )

									UpperCamelCase_											=				WavaVecaProcessor.from_pretrained(
									    self.tmpdirname       ,	bos_token="(BOS)"       ,	eos_token="(EOS)"       ,	do_normalize=snake_case__       ,	padding_value=1.0      )

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

									self.assertEqual(processor.feature_extractor.to_json_string()       ,	feature_extractor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				floats_list((3, 1000)      )

									UpperCamelCase_											=				feature_extractor(snake_case__       ,	return_tensors="np"      )
									UpperCamelCase_											=				processor(snake_case__       ,	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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				"This is a test string"

									UpperCamelCase_											=				processor(text=snake_case__      )

									UpperCamelCase_											=				tokenizer(snake_case__      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									UpperCamelCase_											=				processor.batch_decode(snake_case__      )
									UpperCamelCase_											=				tokenizer.batch_decode(snake_case__      )

									self.assertListEqual(snake_case__       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									self.assertListEqual(
									    processor.model_input_names       ,	feature_extractor.model_input_names       ,	msg="`processor` and `feature_extractor` model input names do not match"       ,	)
