

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


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Union[str, Any]          =					'mbart'
      lowerCamelCase_	:      int          =					['past_key_values']
      lowerCamelCase_	:      List[Any]          =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__(     self							,  lowerCamelCase=50265							,  lowerCamelCase=1024							,  lowerCamelCase=12							,  lowerCamelCase=4096							,  lowerCamelCase=16							,  lowerCamelCase=12							,  lowerCamelCase=4096							,  lowerCamelCase=16							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase="gelu"							,  lowerCamelCase=1024							,  lowerCamelCase=0.1							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.02							,  lowerCamelCase=0.0							,  lowerCamelCase=False							,  lowerCamelCase=1							,  lowerCamelCase=0							,  lowerCamelCase=2							,  lowerCamelCase=2							,  **lowerCamelCase							,  )						->       Tuple:
        snake_case_       						=      vocab_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      d_model
        snake_case_       						=      encoder_ffn_dim
        snake_case_       						=      encoder_layers
        snake_case_       						=      encoder_attention_heads
        snake_case_       						=      decoder_ffn_dim
        snake_case_       						=      decoder_layers
        snake_case_       						=      decoder_attention_heads
        snake_case_       						=      dropout
        snake_case_       						=      attention_dropout
        snake_case_       						=      activation_dropout
        snake_case_       						=      activation_function
        snake_case_       						=      init_std
        snake_case_       						=      encoder_layerdrop
        snake_case_       						=      decoder_layerdrop
        snake_case_       						=      classifier_dropout
        snake_case_       						=      use_cache
        snake_case_       						=      encoder_layers
        snake_case_       						=      scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  is_encoder_decoder=lowerCamelCase							,  forced_eos_token_id=lowerCamelCase							,  **lowerCamelCase							,  )







class  __lowerCamelCase				(	__snake_case	):







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

          if self.use_past:
            snake_case_       						=      {0: """batch"""}
            snake_case_       						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
            snake_case_       						=      {0: """batch""", 1: """decoder_sequence"""}
            snake_case_       						=      {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase							,  direction="""inputs"""   )
        elif self.task == "causal-lm":
          # TODO: figure this case out.
          snake_case_       						=      OrderedDict(
              [
                  ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
              ]   )
          if self.use_past:
            snake_case_						,				snake_case_       						=      self.num_layers
            for i in range(lowerCamelCase   ):
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        else:
          snake_case_       						=      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   )						->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      super().outputs
        else:
          snake_case_       						=      super(lowerCamelCase							,  self   ).outputs
          if self.use_past:
            snake_case_						,				snake_case_       						=      self.num_layers
            for i in range(lowerCamelCase   ):
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

        # Generate decoder inputs
        snake_case_       						=      seq_length if not self.use_past else 1
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
        snake_case_       						=      {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        snake_case_       						=      dict(**lowerCamelCase							,  **lowerCamelCase   )

        if self.use_past:
          if not is_torch_available():
            raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
          else:
            import torch
          snake_case_						,				snake_case_       						=      common_inputs["""input_ids"""].shape
          snake_case_       						=      common_inputs["""decoder_input_ids"""].shape[1]
          snake_case_						,				snake_case_       						=      self.num_attention_heads
          snake_case_       						=      (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          snake_case_       						=      decoder_seq_length + 3
          snake_case_       						=      (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          snake_case_       						=      torch.cat(
              [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase							,  lowerCamelCase   )]							,  dim=1   )

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

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

        if self.use_past:
          if not is_torch_available():
            raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
          else:
            import torch
          snake_case_						,				snake_case_       						=      common_inputs["""input_ids"""].shape
          # Not using the same length for past_key_values
          snake_case_       						=      seqlen + 2
          snake_case_						,				snake_case_       						=      self.num_layers
          snake_case_						,				snake_case_       						=      self.num_attention_heads
          snake_case_       						=      (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = 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
        snake_case_       						=      compute_effective_axis_dimension(
            lowerCamelCase							,  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
        snake_case_       						=      tokenizer.num_special_tokens_to_add(lowerCamelCase   )
        snake_case_       						=      compute_effective_axis_dimension(
            lowerCamelCase							,  fixed_dimension=OnnxConfig.default_fixed_sequence							,  num_token_to_add=lowerCamelCase   )

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowerCamelCase							,  batch_size=lowerCamelCase							,  seq_length=lowerCamelCase							,  is_pair=lowerCamelCase							,  framework=lowerCamelCase   )

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

        return common_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Dict:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      super()._flatten_past_key_values_(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
        else:
          snake_case_       						=      super(lowerCamelCase							,  self   )._flatten_past_key_values_(
              lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

"""simple docstring"""

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    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self							)  ->    Any:
          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(lowercase_      ,				range(len(lowercase_							)							)							)							)

          UpperCAmelCase  	=    {
              'pad_token': '<pad>',
              'unk_token': '<unk>',
              'bos_token': '<s>',
              'eos_token': '</s>',
          }
          UpperCAmelCase  	=    {
              'feature_size': 1,
              'padding_value': 0.0,
              'sampling_rate': 1_6_0_0_0,
              '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      ,				lowercase_							)
          with open(self.vocab_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)

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




      def 			a_					(							self      ,				**lowercase_							)  ->    Any:
          UpperCAmelCase  	=    self.add_kwargs_tokens_map.copy()
          kwargs.update(lowercase_							)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Dict:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self							)  ->    Any:
          shutil.rmtree(self.tmpdirname							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_tokenizer()
          UpperCAmelCase  	=    self.get_feature_extractor()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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

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

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




      def 			a_					(							self							)  ->    Dict:
          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=lowercase_      ,				padding_value=1.0							)

          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(
              self.tmpdirname      ,				bos_token='(BOS)'      ,				eos_token='(EOS)'      ,				do_normalize=lowercase_      ,				padding_value=1.0							)

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

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




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    floats_list((3, 1_0_0_0)							)

          UpperCAmelCase  	=    feature_extractor(lowercase_      ,				return_tensors='np'							)
          UpperCAmelCase  	=    processor(lowercase_      ,				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 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    'This is a test string'

          UpperCAmelCase  	=    processor(text=lowercase_							)

          UpperCAmelCase  	=    tokenizer(lowercase_							)

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




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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

          UpperCAmelCase  	=    processor.batch_decode(lowercase_							)
          UpperCAmelCase  	=    tokenizer.batch_decode(lowercase_							)

          self.assertListEqual(lowercase_      ,				lowercase_							)




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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



