
'''simple docstring'''







import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
						from transformers import WhisperFeatureExtractor, WhisperProcessor


_snake_case					: Optional[Any]     			=					50_358
_snake_case					: Any     			=					50_362


@require_torch
@require_torchaudio
@require_sentencepiece
class       lowerCAmelCase			(					unittest.TestCase      ):




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"openai/whisper-small.en"
								_SCREAMING_SNAKE_CASE					=			tempfile.mkdtemp()




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								return WhisperTokenizer.from_pretrained(self.checkpoint       ,    **_UpperCAmelCase   )




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								return WhisperFeatureExtractor.from_pretrained(self.checkpoint       ,    **_UpperCAmelCase   )




		def      lowercase							(					self   ):
								shutil.rmtree(self.tmpdirname   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )

								processor.save_pretrained(self.tmpdirname   )
								_SCREAMING_SNAKE_CASE					=			WhisperProcessor.from_pretrained(self.tmpdirname   )

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

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




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()   )
								processor.save_pretrained(self.tmpdirname   )

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

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor.from_pretrained(
								    self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=_UpperCAmelCase       ,    padding_value=1.0   )

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

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




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )

								_SCREAMING_SNAKE_CASE					=			floats_list((3, 1_000)   )

								_SCREAMING_SNAKE_CASE					=			feature_extractor(_UpperCAmelCase       ,    return_tensors="np"   )
								_SCREAMING_SNAKE_CASE					=			processor(_UpperCAmelCase       ,    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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )

								_SCREAMING_SNAKE_CASE					=			"This is a test string"

								_SCREAMING_SNAKE_CASE					=			processor(text=_UpperCAmelCase   )

								_SCREAMING_SNAKE_CASE					=			tokenizer(_UpperCAmelCase   )

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




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )

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

								_SCREAMING_SNAKE_CASE					=			processor.batch_decode(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			tokenizer.batch_decode(_UpperCAmelCase   )

								self.assertListEqual(_UpperCAmelCase       ,    _UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )

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




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=_UpperCAmelCase       ,    feature_extractor=_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			processor.get_decoder_prompt_ids(task="transcribe"       ,    no_timestamps=_UpperCAmelCase   )

								self.assertIsInstance(_UpperCAmelCase       ,    _UpperCAmelCase   )
								for ids in forced_decoder_ids:
														self.assertIsInstance(_UpperCAmelCase       ,    (list, tuple)   )

								_SCREAMING_SNAKE_CASE					=			[TRANSCRIBE, NOTIMESTAMPS]
								self.assertListEqual([ids[-1] for ids in forced_decoder_ids]       ,    _UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()   )
								_SCREAMING_SNAKE_CASE					=			processor.get_prompt_ids("Mr. Quilter"   )
								_SCREAMING_SNAKE_CASE					=			processor.tokenizer.decode(_UpperCAmelCase   )

								self.assertListEqual(prompt_ids.tolist()       ,    [50_360, 1_770, 13, 2_264, 346, 353]   )
								self.assertEqual(_UpperCAmelCase       ,    "<|startofprev|> Mr. Quilter"   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()   )
								_SCREAMING_SNAKE_CASE					=			processor.get_prompt_ids(""   )
								_SCREAMING_SNAKE_CASE					=			processor.tokenizer.decode(_UpperCAmelCase   )

								self.assertListEqual(prompt_ids.tolist()       ,    [50_360, 220]   )
								self.assertEqual(_UpperCAmelCase       ,    "<|startofprev|> "   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			WhisperProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()   )

								def _test_prompt_error_raised_helper(UpperCamelCase       ,    UpperCamelCase   ):
														with pytest.raises(_UpperCAmelCase   ) as excinfo:
																				processor.get_prompt_ids(_UpperCAmelCase   )
														_SCREAMING_SNAKE_CASE					=			F'Encountered text in the prompt corresponding to disallowed special token: {special_token}.'
														self.assertEqual(_UpperCAmelCase       ,    str(excinfo.value   )   )

								_test_prompt_error_raised_helper("<|startofprev|> test"       ,    "<|startofprev|>"   )
								_test_prompt_error_raised_helper("test <|notimestamps|>"       ,    "<|notimestamps|>"   )
								_test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>"       ,    "<|zh|>"   )

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      'blenderbot'
      a		:List[str]						 =      ['past_key_values']
      a		:Any						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=8_008					,					_UpperCAmelCase=128					,					_UpperCAmelCase=2					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=24					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=2_560					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=1					,					_UpperCAmelCase=False					,					_UpperCAmelCase=0					,					_UpperCAmelCase=1					,					_UpperCAmelCase=2					,					_UpperCAmelCase=3					,					_UpperCAmelCase=2					,					**_UpperCAmelCase					,					):
            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_						=      use_cache
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=_UpperCAmelCase					,					bos_token_id=_UpperCAmelCase					,					eos_token_id=_UpperCAmelCase					,					is_encoder_decoder=_UpperCAmelCase					,					decoder_start_token_id=_UpperCAmelCase					,					encoder_no_repeat_ngram_size=_UpperCAmelCase					,					forced_eos_token_id=_UpperCAmelCase					,					**_UpperCAmelCase					,					)


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      @property
      def lowercase__		( self):
            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_(_UpperCAmelCase					,					direction='''inputs''')
            elif self.task == "causal-lm":
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              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
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super().outputs
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self).outputs
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            return common_outputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            # Generate decoder inputs
            lowerCAmelCase_						=      seq_length if not self.use_past else 1
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowerCAmelCase_						=      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
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowerCAmelCase_			,		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
                  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(_UpperCAmelCase					,					_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      []
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

                  for _ in range(_UpperCAmelCase):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                            ))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      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
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      seqlen
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_			,		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(_UpperCAmelCase					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      [
                      (torch.zeros(_UpperCAmelCase), torch.zeros(_UpperCAmelCase)) for _ in range(_UpperCAmelCase)
                  ]
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            # 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
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      tokenizer.num_special_tokens_to_add(_UpperCAmelCase)
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      [''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
            lowerCAmelCase_						=      dict(tokenizer(_UpperCAmelCase					,					return_tensors=_UpperCAmelCase))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_causal_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super()._flatten_past_key_values_(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self)._flatten_past_key_values_(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if direction not in ["inputs", "outputs"]:
                  raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

            lowerCAmelCase_						=      '''past_key_values''' if direction == '''inputs''' else '''present'''
            lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

            lowerCAmelCase_						=      '''past_encoder_sequence'''
            lowerCAmelCase_						=      '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

            for i in range(_UpperCAmelCase):
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}



