import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__snake_case						=			logging.get_logger(__name__)







class 	__snake_case      (   lowerCAmelCase__      ):
 __lowerCamelCase			:      Union[str, Any]           =					['''input_features''']
 def __init__(     self						,      snake_case__=80						,      snake_case__=1_6000						,      snake_case__=160						,      snake_case__=30						,      snake_case__=400						,      snake_case__=0.0						,      snake_case__=False						,      **snake_case__						,      )		->							List[Any]:
       '''simple docstring'''





       super().__init__(
           feature_size=__lowerCAmelCase						,      sampling_rate=__lowerCAmelCase						,      padding_value=__lowerCAmelCase						,      return_attention_mask=__lowerCAmelCase						,      **__lowerCAmelCase						,      )
       UpperCAmelCase			:       List[Any]			     =n_fft
       UpperCAmelCase			:       List[Any]			     =hop_length
       UpperCAmelCase			:       int			     =chunk_length
       UpperCAmelCase			:       int			     =chunk_length * sampling_rate
       UpperCAmelCase			:       List[Any]			     =self.n_samples // hop_length
       UpperCAmelCase			:       Union[str, Any]			     =sampling_rate
       UpperCAmelCase			:       int			     =mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2						,      num_mel_filters=__lowerCAmelCase						,      min_frequency=0.0						,      max_frequency=8000.0						,      sampling_rate=__lowerCAmelCase						,      norm='''slaney'''						,      mel_scale='''slaney'''						,      )
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =spectrogram(
           __lowerCAmelCase						,      window_function(self.n_fft						,      '''hann'''  )						,      frame_length=self.n_fft						,      hop_length=self.hop_length						,      power=2.0						,      mel_filters=self.mel_filters						,      log_mel='''log10'''						,      )
       UpperCAmelCase			:       Optional[Any]			     =log_spec[:, :-1]
       UpperCAmelCase			:       Optional[Any]			     =np.maximum(__lowerCAmelCase						,      log_spec.max() - 8.0  )
       UpperCAmelCase			:       str			     =(log_spec + 4.0) / 4.0
       return log_spec
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 		UpperCAmelCase__				(     snake_case__						,      snake_case__						,      snake_case__ = 0.0  )		->							List[Any]:
       '''simple docstring'''





       if attention_mask is not None:
             UpperCAmelCase			:       int			     =np.array(__lowerCAmelCase						,      np.intaa  )
             UpperCAmelCase			:       Dict			     =[]

             for vector, length in zip(__lowerCAmelCase						,      attention_mask.sum(-1  )  ):
                   UpperCAmelCase			:       Tuple			     =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7  )
                   if length < normed_slice.shape[0]:
                         UpperCAmelCase			:       str			     =padding_value

                   normed_input_values.append(__lowerCAmelCase  )
       else:
             UpperCAmelCase			:       List[str]			     =[(x - x.mean()) / np.sqrt(x.var() + 1e-7  ) for x in input_values]

       return normed_input_values
 def __call__(     self						,      snake_case__						,      snake_case__ = True						,      snake_case__ = None						,      snake_case__ = None						,      snake_case__ = None						,      snake_case__ = "max_length"						,      snake_case__ = None						,      snake_case__ = None						,      snake_case__ = None						,      **snake_case__						,      )		->							List[str]:
       '''simple docstring'''





       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                       f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                       f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''  )
       else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''  )

       UpperCAmelCase			:       str			     =isinstance(__lowerCAmelCase						,      np.ndarray  ) and len(raw_speech.shape  ) > 1
       if is_batched_numpy and len(raw_speech.shape  ) > 2:
             raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''  )
       UpperCAmelCase			:       List[Any]			     =is_batched_numpy or (
           isinstance(__lowerCAmelCase						,      (list, tuple)  ) and (isinstance(raw_speech[0]						,      (np.ndarray, tuple, list)  ))
       )

       if is_batched:
             UpperCAmelCase			:       Union[str, Any]			     =[np.asarray([speech]						,      dtype=np.floataa  ).T for speech in raw_speech]
       elif not is_batched and not isinstance(__lowerCAmelCase						,      np.ndarray  ):
             UpperCAmelCase			:       Optional[Any]			     =np.asarray(__lowerCAmelCase						,      dtype=np.floataa  )
       elif isinstance(__lowerCAmelCase						,      np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             UpperCAmelCase			:       List[Any]			     =raw_speech.astype(np.floataa  )

       # always return batch
       if not is_batched:
             UpperCAmelCase			:       List[str]			     =[np.asarray([raw_speech]  ).T]

       UpperCAmelCase			:       Optional[Any]			     =BatchFeature({'''input_features''': raw_speech}  )

       # convert into correct format for padding

       UpperCAmelCase			:       Union[str, Any]			     =self.pad(
           __lowerCAmelCase						,      padding=__lowerCAmelCase						,      max_length=max_length if max_length else self.n_samples						,      truncation=__lowerCAmelCase						,      pad_to_multiple_of=__lowerCAmelCase						,      return_attention_mask=return_attention_mask or do_normalize						,      )

       # zero-mean and unit-variance normalization
       if do_normalize:
             UpperCAmelCase			:       List[Any]			     =self.zero_mean_unit_var_norm(
                 padded_inputs['''input_features''']						,      attention_mask=padded_inputs['''attention_mask''']						,      padding_value=self.padding_value						,      )
             UpperCAmelCase			:       Optional[int]			     =np.stack(padded_inputs['''input_features''']						,      axis=0  )

       # make sure list is in array format
       UpperCAmelCase			:       Optional[int]			     =padded_inputs.get('''input_features'''  ).transpose(2						,      0						,      1  )

       UpperCAmelCase			:       Any			     =[self._np_extract_fbank_features(__lowerCAmelCase  ) for waveform in input_features[0]]

       if isinstance(input_features[0]						,      __lowerCAmelCase  ):
             UpperCAmelCase			:       List[Any]			     =[np.asarray(__lowerCAmelCase						,      dtype=np.floataa  ) for feature in input_features]
       else:
             UpperCAmelCase			:       List[Any]			     =input_features

       if return_attention_mask:
             # rescale from sample (48000) to feature (3000)
             UpperCAmelCase			:       Optional[int]			     =padded_inputs['''attention_mask'''][:, :: self.hop_length]

       if return_tensors is not None:
             UpperCAmelCase			:       int			     =padded_inputs.convert_to_tensors(__lowerCAmelCase  )

       return padded_inputs






 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Any			     =copy.deepcopy(self.__dict__  )
       UpperCAmelCase			:       List[str]			     =self.__class__.__name__
       if "mel_filters" in output:
             del output["mel_filters"]
       return output

'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ViTImageProcessor if is_vision_available() else None



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (3, 32, 128)
					lowerCAmelCase   					=  tempfile.mkdtemp()

					# fmt: off
					lowerCAmelCase   					=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
					# fmt: on
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))

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

					lowerCAmelCase   					=  {
					    """do_normalize""": False,
					    """do_resize""": True,
					    """image_processor_type""": """ViTImageProcessor""",
					    """resample""": 3,
					    """size""": {"""height""": 32, """width""": 128},
					}
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    __lowerCAmelCase)
					with open(self.image_processor_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							json.dump(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return ViTImageProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)

					lowerCAmelCase   					=  Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1))

					return image_input



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)
					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(self.tmpdirname       ,    use_fast=__lowerCAmelCase)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)""")
					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(
					    self.tmpdirname       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer(__lowerCAmelCase)
					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

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

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

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

					lowerCAmelCase   					=  processor.char_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  tokenizer.batch_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  [seq.replace(""" """       ,    """""") for seq in decoded_tok]

					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    processor.model_input_names)







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    38)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    50257)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    30522)

					lowerCAmelCase   					=  processor.batch_decode([char_input, bpe_input, wp_input])

					self.assertListEqual(list(results.keys())       ,    ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




