import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       random.Random()







def      _a					(   lowercase__	:      int  ,	lowercase__	:      str=1.0  ,	lowercase__	:      Optional[int]=None  ,	lowercase__	:      Any=None			):







     '''simple docstring'''


     if rng is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		global_rng

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		[]
     for batch_idx in range(shape[0]			):
          values.append([]			)
          for _ in range(shape[1]			):
               values[-1].append(rng.random() * scale			)

     return values

class    snake_case       (   unittest.TestCase    ):




  def __init__(   self  :						Dict  ,			a_  :						int  ,			a_  :						List[str]=7  ,			a_  :						Dict=400  ,			a_  :						List[str]=2000  ,			a_  :						Any=2048  ,			a_  :						Optional[Any]=128  ,			a_  :						List[str]=1  ,			a_  :						Any=512  ,			a_  :						List[Any]=30  ,			a_  :						List[str]=4_4100  ,			)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		parent
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Any      =		min_seq_length
       SCREAMING_SNAKE_CASE__					:					Tuple      =		max_seq_length
       SCREAMING_SNAKE_CASE__					:					str      =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		spectrogram_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		feature_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_audio_channels
       SCREAMING_SNAKE_CASE__					:					str      =		hop_length
       SCREAMING_SNAKE_CASE__					:					Any      =		chunk_length
       SCREAMING_SNAKE_CASE__					:					str      =		sampling_rate




  def 						__lowercase(   self  :						Optional[Any]					)->							Optional[Any]:




       """simple docstring"""
       return {
           "spectrogram_length": self.spectrogram_length,
           "feature_size": self.feature_size,
           "num_audio_channels": self.num_audio_channels,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "sampling_rate": self.sampling_rate,
       }




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						List[Any]=False  ,			a_  :						Optional[int]=False					)->							List[Any]:




       """simple docstring"""
       def _flatten(a_  :						Optional[Any]					):
            return list(itertools.chain(*a_					)					)

       if equal_length:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
       else:
            # make sure that inputs increase in size
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[
                floats_list((x, self.feature_size)					)
                for x in range(self.min_seq_length  ,			self.max_seq_length  ,			self.seq_length_diff					)
            ]
       if numpify:
            SCREAMING_SNAKE_CASE__					:					Dict      =		[np.asarray(a_					) for x in speech_inputs]
       return speech_inputs




@require_torch
@require_torchaudio
class    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  TvltFeatureExtractor




  def 						__lowercase(   self  :						Tuple					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TvltFeatureExtractionTester(self					)




  def 						__lowercase(   self  :						List[Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.feature_extraction_class(**self.feat_extract_dict					)
       self.assertTrue(hasattr(a_  ,			'spectrogram_length'					)					)
       self.assertTrue(hasattr(a_  ,			'feature_size'					)					)
       self.assertTrue(hasattr(a_  ,			'num_audio_channels'					)					)
       self.assertTrue(hasattr(a_  ,			'hop_length'					)					)
       self.assertTrue(hasattr(a_  ,			'chunk_length'					)					)
       self.assertTrue(hasattr(a_  ,			'sampling_rate'					)					)




  def 						__lowercase(   self  :						Optional[int]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.feature_extraction_class(**self.feat_extract_dict					)

       with tempfile.TemporaryDirectory() as tmpdirname:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		feat_extract_first.save_pretrained(a_					)[0]
            check_json_file_has_correct_format(a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		self.feature_extraction_class.from_pretrained(a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		feat_extract_first.to_dict()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		feat_extract_second.to_dict()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		dict_first.pop('mel_filters'					)
       SCREAMING_SNAKE_CASE__					:					Any      =		dict_second.pop('mel_filters'					)
       self.assertTrue(np.allclose(a_  ,			a_					)					)
       self.assertEqual(a_  ,			a_					)




  def 						__lowercase(   self  :						Tuple					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.feature_extraction_class(**self.feat_extract_dict					)

       with tempfile.TemporaryDirectory() as tmpdirname:
            SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(a_  ,			'feat_extract.json'					)
            feat_extract_first.to_json_file(a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.feature_extraction_class.from_json_file(a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		feat_extract_first.to_dict()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		feat_extract_second.to_dict()
       SCREAMING_SNAKE_CASE__					:					Dict      =		dict_first.pop('mel_filters'					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		dict_second.pop('mel_filters'					)
       self.assertTrue(np.allclose(a_  ,			a_					)					)
       self.assertEqual(a_  ,			a_					)




  def 						__lowercase(   self  :						int					)->							Union[str, Any]:




       """simple docstring"""
       # Initialize feature_extractor
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.feature_extraction_class(**self.feat_extract_dict					)

       # create three inputs of length 800, 1000, and 1200
       SCREAMING_SNAKE_CASE__					:					List[str]      =		[floats_list((1, x)					)[0] for x in range(800  ,			1400  ,			200					)]
       SCREAMING_SNAKE_CASE__					:					Dict      =		[np.asarray(a_					) for speech_input in speech_inputs]

       # Test not batched input
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		feature_extractor(np_speech_inputs[0]  ,			return_tensors='np'  ,			sampling_rate=4_4100					).audio_values

       self.assertTrue(encoded_audios.ndim == 4					)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

       # Test batched
       SCREAMING_SNAKE_CASE__					:					Dict      =		feature_extractor(a_  ,			return_tensors='np'  ,			sampling_rate=4_4100					).audio_values

       self.assertTrue(encoded_audios.ndim == 4					)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

       # Test audio masking
       SCREAMING_SNAKE_CASE__					:					Dict      =		feature_extractor(
           a_  ,			return_tensors='np'  ,			sampling_rate=4_4100  ,			mask_audio=a_					).audio_values

       self.assertTrue(encoded_audios.ndim == 4					)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

       # Test 2-D numpy arrays are batched.
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       SCREAMING_SNAKE_CASE__					:					int      =		np.asarray(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		feature_extractor(a_  ,			return_tensors='np'  ,			sampling_rate=4_4100					).audio_values
       self.assertTrue(encoded_audios.ndim == 4					)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		load_dataset('hf-internal-testing/librispeech_asr_dummy'  ,			'clean'  ,			split='validation'					)
       # automatic decoding with librispeech
       SCREAMING_SNAKE_CASE__					:					str      =		ds.sort('id'					).select(range(a_					)					)[:num_samples]['audio']

       return [x["array"] for x in speech_samples]




  def 						__lowercase(   self  :						Optional[int]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self._load_datasamples(1					)
       SCREAMING_SNAKE_CASE__					:					str      =		TvltFeatureExtractor()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		feature_extractor(a_  ,			return_tensors='pt'					).audio_values

       self.assertEquals(audio_values.shape  ,			(1, 1, 192, 128)					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]					)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]  ,			a_  ,			atol=1e-4					)					)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase											=    {
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FalconForCausalLM""",
     """FalconModel""",
     """FalconPreTrainedModel""",
     """FalconForSequenceClassification""",
     """FalconForTokenClassification""",
     """FalconForQuestionAnswering""",
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

 _lowercase											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
