

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

__lowercase				      =						random.Random()







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1.0		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None   ):
      '''simple docstring'''







      if rng is None:
            A_							       =		global_rng

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

      return values

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    str			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Union[str, Any]=7			,						lowerCamelCase__	:    List[str]=4_0_0			,						lowerCamelCase__	:    Tuple=2_0_0_0			,						lowerCamelCase__	:    Optional[int]=2_0_4_8			,						lowerCamelCase__	:    Tuple=1_2_8			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    List[str]=5_1_2			,						lowerCamelCase__	:    List[str]=3_0			,						lowerCamelCase__	:    Dict=4_4_1_0_0			,						) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		min_seq_length
         A_							       =		max_seq_length
         A_							       =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         A_							       =		spectrogram_length
         A_							       =		feature_size
         A_							       =		num_audio_channels
         A_							       =		hop_length
         A_							       =		chunk_length
         A_							       =		sampling_rate

   def     UpperCamelCase					(				self	:    int       ) ->			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     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[int]=False			,						lowerCamelCase__	:    Optional[Any]=False       ) ->			Optional[Any]:
         """simple docstring"""





         def _flatten(lowerCamelCase__	:    Any       ):
               return list(itertools.chain(*lowerCamelCase__       )       )

         if equal_length:
               A_							       =		[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
         else:
               # make sure that inputs increase in size
               A_							       =		[
                   floats_list((x, self.feature_size)       )
                   for x in range(self.min_seq_length			,						self.max_seq_length			,						self.seq_length_diff       )
               ]
         if numpify:
               A_							       =		[np.asarray(lowerCamelCase__       ) for x in speech_inputs]
         return speech_inputs



@require_torch
@require_torchaudio
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    TvltFeatureExtractor

   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TvltFeatureExtractionTester(self       )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.feature_extraction_class(**self.feat_extract_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''spectrogram_length'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''feature_size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''num_audio_channels'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''hop_length'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''chunk_length'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''sampling_rate'''       )       )

   def     UpperCamelCase					(				self	:    str       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.feature_extraction_class(**self.feat_extract_dict       )

         with tempfile.TemporaryDirectory() as tmpdirname:
               A_							       =		feat_extract_first.save_pretrained(lowerCamelCase__       )[0]
               check_json_file_has_correct_format(lowerCamelCase__       )
               A_							       =		self.feature_extraction_class.from_pretrained(lowerCamelCase__       )

         A_							       =		feat_extract_first.to_dict()
         A_							       =		feat_extract_second.to_dict()
         A_							       =		dict_first.pop('''mel_filters'''       )
         A_							       =		dict_second.pop('''mel_filters'''       )
         self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__       )       )
         self.assertEqual(lowerCamelCase__			,						lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.feature_extraction_class(**self.feat_extract_dict       )

         with tempfile.TemporaryDirectory() as tmpdirname:
               A_							       =		os.path.join(lowerCamelCase__			,						'''feat_extract.json'''       )
               feat_extract_first.to_json_file(lowerCamelCase__       )
               A_							       =		self.feature_extraction_class.from_json_file(lowerCamelCase__       )

         A_							       =		feat_extract_first.to_dict()
         A_							       =		feat_extract_second.to_dict()
         A_							       =		dict_first.pop('''mel_filters'''       )
         A_							       =		dict_second.pop('''mel_filters'''       )
         self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__       )       )
         self.assertEqual(lowerCamelCase__			,						lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.feature_extraction_class(**self.feat_extract_dict       )

         # create three inputs of length 800, 1000, and 1200
         A_							       =		[floats_list((1, x)       )[0] for x in range(8_0_0			,						1_4_0_0			,						2_0_0       )]
         A_							       =		[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

         # Test not batched input
         A_							       =		feature_extractor(np_speech_inputs[0]			,						return_tensors='''np'''			,						sampling_rate=4_4_1_0_0       ).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
         A_							       =		feature_extractor(lowerCamelCase__			,						return_tensors='''np'''			,						sampling_rate=4_4_1_0_0       ).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
         A_							       =		feature_extractor(
             lowerCamelCase__			,						return_tensors='''np'''			,						sampling_rate=4_4_1_0_0			,						mask_audio=lowerCamelCase__       ).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.
         A_							       =		[floats_list((1, x)       )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         A_							       =		np.asarray(lowerCamelCase__       )
         A_							       =		feature_extractor(lowerCamelCase__			,						return_tensors='''np'''			,						sampling_rate=4_4_1_0_0       ).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     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    List[str]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,						'''clean'''			,						split='''validation'''       )
         # automatic decoding with librispeech
         A_							       =		ds.sort('''id'''       ).select(range(lowerCamelCase__       )       )[:num_samples]['''audio''']

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





   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         A_							       =		self._load_datasamples(1       )
         A_							       =		TvltFeatureExtractor()
         A_							       =		feature_extractor(lowerCamelCase__			,						return_tensors='''pt'''       ).audio_values

         self.assertEquals(audio_values.shape			,						(1, 1, 1_9_2, 1_2_8)       )

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








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    'table-transformer'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Union[str, Any]  					=    {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    Any=1_0_0			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[Any]=2_0_4_8			,						lowerCamelCase__	:    List[str]=8			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[str]=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=8			,						lowerCamelCase__	:    Union[str, Any]=0.0			,						lowerCamelCase__	:    Any=0.0			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]="relu"			,						lowerCamelCase__	:    Optional[int]=2_5_6			,						lowerCamelCase__	:    List[str]=0.1			,						lowerCamelCase__	:    str=0.0			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Dict=0.02			,						lowerCamelCase__	:    Union[str, Any]=1.0			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[int]="sine"			,						lowerCamelCase__	:    str="resnet50"			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    Any=1			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    List[str]=1			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Tuple=2			,						lowerCamelCase__	:    List[str]=0.1			,						**lowerCamelCase__	:    List[str]			,						) ->			Dict:
         """simple docstring"""





         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
                     A_							       =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
               elif isinstance(lowerCamelCase__			,						lowerCamelCase__       ):
                     A_							       =		backbone_config.get('''model_type'''       )
                     A_							       =		CONFIG_MAPPING[backbone_model_type]
                     A_							       =		config_class.from_dict(lowerCamelCase__       )
               # set timm attributes to None
               A_       ,A_       ,A_							       =		None, None, None

         A_							       =		use_timm_backbone
         A_							       =		backbone_config
         A_							       =		num_channels
         A_							       =		num_queries
         A_							       =		d_model
         A_							       =		encoder_ffn_dim
         A_							       =		encoder_layers
         A_							       =		encoder_attention_heads
         A_							       =		decoder_ffn_dim
         A_							       =		decoder_layers
         A_							       =		decoder_attention_heads
         A_							       =		dropout
         A_							       =		attention_dropout
         A_							       =		activation_dropout
         A_							       =		activation_function
         A_							       =		init_std
         A_							       =		init_xavier_std
         A_							       =		encoder_layerdrop
         A_							       =		decoder_layerdrop
         A_							       =		encoder_layers
         A_							       =		auxiliary_loss
         A_							       =		position_embedding_type
         A_							       =		backbone
         A_							       =		use_pretrained_backbone
         A_							       =		dilation
         # Hungarian matcher
         A_							       =		class_cost
         A_							       =		bbox_cost
         A_							       =		giou_cost
         # Loss coefficients
         A_							       =		mask_loss_coefficient
         A_							       =		dice_loss_coefficient
         A_							       =		bbox_loss_coefficient
         A_							       =		giou_loss_coefficient
         A_							       =		eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase__			,						**lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         return self.encoder_attention_heads





   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			int:
         """simple docstring"""





         return self.d_model



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    version.parse('1.11'			)

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-5





   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         return 1_2





