




import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__	):

     @register_to_config
     def __init__(						self	,  _a = 128	,  _a = 256	,  _a = 2000.0	,  _a = 768	,  _a = 12	,  _a = 12	,  _a = 64	,  _a = 2048	,  _a = 0.1	,  )		->							Tuple:
           super().__init__()

           _A							:  Optional[int]    =		nn.Sequential(
               nn.Linear(_a	,  d_model * 4	,  bias=_a       )	,  nn.SiLU()	,  nn.Linear(d_model * 4	,  d_model * 4	,  bias=_a       )	,  nn.SiLU()	,  )

           _A							:  Tuple    =		nn.Embedding(_a	,  _a       )
           _A							:  str    =		False

           _A							:  Optional[int]    =		nn.Linear(_a	,  _a	,  bias=_a       )

           _A							:  Optional[Any]    =		nn.Dropout(p=_a       )

           _A							:  Dict    =		nn.ModuleList()
           for lyr_num in range(_a       ):
                 # FiLM conditional T5 decoder
                 _A							:  int    =		DecoderLayer(d_model=_a	,  d_kv=_a	,  num_heads=_a	,  d_ff=_a	,  dropout_rate=_a       )
                 self.decoders.append(_a       )

           _A							:  List[str]    =		TaLayerNorm(_a       )

           _A							:  Tuple    =		nn.Dropout(p=_a       )
           _A							:  str    =		nn.Linear(_a	,  _a	,  bias=_a       )

     def       a__			(						self	,  _a	,  _a       )		->							Tuple:
           _A							:  Optional[int]    =		torch.mul(query_input.unsqueeze(-1       )	,  key_input.unsqueeze(-2       )       )
           return mask.unsqueeze(-3       )



     def       a__			(						self	,  _a	,  _a	,  _a       )		->							Optional[Any]:
           _A    ,     _A    ,     _A							:  Any    =		decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

           # decoder_noise_time is in [0, 1), so rescale to expected timing range.
           _A							:  int    =		get_timestep_embedding(
               decoder_noise_time * self.config.max_decoder_noise_time	,  embedding_dim=self.config.d_model	,  max_period=self.config.max_decoder_noise_time	,  ).to(dtype=self.dtype       )

           _A							:  int    =		self.conditioning_emb(_a       ).unsqueeze(1       )

           assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

           _A							:  List[str]    =		decoder_input_tokens.shape[1]

           # If we want to use relative positions for audio context, we can just offset
           # this sequence by the length of encodings_and_masks.
           _A							:  int    =		torch.broadcast_to(
               torch.arange(_a	,  device=decoder_input_tokens.device       )	,  (batch, seq_length)	,  )

           _A							:  List[Any]    =		self.position_encoding(_a       )

           _A							:  Optional[int]    =		self.continuous_inputs_projection(_a       )
           inputs += position_encodings
           _A							:  Dict    =		self.dropout(_a       )

           # decoder: No padding present.
           _A							:  Union[str, Any]    =		torch.ones(
               decoder_input_tokens.shape[:2]	,  device=decoder_input_tokens.device	,  dtype=inputs.dtype       )

           # Translate encoding masks to encoder-decoder masks.
           _A							:  List[str]    =		[(x, self.encoder_decoder_mask(_a	,  _a       )) for x, y in encodings_and_masks]

           # cross attend style: concat encodings
           _A							:  Dict    =		torch.cat([x[0] for x in encodings_and_encdec_masks]	,  dim=1       )
           _A							:  List[Any]    =		torch.cat([x[1] for x in encodings_and_encdec_masks]	,  dim=-1       )

           for lyr in self.decoders:
                 _A							:  Tuple    =		lyr(
                     _a	,  conditioning_emb=_a	,  encoder_hidden_states=_a	,  encoder_attention_mask=_a	,  )[0]

           _A							:  str    =		self.decoder_norm(_a       )
           _A							:  Dict    =		self.post_dropout(_a       )

           _A							:  List[str]    =		self.spec_out(_a       )
           return spec_out







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a=1e-6       )		->							List[Any]:
           super().__init__()
           _A							:  Tuple    =		nn.ModuleList()

           # cond self attention: layer 0
           self.layer.append(
               TaLayerSelfAttentionCond(d_model=_a	,  d_kv=_a	,  num_heads=_a	,  dropout_rate=_a       )       )

           # cross attention: layer 1
           self.layer.append(
               TaLayerCrossAttention(
                   d_model=_a	,  d_kv=_a	,  num_heads=_a	,  dropout_rate=_a	,  layer_norm_epsilon=_a	,  )       )

           # Film Cond MLP + dropout: last layer
           self.layer.append(
               TaLayerFFCond(d_model=_a	,  d_ff=_a	,  dropout_rate=_a	,  layer_norm_epsilon=_a       )       )



     def       a__			(						self	,  _a	,  _a=None	,  _a=None	,  _a=None	,  _a=None	,  _a=None	,  )		->							Union[str, Any]:
           _A							:  Any    =		self.layer[0](
               _a	,  conditioning_emb=_a	,  attention_mask=_a	,  )

           if encoder_hidden_states is not None:
                 _A							:  Any    =		torch.where(encoder_attention_mask > 0	,  0	,  -1e10       ).to(
                     encoder_hidden_states.dtype       )

                 _A							:  Any    =		self.layer[1](
                     _a	,  key_value_states=_a	,  attention_mask=_a	,  )

           # Apply Film Conditional Feed Forward layer
           _A							:  Optional[int]    =		self.layer[-1](_a	,  _a       )

           return (hidden_states,)







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a       )		->							Tuple:
           super().__init__()
           _A							:  Any    =		TaLayerNorm(_a       )
           _A							:  Optional[int]    =		TaFiLMLayer(in_features=d_model * 4	,  out_features=_a       )
           _A							:  Union[str, Any]    =		Attention(query_dim=_a	,  heads=_a	,  dim_head=_a	,  out_bias=_a	,  scale_qk=_a       )
           _A							:  Any    =		nn.Dropout(_a       )



     def       a__			(						self	,  _a	,  _a=None	,  _a=None	,  )		->							Tuple:
           # pre_self_attention_layer_norm
           _A							:  List[str]    =		self.layer_norm(_a       )

           if conditioning_emb is not None:
                 _A							:  int    =		self.FiLMLayer(_a	,  _a       )

           # Self-attention block
           _A							:  List[str]    =		self.attention(_a       )

           _A							:  Optional[Any]    =		hidden_states + self.dropout(_a       )

           return hidden_states







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							List[str]:
           super().__init__()
           _A							:  Union[str, Any]    =		Attention(query_dim=_a	,  heads=_a	,  dim_head=_a	,  out_bias=_a	,  scale_qk=_a       )
           _A							:  Any    =		TaLayerNorm(_a	,  eps=_a       )
           _A							:  List[Any]    =		nn.Dropout(_a       )



     def       a__			(						self	,  _a	,  _a=None	,  _a=None	,  )		->							Tuple:
           _A							:  Optional[Any]    =		self.layer_norm(_a       )
           _A							:  str    =		self.attention(
               _a	,  encoder_hidden_states=_a	,  attention_mask=attention_mask.squeeze(1       )	,  )
           _A							:  Any    =		hidden_states + self.dropout(_a       )
           return layer_output







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a       )		->							Dict:
           super().__init__()
           _A							:  List[Any]    =		TaDenseGatedActDense(d_model=_a	,  d_ff=_a	,  dropout_rate=_a       )
           _A							:  str    =		TaFiLMLayer(in_features=d_model * 4	,  out_features=_a       )
           _A							:  List[Any]    =		TaLayerNorm(_a	,  eps=_a       )
           _A							:  Optional[Any]    =		nn.Dropout(_a       )



     def       a__			(						self	,  _a	,  _a=None       )		->							int:
           _A							:  Optional[int]    =		self.layer_norm(_a       )
           if conditioning_emb is not None:
                 _A							:  str    =		self.film(_a	,  _a       )

           _A							:  Tuple    =		self.DenseReluDense(_a       )
           _A							:  Tuple    =		hidden_states + self.dropout(_a       )
           return hidden_states







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a       )		->							str:
           super().__init__()
           _A							:  str    =		nn.Linear(_a	,  _a	,  bias=_a       )
           _A							:  Optional[int]    =		nn.Linear(_a	,  _a	,  bias=_a       )
           _A							:  int    =		nn.Linear(_a	,  _a	,  bias=_a       )
           _A							:  Dict    =		nn.Dropout(_a       )
           _A							:  Optional[Any]    =		NewGELUActivation()



     def       a__			(						self	,  _a       )		->							Union[str, Any]:
           _A							:  Union[str, Any]    =		self.act(self.wi_a(_a       )       )
           _A							:  Tuple    =		self.wi_a(_a       )
           _A							:  Optional[Any]    =		hidden_gelu * hidden_linear
           _A							:  Tuple    =		self.dropout(_a       )

           _A							:  str    =		self.wo(_a       )
           return hidden_states







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a=1e-6       )		->							Union[str, Any]:
           super().__init__()
           _A							:  Dict    =		nn.Parameter(torch.ones(_a       )       )
           _A							:  List[Any]    =		eps



     def       a__			(						self	,  _a       )		->							Tuple:
           # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
           # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
           # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
           # half-precision inputs is done in fp32

           _A							:  int    =		hidden_states.to(torch.floataa       ).pow(2       ).mean(-1	,  keepdim=_a       )
           _A							:  Any    =		hidden_states * torch.rsqrt(variance + self.variance_epsilon       )

           # convert into half-precision if necessary
           if self.weight.dtype in [torch.floataa, torch.bfloataa]:
                 _A							:  str    =		hidden_states.to(self.weight.dtype       )

           return self.weight * hidden_states







class 		lowercase							(     nn.Module	):

     def       a__			(						self	,  _a       )		->							torch.Tensor:
           return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.044715 * torch.pow(_a	,  3.0       ))       ))







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a       )		->							int:
           super().__init__()
           _A							:  Any    =		nn.Linear(_a	,  out_features * 2	,  bias=_a       )



     def       a__			(						self	,  _a	,  _a       )		->							int:
           _A							:  Dict    =		self.scale_bias(_a       )
           _A    ,     _A							:  Union[str, Any]    =		torch.chunk(_a	,  2	,  -1       )
           _A							:  Dict    =		x * (1 + scale) + shift
           return x







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

_snake_case            = random.Random()







def        lowerCAmelCase_			(  snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None  ):
      if rng is None:
            _A							:  str    =		global_rng

      _A							:  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 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=400	,  _a=2000	,  _a=2048	,  _a=128	,  _a=1	,  _a=512	,  _a=30	,  _a=4_4100	,  )		->							Tuple:
           _A							:  Any    =		parent
           _A							:  str    =		batch_size
           _A							:  Union[str, Any]    =		min_seq_length
           _A							:  int    =		max_seq_length
           _A							:  List[str]    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:  Tuple    =		spectrogram_length
           _A							:  int    =		feature_size
           _A							:  str    =		num_audio_channels
           _A							:  Tuple    =		hop_length
           _A							:  List[str]    =		chunk_length
           _A							:  Union[str, Any]    =		sampling_rate

     def       a__			(						self       )		->							Tuple:
           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       a__			(						self	,  _a=False	,  _a=False       )		->							Optional[int]:
           def _flatten(_a       ):
                 return list(itertools.chain(*_a       )       )

           if equal_length:
                 _A							:  List[str]    =		[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
           else:
                 # make sure that inputs increase in size
                 _A							:  Union[str, Any]    =		[
                     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							:  List[Any]    =		[np.asarray(_a       ) for x in speech_inputs]
           return speech_inputs







@require_torch
@require_torchaudio
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		TvltFeatureExtractor

     def       a__			(						self       )		->							Any:
           _A							:  int    =		TvltFeatureExtractionTester(self       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		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       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  Tuple    =		feat_extract_first.save_pretrained(_a       )[0]
                 check_json_file_has_correct_format(_a       )
                 _A							:  Optional[int]    =		self.feature_extraction_class.from_pretrained(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  int    =		feat_extract_second.to_dict()
           _A							:  int    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[int]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  List[str]    =		os.path.join(_a	,  """feat_extract.json"""       )
                 feat_extract_first.to_json_file(_a       )
                 _A							:  Union[str, Any]    =		self.feature_extraction_class.from_json_file(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  Union[str, Any]    =		feat_extract_second.to_dict()
           _A							:  List[Any]    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[Any]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           # Initialize feature_extractor
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )

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

           # Test not batched input
           _A							:  Optional[int]    =		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
           _A							:  str    =		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
           _A							:  Optional[Any]    =		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.
           _A							:  Dict    =		[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           _A							:  Optional[int]    =		np.asarray(_a       )
           _A							:  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       a__			(						self	,  _a       )		->							str:
           _A							:  Optional[Any]    =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,  """clean"""	,  split="""validation"""       )
           # automatic decoding with librispeech
           _A							:  Dict    =		ds.sort("""id"""       ).select(range(_a       )       )[:num_samples]["""audio"""]

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



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		self._load_datasamples(1       )
           _A							:  List[str]    =		TvltFeatureExtractor()
           _A							:  List[Any]    =		feature_extractor(_a	,  return_tensors="""pt"""       ).audio_values

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

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

