from typing import TYPE_CHECKING

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


lowerCAmelCase__     :   Dict		=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__     :   Tuple		=   ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__     :   List[Any]		=   [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__     :   Dict		=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






import flax.linen as nn
import jax
import jax.numpy as jnp
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             a_					:		Dict							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Optional[int]:
             a_							,		a_							,		a_							,		a_					:		Union[str, Any]							=  hidden_states.shape
             a_					:		List[str]							=  jax.image.resize(
                 SCREAMING_SNAKE_CASE__     ,							shape=(batch, height * 2, width * 2, channels)     ,							method='nearest'     ,							)
             a_					:		Any							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Optional[int]							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(2, 2)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_					:		str							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     int        =						None
      snake_case__				:     float        =						0.0
      snake_case__				:     bool        =						None
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[str]							=  self.in_channels if self.out_channels is None else self.out_channels

             a_					:		Optional[int]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		Any							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		Optional[int]							=  nn.Dense(SCREAMING_SNAKE_CASE__     ,							dtype=self.dtype     )

             a_					:		Union[str, Any]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		int							=  nn.Dropout(self.dropout_prob     )
             a_					:		Optional[Any]							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		List[str]							=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_					:		List[Any]							=  None
             if use_nin_shortcut:
                    a_					:		Union[str, Any]							=  nn.Conv(
                        SCREAMING_SNAKE_CASE__     ,							kernel_size=(1, 1)     ,							strides=(1, 1)     ,							padding='VALID'     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     )		->			int:
             a_					:		List[Any]							=  hidden_states
             a_					:		Any							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[str]							=  jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__     ,							1     )     ,							1     )
             a_					:		Optional[int]							=  hidden_states + temb

             a_					:		List[str]							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  self.dropout(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             if self.conv_shortcut is not None:
                    a_					:		List[str]							=  self.conv_shortcut(SCREAMING_SNAKE_CASE__     )

             return hidden_states + residual


