




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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  _UpperCamelCase   ):



	"""simple docstring"""


	@register_to_config
	def __init__(					self		:						Optional[int]					,     lowerCAmelCase		:						int = 128					,     lowerCAmelCase		:						int = 256					,     lowerCAmelCase		:						float = 20_00.0					,     lowerCAmelCase		:						int = 768					,     lowerCAmelCase		:						int = 12					,     lowerCAmelCase		:						int = 12					,     lowerCAmelCase		:						int = 64					,     lowerCAmelCase		:						int = 2048					,     lowerCAmelCase		:						float = 0.1					,     )	->       int:







						"""simple docstring"""







						super().__init__()

						__UpperCamelCase					:	List[str]             =      nn.Sequential(
						    nn.Linear(lowerCAmelCase					,     d_model * 4					,     bias=lowerCAmelCase	)					,     nn.SiLU()					,     nn.Linear(d_model * 4					,     d_model * 4					,     bias=lowerCAmelCase	)					,     nn.SiLU()					,     )

						__UpperCamelCase					:	Any             =      nn.Embedding(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      False

						__UpperCamelCase					:	Tuple             =      nn.Linear(lowerCAmelCase					,     lowerCAmelCase					,     bias=lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      nn.Dropout(p=lowerCAmelCase	)

						__UpperCamelCase					:	int             =      nn.ModuleList()
						for lyr_num in range(lowerCAmelCase	):
											# FiLM conditional T5 decoder
											__UpperCamelCase					:	Optional[Any]             =      DecoderLayer(d_model=lowerCAmelCase					,     d_kv=lowerCAmelCase					,     num_heads=lowerCAmelCase					,     d_ff=lowerCAmelCase					,     dropout_rate=lowerCAmelCase	)
											self.decoders.append(lowerCAmelCase	)

						__UpperCamelCase					:	Optional[Any]             =      TaLayerNorm(lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      nn.Dropout(p=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      nn.Linear(lowerCAmelCase					,     lowerCAmelCase					,     bias=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						str	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      torch.mul(query_input.unsqueeze(-1	)					,     key_input.unsqueeze(-2	)	)
						return mask.unsqueeze(-3	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						Any	)	->       int:







						"""simple docstring"""







						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	int             =      decoder_input_tokens.shape
						assert decoder_noise_time.shape == (batch,)

						# decoder_noise_time is in [0, 1), so rescale to expected timing range.
						__UpperCamelCase					:	str             =      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	)

						__UpperCamelCase					:	Dict             =      self.conditioning_emb(lowerCAmelCase	).unsqueeze(1	)

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

						__UpperCamelCase					:	Union[str, Any]             =      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.
						__UpperCamelCase					:	Optional[int]             =      torch.broadcast_to(
						    torch.arange(lowerCAmelCase					,     device=decoder_input_tokens.device	)					,     (batch, seq_length)					,     )

						__UpperCamelCase					:	Any             =      self.position_encoding(lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      self.continuous_inputs_projection(lowerCAmelCase	)
						inputs += position_encodings
						__UpperCamelCase					:	List[str]             =      self.dropout(lowerCAmelCase	)

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

						# Translate encoding masks to encoder-decoder masks.
						__UpperCamelCase					:	Tuple             =      [(x, self.encoder_decoder_mask(lowerCAmelCase					,     lowerCAmelCase	)) for x, y in encodings_and_masks]

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

						for lyr in self.decoders:
											__UpperCamelCase					:	int             =      lyr(
											    lowerCAmelCase					,     conditioning_emb=lowerCAmelCase					,     encoder_hidden_states=lowerCAmelCase					,     encoder_attention_mask=lowerCAmelCase					,     )[0]

						__UpperCamelCase					:	List[Any]             =      self.decoder_norm(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      self.post_dropout(lowerCAmelCase	)

						__UpperCamelCase					:	List[Any]             =      self.spec_out(lowerCAmelCase	)
						return spec_out






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						str					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Any=1E-6	)	->       Any:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Union[str, Any]             =      nn.ModuleList()

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

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

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






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						Tuple=None					,     lowerCAmelCase		:						Optional[Any]=None					,     lowerCAmelCase		:						Optional[Any]=None					,     lowerCAmelCase		:						Optional[Any]=None					,     lowerCAmelCase		:						Any=None					,     )	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self.layer[0](
						    lowerCAmelCase					,     conditioning_emb=lowerCAmelCase					,     attention_mask=lowerCAmelCase					,     )

						if encoder_hidden_states is not None:
											__UpperCamelCase					:	int             =      torch.where(encoder_attention_mask > 0					,     0					,     -1E10	).to(
											    encoder_hidden_states.dtype	)

											__UpperCamelCase					:	List[Any]             =      self.layer[1](
											    lowerCAmelCase					,     key_value_states=lowerCAmelCase					,     attention_mask=lowerCAmelCase					,     )

						# Apply Film Conditional Feed Forward layer
						__UpperCamelCase					:	Union[str, Any]             =      self.layer[-1](lowerCAmelCase					,     lowerCAmelCase	)

						return (hidden_states,)






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						List[str]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]	)	->       int:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Dict             =      TaLayerNorm(lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      TaFiLMLayer(in_features=d_model * 4					,     out_features=lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      Attention(query_dim=lowerCAmelCase					,     heads=lowerCAmelCase					,     dim_head=lowerCAmelCase					,     out_bias=lowerCAmelCase					,     scale_qk=lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      nn.Dropout(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[Any]=None					,     lowerCAmelCase		:						Optional[Any]=None					,     )	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      self.layer_norm(lowerCAmelCase	)

						if conditioning_emb is not None:
											__UpperCamelCase					:	Any             =      self.FiLMLayer(lowerCAmelCase					,     lowerCAmelCase	)

						# Self-attention block
						__UpperCamelCase					:	Optional[int]             =      self.attention(lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      hidden_states + self.dropout(lowerCAmelCase	)

						return hidden_states






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Dict	)	->       Optional[Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	List[str]             =      Attention(query_dim=lowerCAmelCase					,     heads=lowerCAmelCase					,     dim_head=lowerCAmelCase					,     out_bias=lowerCAmelCase					,     scale_qk=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      TaLayerNorm(lowerCAmelCase					,     eps=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      nn.Dropout(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						Any					,     lowerCAmelCase		:						List[str]=None					,     lowerCAmelCase		:						List[str]=None					,     )	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      self.layer_norm(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[Any]             =      self.attention(
						    lowerCAmelCase					,     encoder_hidden_states=lowerCAmelCase					,     attention_mask=attention_mask.squeeze(1	)					,     )
						__UpperCamelCase					:	List[str]             =      hidden_states + self.dropout(lowerCAmelCase	)
						return layer_output






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Any					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Any					,     lowerCAmelCase		:						Any					,     lowerCAmelCase		:						Tuple	)	->       Dict:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Union[str, Any]             =      TaDenseGatedActDense(d_model=lowerCAmelCase					,     d_ff=lowerCAmelCase					,     dropout_rate=lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      TaFiLMLayer(in_features=d_model * 4					,     out_features=lowerCAmelCase	)
						__UpperCamelCase					:	str             =      TaLayerNorm(lowerCAmelCase					,     eps=lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      nn.Dropout(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						List[Any]=None	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.layer_norm(lowerCAmelCase	)
						if conditioning_emb is not None:
											__UpperCamelCase					:	Dict             =      self.film(lowerCAmelCase					,     lowerCAmelCase	)

						__UpperCamelCase					:	List[Any]             =      self.DenseReluDense(lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      hidden_states + self.dropout(lowerCAmelCase	)
						return hidden_states






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Dict					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Dict             =      nn.Linear(lowerCAmelCase					,     lowerCAmelCase					,     bias=lowerCAmelCase	)
						__UpperCamelCase					:	int             =      nn.Linear(lowerCAmelCase					,     lowerCAmelCase					,     bias=lowerCAmelCase	)
						__UpperCamelCase					:	int             =      nn.Linear(lowerCAmelCase					,     lowerCAmelCase					,     bias=lowerCAmelCase	)
						__UpperCamelCase					:	Optional[Any]             =      nn.Dropout(lowerCAmelCase	)
						__UpperCamelCase					:	str             =      NewGELUActivation()






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						Tuple	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.act(self.wi_a(lowerCAmelCase	)	)
						__UpperCamelCase					:	Union[str, Any]             =      self.wi_a(lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      hidden_gelu * hidden_linear
						__UpperCamelCase					:	int             =      self.dropout(lowerCAmelCase	)

						__UpperCamelCase					:	List[Any]             =      self.wo(lowerCAmelCase	)
						return hidden_states






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Dict					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple=1E-6	)	->       Dict:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      nn.Parameter(torch.ones(lowerCAmelCase	)	)
						__UpperCamelCase					:	Any             =      eps






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Union[str, Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      hidden_states.to(torch.floataa	).pow(2	).mean(-1					,     keepdim=lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      hidden_states * torch.rsqrt(variance + self.variance_epsilon	)

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

						return self.weight * hidden_states






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						torch.Tensor	)	->       torch.Tensor:







						"""simple docstring"""







						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase					,     3.0	))	))






class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Any					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						int	)	->       Dict:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	int             =      nn.Linear(lowerCAmelCase					,     out_features * 2					,     bias=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.scale_bias(lowerCAmelCase	)
						__UpperCamelCase      ,			__UpperCamelCase					:	Union[str, Any]             =      torch.chunk(lowerCAmelCase					,     2					,     -1	)
						__UpperCamelCase					:	Tuple             =      x * (1 + scale) + shift
						return x












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






