"""simple docstring"""





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   UpperCAmelCase_			(					_a				,							_a):


      @register_to_config
      def __init__(     self	,    a = 1_2_8	,    a = 2_5_6	,    a = 2_000.0	,    a = 7_6_8	,    a = 1_2	,    a = 1_2	,    a = 6_4	,    a = 2_0_4_8	,    a = 0.1	,    ) ->      Optional[int]:
             super().__init__()

             lowercase__    :	Any     					=			nn.Sequential(
                 nn.Linear(a	,    d_model * 4	,    bias=a							)	,    nn.SiLU()	,    nn.Linear(d_model * 4	,    d_model * 4	,    bias=a							)	,    nn.SiLU()	,    )

             lowercase__    :	Union[str, Any]     					=			nn.Embedding(a	,    a							)
             lowercase__    :	Any     					=			False

             lowercase__    :	List[str]     					=			nn.Linear(a	,    a	,    bias=a							)

             lowercase__    :	Dict     					=			nn.Dropout(p=a							)

             lowercase__    :	List[Any]     					=			nn.ModuleList()
             for lyr_num in range(a							):
                    # FiLM conditional T5 decoder
                    lowercase__    :	Tuple     					=			DecoderLayer(d_model=a	,    d_kv=a	,    num_heads=a	,    d_ff=a	,    dropout_rate=a							)
                    self.decoders.append(a							)

             lowercase__    :	str     					=			TaLayerNorm(a							)

             lowercase__    :	List[Any]     					=			nn.Dropout(p=a							)
             lowercase__    :	List[str]     					=			nn.Linear(a	,    a	,    bias=a							)


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      int:
             lowercase__    :	Optional[int]     					=			torch.mul(query_input.unsqueeze(-1							)	,    key_input.unsqueeze(-2							)							)
             return mask.unsqueeze(-3							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[Any]:
             lowercase__					,   lowercase__					,   lowercase__    :	List[str]     					=			decoder_input_tokens.shape
             assert decoder_noise_time.shape == (batch,)

             # decoder_noise_time is in [0, 1), so rescale to expected timing range.
             lowercase__    :	Union[str, Any]     					=			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							)

             lowercase__    :	Tuple     					=			self.conditioning_emb(a							).unsqueeze(1							)

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

             lowercase__    :	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.
             lowercase__    :	int     					=			torch.broadcast_to(
                 torch.arange(a	,    device=decoder_input_tokens.device							)	,    (batch, seq_length)	,    )

             lowercase__    :	Union[str, Any]     					=			self.position_encoding(a							)

             lowercase__    :	Union[str, Any]     					=			self.continuous_inputs_projection(a							)
             inputs += position_encodings
             lowercase__    :	Tuple     					=			self.dropout(a							)

             # decoder: No padding present.
             lowercase__    :	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.
             lowercase__    :	Any     					=			[(x, self.encoder_decoder_mask(a	,    a							)) for x, y in encodings_and_masks]

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

             for lyr in self.decoders:
                    lowercase__    :	List[str]     					=			lyr(
                        a	,    conditioning_emb=a	,    encoder_hidden_states=a	,    encoder_attention_mask=a	,    )[0]

             lowercase__    :	Any     					=			self.decoder_norm(a							)
             lowercase__    :	Dict     					=			self.post_dropout(a							)

             lowercase__    :	Tuple     					=			self.spec_out(a							)
             return spec_out





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a	,    a	,    a	,    a	,    a=1e-6							) ->      Dict:
             super().__init__()
             lowercase__    :	List[Any]     					=			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 	_UpperCAmelCase (     self	,    a	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    ) ->      List[Any]:
             lowercase__    :	List[Any]     					=			self.layer[0](
                 a	,    conditioning_emb=a	,    attention_mask=a	,    )

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

                    lowercase__    :	List[str]     					=			self.layer[1](
                        a	,    key_value_states=a	,    attention_mask=a	,    )

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

             return (hidden_states,)





class   UpperCAmelCase_			(					nn.Module):


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


      def 	_UpperCAmelCase (     self	,    a	,    a=None	,    a=None	,    ) ->      List[Any]:
             # pre_self_attention_layer_norm
             lowercase__    :	Optional[Any]     					=			self.layer_norm(a							)

             if conditioning_emb is not None:
                    lowercase__    :	List[Any]     					=			self.FiLMLayer(a	,    a							)

             # Self-attention block
             lowercase__    :	Dict     					=			self.attention(a							)

             lowercase__    :	str     					=			hidden_states + self.dropout(a							)

             return hidden_states





class   UpperCAmelCase_			(					nn.Module):


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


      def 	_UpperCAmelCase (     self	,    a	,    a=None	,    a=None	,    ) ->      str:
             lowercase__    :	Dict     					=			self.layer_norm(a							)
             lowercase__    :	int     					=			self.attention(
                 a	,    encoder_hidden_states=a	,    attention_mask=attention_mask.squeeze(1							)	,    )
             lowercase__    :	Optional[int]     					=			hidden_states + self.dropout(a							)
             return layer_output





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a	,    a	,    a							) ->      Tuple:
             super().__init__()
             lowercase__    :	Optional[Any]     					=			TaDenseGatedActDense(d_model=a	,    d_ff=a	,    dropout_rate=a							)
             lowercase__    :	Optional[int]     					=			TaFiLMLayer(in_features=d_model * 4	,    out_features=a							)
             lowercase__    :	int     					=			TaLayerNorm(a	,    eps=a							)
             lowercase__    :	Dict     					=			nn.Dropout(a							)


      def 	_UpperCAmelCase (     self	,    a	,    a=None							) ->      Optional[int]:
             lowercase__    :	Any     					=			self.layer_norm(a							)
             if conditioning_emb is not None:
                    lowercase__    :	Dict     					=			self.film(a	,    a							)

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





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a	,    a							) ->      List[str]:
             super().__init__()
             lowercase__    :	Optional[int]     					=			nn.Linear(a	,    a	,    bias=a							)
             lowercase__    :	str     					=			nn.Linear(a	,    a	,    bias=a							)
             lowercase__    :	List[Any]     					=			nn.Linear(a	,    a	,    bias=a							)
             lowercase__    :	Optional[Any]     					=			nn.Dropout(a							)
             lowercase__    :	Optional[int]     					=			NewGELUActivation()


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             lowercase__    :	int     					=			self.act(self.wi_a(a							)							)
             lowercase__    :	str     					=			self.wi_a(a							)
             lowercase__    :	Union[str, Any]     					=			hidden_gelu * hidden_linear
             lowercase__    :	Dict     					=			self.dropout(a							)

             lowercase__    :	Optional[int]     					=			self.wo(a							)
             return hidden_states





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a=1e-6							) ->      Tuple:
             super().__init__()
             lowercase__    :	Optional[Any]     					=			nn.Parameter(torch.ones(a							)							)
             lowercase__    :	Union[str, Any]     					=			eps


      def 	_UpperCAmelCase (     self	,    a							) ->      int:
             # 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

             lowercase__    :	Dict     					=			hidden_states.to(torch.floataa							).pow(2							).mean(-1	,    keepdim=a							)
             lowercase__    :	Optional[Any]     					=			hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

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

             return self.weight * hidden_states





class   UpperCAmelCase_			(					nn.Module):


      def 	_UpperCAmelCase (     self	,    a							) ->      torch.Tensor:
             return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044_715 * torch.pow(a	,    3.0							))							))





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a							) ->      Union[str, Any]:
             super().__init__()
             lowercase__    :	Optional[Any]     					=			nn.Linear(a	,    out_features * 2	,    bias=a							)


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      str:
             lowercase__    :	Tuple     					=			self.scale_bias(a							)
             lowercase__					,   lowercase__    :	Any     					=			torch.chunk(a	,    2	,    -1							)
             lowercase__    :	Dict     					=			x * (1 + scale) + shift
             return x


"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

