"""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    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase				):



      @register_to_config
      def __init__(						self    ,			lowercase = 1_28    ,			lowercase = 2_56    ,			lowercase = 2000.0    ,			lowercase = 7_68    ,			lowercase = 12    ,			lowercase = 12    ,			lowercase = 64    ,			lowercase = 20_48    ,			lowercase = 0.1    ,			)	->			int:





        '''simple docstring'''





        super().__init__()

        a__:     Dict										=	nn.Sequential(
            nn.Linear(lowercase    ,			d_model * 4    ,			bias=lowercase)    ,			nn.SiLU()    ,			nn.Linear(d_model * 4    ,			d_model * 4    ,			bias=lowercase)    ,			nn.SiLU()    ,			)

        a__:     Any										=	nn.Embedding(lowercase    ,			lowercase)
        a__:     Dict										=	False

        a__:     Optional[int]										=	nn.Linear(lowercase    ,			lowercase    ,			bias=lowercase)

        a__:     Tuple										=	nn.Dropout(p=lowercase)

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

        a__:     List[Any]										=	TaLayerNorm(lowercase)

        a__:     Optional[int]										=	nn.Dropout(p=lowercase)
        a__:     str										=	nn.Linear(lowercase    ,			lowercase    ,			bias=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     List[str]										=	torch.mul(query_input.unsqueeze(-1)    ,			key_input.unsqueeze(-2))
        return mask.unsqueeze(-3)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





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

        # decoder_noise_time is in [0, 1), so rescale to expected timing range.
        a__:     Tuple										=	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__:     Any										=	self.conditioning_emb(lowercase).unsqueeze(1)

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

        a__:     List[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.
        a__:     List[str]										=	torch.broadcast_to(
            torch.arange(lowercase    ,			device=decoder_input_tokens.device)    ,			(batch, seq_length)    ,			)

        a__:     Union[str, Any]										=	self.position_encoding(lowercase)

        a__:     Tuple										=	self.continuous_inputs_projection(lowercase)
        inputs += position_encodings
        a__:     Tuple										=	self.dropout(lowercase)

        # decoder: No padding present.
        a__:     Optional[int]										=	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(lowercase    ,			lowercase)) for x, y in encodings_and_masks]

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

        for lyr in self.decoders:
          a__:     str										=	lyr(
              lowercase    ,			conditioning_emb=lowercase    ,			encoder_hidden_states=lowercase    ,			encoder_attention_mask=lowercase    ,			)[0]

        a__:     Dict										=	self.decoder_norm(lowercase)
        a__:     Dict										=	self.post_dropout(lowercase)

        a__:     Union[str, Any]										=	self.spec_out(lowercase)
        return spec_out







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=1e-6)	->			Union[str, Any]:





        '''simple docstring'''





        super().__init__()
        a__:     Optional[int]										=	nn.ModuleList()

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

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

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			)	->			List[Any]:





        '''simple docstring'''





        a__:     List[Any]										=	self.layer[0](
            lowercase    ,			conditioning_emb=lowercase    ,			attention_mask=lowercase    ,			)

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

          a__:     Tuple										=	self.layer[1](
              lowercase    ,			key_value_states=lowercase    ,			attention_mask=lowercase    ,			)

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

        return (hidden_states,)







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.layer_norm(lowercase)

        if conditioning_emb is not None:
          a__:     int										=	self.FiLMLayer(lowercase    ,			lowercase)

        # Self-attention block
        a__:     str										=	self.attention(lowercase)

        a__:     Tuple										=	hidden_states + self.dropout(lowercase)

        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        super().__init__()
        a__:     Any										=	Attention(query_dim=lowercase    ,			heads=lowercase    ,			dim_head=lowercase    ,			out_bias=lowercase    ,			scale_qk=lowercase)
        a__:     Union[str, Any]										=	TaLayerNorm(lowercase    ,			eps=lowercase)
        a__:     Dict										=	nn.Dropout(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.layer_norm(lowercase)
        a__:     str										=	self.attention(
            lowercase    ,			encoder_hidden_states=lowercase    ,			attention_mask=attention_mask.squeeze(1)    ,			)
        a__:     Tuple										=	hidden_states + self.dropout(lowercase)
        return layer_output







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None)	->			Dict:





        '''simple docstring'''





        a__:     int										=	self.layer_norm(lowercase)
        if conditioning_emb is not None:
          a__:     Union[str, Any]										=	self.film(lowercase    ,			lowercase)

        a__:     Dict										=	self.DenseReluDense(lowercase)
        a__:     Dict										=	hidden_states + self.dropout(lowercase)
        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        super().__init__()
        a__:     str										=	nn.Linear(lowercase    ,			lowercase    ,			bias=lowercase)
        a__:     Tuple										=	nn.Linear(lowercase    ,			lowercase    ,			bias=lowercase)
        a__:     str										=	nn.Linear(lowercase    ,			lowercase    ,			bias=lowercase)
        a__:     int										=	nn.Dropout(lowercase)
        a__:     int										=	NewGELUActivation()



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	self.act(self.wi_a(lowercase))
        a__:     List[Any]										=	self.wi_a(lowercase)
        a__:     Dict										=	hidden_gelu * hidden_linear
        a__:     Optional[Any]										=	self.dropout(lowercase)

        a__:     List[str]										=	self.wo(lowercase)
        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase=1e-6)	->			str:





        '''simple docstring'''





        super().__init__()
        a__:     Any										=	nn.Parameter(torch.ones(lowercase))
        a__:     Any										=	eps



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	hidden_states.to(torch.floataa).pow(2).mean(-1    ,			keepdim=lowercase)
        a__:     Union[str, 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__:     Union[str, Any]										=	hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states







class    __snake_case       (     nn.Module				):



      def   lowerCamelCase_   (						self    ,			lowercase)	->			torch.Tensor:





        '''simple docstring'''





        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(lowercase    ,			3.0))))







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        super().__init__()
        a__:     List[str]										=	nn.Linear(lowercase    ,			out_features * 2    ,			bias=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





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



"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


