

'''simple docstring'''






import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      lowercase_      (lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Tuple							     =     GPTaTokenizer
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     GPTaTokenizerFast
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     True
  SCREAMING_SNAKE_CASE       :    str							     =     {'add_prefix_space': True}
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     False






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase				    =					[
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''\u0120''',
       '''\u0120l''',
       '''\u0120n''',
       '''\u0120lo''',
       '''\u0120low''',
       '''er''',
       '''\u0120lowest''',
       '''\u0120newer''',
       '''\u0120wider''',
       '''<unk>''',
       '''<|endoftext|>''',
   ]
   __lowercase				    =					dict(zip(lowercase__   ,range(len(lowercase__     )     )     )     )
   __lowercase				    =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
   __lowercase				    =					{'''unk_token''': '''<unk>'''}

   __lowercase				    =					os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file''']     )
   __lowercase				    =					os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''merges_file''']     )
   with open(self.vocab_file   ,'''w'''   ,encoding='''utf-8'''     ) as fp:
    fp.write(json.dumps(lowercase__     ) + '''\n'''     )
   with open(self.merges_file   ,'''w'''   ,encoding='''utf-8'''     ) as fp:
    fp.write('''\n'''.join(lowercase__     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,**lowercase__  :	Optional[Any]     ):
   kwargs.update(self.special_tokens_map     )
   return GPTaTokenizer.from_pretrained(self.tmpdirname   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,**lowercase__  :	str     ):
   kwargs.update(self.special_tokens_map     )
   return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	str     ):
   __lowercase				    =					'''lower newer'''
   __lowercase				    =					'''lower newer'''
   return input_text, output_text






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					GPTaTokenizer(self.vocab_file   ,self.merges_file   ,**self.special_tokens_map     )
   __lowercase				    =					'''lower newer'''
   __lowercase				    =					['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
   __lowercase				    =					tokenizer.tokenize(lowercase__   ,add_prefix_space=lowercase__     )
   self.assertListEqual(lowercase__   ,lowercase__     )

   __lowercase				    =					tokens + [tokenizer.unk_token]
   __lowercase				    =					[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__     )   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   if not self.test_rust_tokenizer:
    return

   __lowercase				    =					self.get_tokenizer()
   __lowercase				    =					self.get_rust_tokenizer(add_prefix_space=lowercase__     )

   __lowercase				    =					'''lower newer'''

   # Testing tokenization
   __lowercase				    =					tokenizer.tokenize(lowercase__   ,add_prefix_space=lowercase__     )
   __lowercase				    =					rust_tokenizer.tokenize(lowercase__     )
   self.assertListEqual(lowercase__   ,lowercase__     )

   # Testing conversion to ids without special tokens
   __lowercase				    =					tokenizer.encode(lowercase__   ,add_special_tokens=lowercase__   ,add_prefix_space=lowercase__     )
   __lowercase				    =					rust_tokenizer.encode(lowercase__   ,add_special_tokens=lowercase__     )
   self.assertListEqual(lowercase__   ,lowercase__     )

   # Testing conversion to ids with special tokens
   __lowercase				    =					self.get_rust_tokenizer(add_prefix_space=lowercase__     )
   __lowercase				    =					tokenizer.encode(lowercase__   ,add_prefix_space=lowercase__     )
   __lowercase				    =					rust_tokenizer.encode(lowercase__     )
   self.assertListEqual(lowercase__   ,lowercase__     )

   # Testing the unknown token
   __lowercase				    =					tokens + [rust_tokenizer.unk_token]
   __lowercase				    =					[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase__     )   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,*lowercase__  :	Optional[int]   ,**lowercase__  :	int     ):
   # It's very difficult to mix/test pretokenization with byte-level
   # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Dict=1_5     ):
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"     ):
     __lowercase				    =					self.rust_tokenizer_class.from_pretrained(lowercase__   ,**lowercase__     )

     # Simple input
     __lowercase				    =					'''This is a simple input'''
     __lowercase				    =					['''This is a simple input 1''', '''This is a simple input 2''']
     __lowercase				    =					('''This is a simple input''', '''This is a pair''')
     __lowercase				    =					[
         ('''This is a simple input 1''', '''This is a simple input 2'''),
         ('''This is a simple pair 1''', '''This is a simple pair 2'''),
     ]

     # Simple input tests
     self.assertRaises(lowercase__   ,tokenizer_r.encode   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''     )

     # Simple input
     self.assertRaises(lowercase__   ,tokenizer_r.encode_plus   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''     )

     # Simple input
     self.assertRaises(
         lowercase__   ,tokenizer_r.batch_encode_plus   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''   ,)

     # Pair input
     self.assertRaises(lowercase__   ,tokenizer_r.encode   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''     )

     # Pair input
     self.assertRaises(lowercase__   ,tokenizer_r.encode_plus   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''     )

     # Pair input
     self.assertRaises(
         lowercase__   ,tokenizer_r.batch_encode_plus   ,lowercase__   ,max_length=lowercase__   ,padding='''max_length'''   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					GPTaTokenizer.from_pretrained(self.tmpdirname   ,pad_token='''<pad>'''     )

   # Simple input
   __lowercase				    =					'''This is a simple input'''
   __lowercase				    =					['''This is a simple input looooooooong''', '''This is a simple input''']
   __lowercase				    =					('''This is a simple input''', '''This is a pair''')
   __lowercase				    =					[
       ('''This is a simple input loooooong''', '''This is a simple input'''),
       ('''This is a simple pair loooooong''', '''This is a simple pair'''),
   ]

   __lowercase				    =					tokenizer.pad_token_id

   __lowercase				    =					tokenizer(lowercase__   ,padding='''max_length'''   ,max_length=3_0   ,return_tensors='''np'''     )
   __lowercase				    =					tokenizer(lowercase__   ,padding=lowercase__   ,truncate=lowercase__   ,return_tensors='''np'''     )
   __lowercase				    =					tokenizer(*lowercase__   ,padding='''max_length'''   ,max_length=6_0   ,return_tensors='''np'''     )
   __lowercase				    =					tokenizer(lowercase__   ,padding=lowercase__   ,truncate=lowercase__   ,return_tensors='''np'''     )

   # s
   # test single string max_length padding
   self.assertEqual(out_s['''input_ids'''].shape[-1]   ,3_0     )
   self.assertTrue(pad_token_id in out_s['''input_ids''']     )
   self.assertTrue(0 in out_s['''attention_mask''']     )

   # s2
   # test automatic padding
   self.assertEqual(out_sa['''input_ids'''].shape[-1]   ,3_3     )
   # long slice doesn't have padding
   self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]     )
   self.assertFalse(0 in out_sa['''attention_mask'''][0]     )
   # short slice does have padding
   self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]     )
   self.assertTrue(0 in out_sa['''attention_mask'''][1]     )

   # p
   # test single pair max_length padding
   self.assertEqual(out_p['''input_ids'''].shape[-1]   ,6_0     )
   self.assertTrue(pad_token_id in out_p['''input_ids''']     )
   self.assertTrue(0 in out_p['''attention_mask''']     )

   # p2
   # test automatic padding pair
   self.assertEqual(out_pa['''input_ids'''].shape[-1]   ,5_2     )
   # long slice pair doesn't have padding
   self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]     )
   self.assertFalse(0 in out_pa['''attention_mask'''][0]     )
   # short slice pair does have padding
   self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]     )
   self.assertTrue(0 in out_pa['''attention_mask'''][1]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					'''$$$'''
   __lowercase				    =					GPTaTokenizer.from_pretrained(self.tmpdirname   ,bos_token=lowercase__   ,add_bos_token=lowercase__     )

   __lowercase				    =					'''This is a simple input'''
   __lowercase				    =					['''This is a simple input 1''', '''This is a simple input 2''']

   __lowercase				    =					tokenizer.bos_token_id

   __lowercase				    =					tokenizer(lowercase__     )
   __lowercase				    =					tokenizer(lowercase__     )

   self.assertEqual(out_s.input_ids[0]   ,lowercase__     )
   self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

   __lowercase				    =					tokenizer.decode(out_s.input_ids     )
   __lowercase				    =					tokenizer.batch_decode(out_sa.input_ids     )

   self.assertEqual(decode_s.split()[0]   ,lowercase__     )
   self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   # TODO: change to self.get_tokenizers() when the fast version is implemented
   __lowercase				    =					[self.get_tokenizer(do_lower_case=lowercase__   ,add_bos_token=lowercase__     )]
   for tokenizer in tokenizers:
    with self.subTest(F"{tokenizer.__class__.__name__}"     ):
     __lowercase				    =					'''Encode this.'''
     __lowercase				    =					'''This one too please.'''
     __lowercase				    =					tokenizer.encode(lowercase__   ,add_special_tokens=lowercase__     )
     encoded_sequence += tokenizer.encode(lowercase__   ,add_special_tokens=lowercase__     )
     __lowercase				    =					tokenizer.encode_plus(
         lowercase__   ,lowercase__   ,add_special_tokens=lowercase__   ,return_special_tokens_mask=lowercase__   ,)
     __lowercase				    =					encoded_sequence_dict['''input_ids''']
     __lowercase				    =					encoded_sequence_dict['''special_tokens_mask''']
     self.assertEqual(len(lowercase__     )   ,len(lowercase__     )     )

     __lowercase				    =					[
         (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowercase__     )
     ]
     __lowercase				    =					[x for x in filtered_sequence if x is not None]
     self.assertEqual(lowercase__   ,lowercase__     )






@require_tokenizers
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   # More context:
   # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
   # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
   # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

   __lowercase				    =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,from_slow=lowercase__     )
   __lowercase				    =					'''A photo of a cat'''

   __lowercase				    =					tokenizer.encode(
       lowercase__   ,)
   self.assertEqual(lowercase__   ,[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]     )
   tokenizer.save_pretrained('''test_opt'''     )

   __lowercase				    =					AutoTokenizer.from_pretrained('''./test_opt'''     )
   __lowercase				    =					tokenizer.encode(
       lowercase__   ,)
   self.assertEqual(lowercase__   ,[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,use_slow=lowercase__     )
   __lowercase				    =					'''A photo of a cat'''

   __lowercase				    =					tokenizer.encode(
       lowercase__   ,)
   # Same as above
   self.assertEqual(lowercase__   ,[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]     )






  @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,from_slow=lowercase__     )

   __lowercase				    =					'''bos'''
   __lowercase				    =					tokenizer.get_vocab()['''bos''']

   __lowercase				    =					'''A photo of a cat'''
   __lowercase				    =					tokenizer.encode(
       lowercase__   ,)
   # We changed the bos token
   self.assertEqual(lowercase__   ,[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]     )
   tokenizer.save_pretrained('''./tok'''     )
   __lowercase				    =					AutoTokenizer.from_pretrained('''./tok'''     )
   self.assertTrue(tokenizer.is_fast     )
   __lowercase				    =					tokenizer.encode(
       lowercase__   ,)
   self.assertEqual(lowercase__   ,[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]     )


'''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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__				:					Union[str, Any] 					=	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__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

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

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	torch.mul(query_input.unsqueeze(-1				)       ,key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)




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



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	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__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

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

         UpperCAmelCase__				:					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.
         UpperCAmelCase__				:					int 					=	torch.broadcast_to(
             torch.arange(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

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

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase__				:					Dict 					=	[(x, self.encoder_decoder_mask(lowerCamelCase_       ,lowerCamelCase_				)) for x, y in encodings_and_masks]

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

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	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       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

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

            UpperCAmelCase__				:					Union[str, Any] 					=	self.layer[1](
                lowerCamelCase_       ,key_value_states=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[-1](lowerCamelCase_       ,lowerCamelCase_				)

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Optional[int] 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	TaDenseGatedActDense(d_model=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.DenseReluDense(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




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



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




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



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(lowerCamelCase_       ,3.0				))				))




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scale_bias(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	torch.chunk(lowerCamelCase_       ,2       ,-1				)
         UpperCAmelCase__				:					List[Any] 					=	x * (1 + scale) + shift
         return x




