






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  PhobertTokenizer
	snake_case__							:     List[str]     	=  False





	def 						_A (		self				:					Optional[int]	):
		super().setUp()

		# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
		UpperCamelCase     :int          =	["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
		UpperCamelCase     :List[str]          =	dict(zip(__lowerCamelCase ,  range(len(__lowerCamelCase	)	)	)	)
		UpperCamelCase     :Tuple          =	["""#version: 0.2""", """l à</w>"""]
		UpperCamelCase     :Dict          =	{"""unk_token""": """<unk>"""}

		UpperCamelCase     :str          =	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""vocab_file"""]	)
		UpperCamelCase     :str          =	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""merges_file"""]	)

		with open(self.vocab_file ,  """w""" ,  encoding="""utf-8"""	) as fp:
			for token in vocab_tokens:
				fp.write(F"""{token} {vocab_tokens[token]}\n"""	)
		with open(self.merges_file ,  """w""" ,  encoding="""utf-8"""	) as fp:
			fp.write("""\n""".join(__lowerCamelCase	)	)





	def 						_A (		self				:					List[Any] ,  **__lowerCamelCase				:					Optional[int]	):
		kwargs.update(self.special_tokens_map	)
		return PhobertTokenizer.from_pretrained(self.tmpdirname ,  **__lowerCamelCase	)





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :List[str]          =	"""Tôi là VinAI Research"""
		UpperCamelCase     :Union[str, Any]          =	"""T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
		return input_text, output_text





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Any          =	PhobertTokenizer(self.vocab_file ,  self.merges_file ,  **self.special_tokens_map	)
		UpperCamelCase     :int          =	"""Tôi là VinAI Research"""
		UpperCamelCase     :List[str]          =	"""T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
		UpperCamelCase     :List[str]          =	tokenizer.tokenize(__lowerCamelCase	)
		print(__lowerCamelCase	)
		self.assertListEqual(__lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	tokens + [tokenizer.unk_token]

		UpperCamelCase     :List[Any]          =	[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase	) ,  __lowerCamelCase	)




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

