


'''simple docstring'''



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :List[Any]						=   XLMTokenizer
					lowerCAmelCase :Dict						=   False




					def 			snake_case__  ( self):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase__					:   Dict       				=       [
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								UpperCAmelCase__					:   Tuple       				=       dict(zip(_lowerCamelCase							,							range(len(_lowerCamelCase))))
								UpperCAmelCase__					:   Dict       				=       ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								UpperCAmelCase__					:   int       				=       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCAmelCase__					:   Union[str, Any]       				=       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["""merges_file"""])
								with open(self.vocab_file							,							"""w""") as fp:
											fp.write(json.dumps(_lowerCamelCase))
								with open(self.merges_file							,							"""w""") as fp:
											fp.write("""\n""".join(_lowerCamelCase))




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       """lower newer"""
								UpperCAmelCase__					:   Any       				=       """lower newer"""
								return input_text, output_text




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       XLMTokenizer(self.vocab_file							,							self.merges_file)

								UpperCAmelCase__					:   Any       				=       """lower"""
								UpperCAmelCase__					:   Any       				=       ["""low""", """er</w>"""]
								UpperCAmelCase__					:   Optional[int]       				=       tokenizer.tokenize(_lowerCamelCase)
								self.assertListEqual(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__					:   List[str]       				=       tokens + ["""<unk>"""]
								UpperCAmelCase__					:   str       				=       [14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase)							,							_lowerCamelCase)




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       XLMTokenizer.from_pretrained("""xlm-mlm-en-2048""")

								UpperCAmelCase__					:   Any       				=       tokenizer.encode("""sequence builders"""							,							add_special_tokens=_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       tokenizer.encode("""multi-sequence build"""							,							add_special_tokens=_lowerCamelCase)

								UpperCAmelCase__					:   Tuple       				=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase							,							_lowerCamelCase)

								assert encoded_sentence == [0] + text + [1]
								assert encoded_pair == [0] + text + [1] + text_a + [1]



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)