

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 _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =XLMTokenizer
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    "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>",
				]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dict(zip(a      ,      range(len(a	)	)	)	)
				SCREAMING_SNAKE_CASE   :      int				     =       ["l o 123", "lo w 1456", "e r</w> 1789", ""]

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["merges_file"]	)
				with open(self.vocab_file      ,      "w"	) as fp:
					fp.write(json.dumps(a	)	)
				with open(self.merges_file      ,      "w"	) as fp:
					fp.write("\n".join(a	)	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Dict	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       "lower newer"
				return input_text, output_text
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       XLMTokenizer(self.vocab_file      ,      self.merges_file	)

				SCREAMING_SNAKE_CASE   :      int				     =       "lower"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ["low", "er</w>"]
				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.tokenize(a	)
				self.assertListEqual(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokens + ["<unk>"]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [14, 15, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(a	)      ,      a	)






			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       XLMTokenizer.from_pretrained("xlm-mlm-en-2048"	)

				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.encode("sequence builders"      ,      add_special_tokens=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       tokenizer.encode("multi-sequence build"      ,      add_special_tokens=a	)

				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.build_inputs_with_special_tokens(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.build_inputs_with_special_tokens(a      ,      a	)

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


from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: str      ,      a							: int = 16      ,      a							: int = 88      ,      a							: Optional[int] = None      ,      a							: Optional[int] = None      ,      a							: int = 1      ,      a							: float = 0.0      ,      a							: int = 32      ,      a							: Optional[int] = None      ,      a							: bool = False      ,      a							: Optional[int] = None      ,      a							: str = "geglu"      ,      a							: bool = True      ,      a							: bool = True      ,      )							->       str:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_head_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads * attention_head_dim

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_channels

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.nn.GroupNorm(num_groups=a      ,      num_channels=a      ,      eps=1e-6      ,      affine=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.Linear(a      ,      a	)

				# 3. Define transformers blocks
				SCREAMING_SNAKE_CASE   :      List[str]				     =       nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a      ,      a      ,      a      ,      dropout=a      ,      cross_attention_dim=a      ,      activation_fn=a      ,      attention_bias=a      ,      double_self_attention=a      ,      norm_elementwise_affine=a      ,      )
				        for d in range(a	)
				    ]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(a      ,      a	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Optional[int]=None      ,      a							: List[str]=None      ,      a							: List[str]=None      ,      a							: Union[str, Any]=1      ,      a							: Any=None      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       hidden_states.shape
				SCREAMING_SNAKE_CASE   :      Any				     =       batch_frames // num_frames

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_states[None, :].reshape(a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states.permute(0      ,      2      ,      1      ,      3      ,      4	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.norm(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_states.permute(0      ,      3      ,      4      ,      2      ,      1	).reshape(batch_size * height * width      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.proj_in(a	)

				# 2. Blocks
				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       block(
					    a      ,      encoder_hidden_states=a      ,      timestep=a      ,      cross_attention_kwargs=a      ,      class_labels=a      ,      )

				# 3. Output
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.proj_out(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       (
				    hidden_states[None, None, :]
				    .reshape(a      ,      a      ,      a      ,      a      ,      a	)
				    .permute(0      ,      3      ,      4      ,      1      ,      2	)
				    .contiguous()
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states.reshape(a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=a	)