



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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: torch.FloatTensor







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






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

    """simple docstring"""







    super().__init__()
    _snake_case    :  List[Any]					=					num_attention_heads
    _snake_case    :  Union[str, Any]					=					attention_head_dim
    _snake_case    :  Dict					=					num_attention_heads * attention_head_dim

    _snake_case    :  Tuple					=					in_channels

    _snake_case    :  int					=					torch.nn.GroupNorm(num_groups=lowerCAmelCase				,		num_channels=lowerCAmelCase				,		eps=1E-6				,		affine=lowerCAmelCase)
    _snake_case    :  int					=					nn.Linear(lowerCAmelCase				,		lowerCAmelCase)

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

    _snake_case    :  List[Any]					=					nn.Linear(lowerCAmelCase				,		lowerCAmelCase)


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Tuple=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			Optional[Any]=1				,		lowerCAmelCase							:			Union[str, Any]=None				,		lowerCAmelCase							:			bool = True				,		)				->				Dict:

    """simple docstring"""







    _snake_case	,    _snake_case	,    _snake_case	,    _snake_case    :  List[Any]					=					hidden_states.shape
    _snake_case    :  Tuple					=					batch_frames // num_frames

    _snake_case    :  Union[str, Any]					=					hidden_states

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

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

    _snake_case    :  Any					=					self.proj_in(lowerCAmelCase)

    # 2. Blocks
    for block in self.transformer_blocks:
     _snake_case    :  str					=					block(
         lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		timestep=lowerCAmelCase				,		cross_attention_kwargs=lowerCAmelCase				,		class_labels=lowerCAmelCase				,		)

    # 3. Output
    _snake_case    :  str					=					self.proj_out(lowerCAmelCase)
    _snake_case    :  str					=					(
        hidden_states[None, None, :]
        .reshape(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
        .permute(0				,		3				,		4				,		1				,		2)
        .contiguous()
    )
    _snake_case    :  Tuple					=					hidden_states.reshape(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  List[Any]					=					hidden_states + residual

    if not return_dict:
     return (output,)

    return TransformerTemporalModelOutput(sample=lowerCAmelCase)





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Optional[Any]          =		CTRLTokenizer
   snake_case_					: Optional[Any]          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			int)				->				Dict:

    """simple docstring"""







    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _snake_case    :  Dict					=					["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
    _snake_case    :  Optional[int]					=					dict(zip(lowerCAmelCase				,		range(len(lowerCAmelCase))))
    _snake_case    :  int					=					["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
    _snake_case    :  List[Any]					=					{"""unk_token""": """<unk>"""}

    _snake_case    :  Dict					=					os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["""vocab_file"""])
    _snake_case    :  Any					=					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(lowerCAmelCase) + """\n""")
    with open(self.merges_file				,		"""w"""				,		encoding="""utf-8""") as fp:
     fp.write("""\n""".join(lowerCAmelCase))

   def      UpperCamelCase_  (					self							:			Dict				,		**lowerCAmelCase							:			int)				->				Optional[Any]:

    """simple docstring"""







    kwargs.update(self.special_tokens_map)
    return CTRLTokenizer.from_pretrained(self.tmpdirname				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    return input_text, output_text


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

    """simple docstring"""







    _snake_case    :  str					=					CTRLTokenizer(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map)
    _snake_case    :  Dict					=					"""adapt react readapt apt"""
    _snake_case    :  int					=					"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
    _snake_case    :  Tuple					=					tokenizer.tokenize(lowerCAmelCase)
    self.assertListEqual(lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  str					=					tokens + [tokenizer.unk_token]

    _snake_case    :  Union[str, Any]					=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)				,		lowerCAmelCase)
