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        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42







class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase				):





			@register_to_config
			def __init__(       self	:	int  ,  lowercase__	:	int = 16  ,  lowercase__	:	int = 88  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	int = 1  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 32  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	bool = False  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	str = "geglu"  ,  lowercase__	:	bool = True  ,  lowercase__	:	bool = True  ,  ):
										super().__init__()
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							attention_head_dim
										_lowerCAmelCase							=							num_attention_heads * attention_head_dim

										_lowerCAmelCase							=							in_channels

										_lowerCAmelCase							=							torch.nn.GroupNorm(num_groups=lowercase__  ,  num_channels=lowercase__  ,  eps=1e-6  ,  affine=lowercase__						)
										_lowerCAmelCase							=							nn.Linear(lowercase__  ,  lowercase__						)

										# 3. Define transformers blocks
										_lowerCAmelCase							=							nn.ModuleList(
										    [
										        BasicTransformerBlock(
										            lowercase__  ,  lowercase__  ,  lowercase__  ,  dropout=lowercase__  ,  cross_attention_dim=lowercase__  ,  activation_fn=lowercase__  ,  attention_bias=lowercase__  ,  double_self_attention=lowercase__  ,  norm_elementwise_affine=lowercase__  ,  )
										        for d in range(lowercase__						)
										    ]						)

										_lowerCAmelCase							=							nn.Linear(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Any  ,  lowercase__	:	List[Any]=None  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	Dict=1  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							hidden_states.shape
										_lowerCAmelCase							=							batch_frames // num_frames

										_lowerCAmelCase							=							hidden_states

										_lowerCAmelCase							=							hidden_states[None, :].reshape(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							hidden_states.permute(0  ,  2  ,  1  ,  3  ,  4						)

										_lowerCAmelCase							=							self.norm(lowercase__						)
										_lowerCAmelCase							=							hidden_states.permute(0  ,  3  ,  4  ,  2  ,  1						).reshape(batch_size * height * width  ,  lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							self.proj_in(lowercase__						)

										# 2. Blocks
										for block in self.transformer_blocks:
																	_lowerCAmelCase							=							block(
																	    lowercase__  ,  encoder_hidden_states=lowercase__  ,  timestep=lowercase__  ,  cross_attention_kwargs=lowercase__  ,  class_labels=lowercase__  ,  )

										# 3. Output
										_lowerCAmelCase							=							self.proj_out(lowercase__						)
										_lowerCAmelCase							=							(
										    hidden_states[None, None, :]
										    .reshape(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)
										    .permute(0  ,  3  ,  4  ,  1  ,  2						)
										    .contiguous()
										)
										_lowerCAmelCase							=							hidden_states.reshape(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							hidden_states + residual

										if not return_dict:
																	return (output,)

										return TransformerTemporalModelOutput(sample=lowercase__						)







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowercase:	str       			=		'''sshleifer/bart-tiny-random'''
_lowercase:	Union[str, Any]       			=		'''patrickvonplaten/t5-tiny-random'''



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return AutoConfig.from_pretrained(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.num_hidden_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  self.teacher_config.encoder_layers						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										with self.assertRaises(lowercase__						):
																	create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=lowercase__  ,  d=lowercase__						)






