

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 			A						(							nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any],lowercase_			:	int = 1_6,lowercase_			:	int = 8_8,lowercase_			:	Optional[int] = None,lowercase_			:	int = 1,lowercase_			:	float = 0.0,lowercase_			:	int = 3_2,lowercase_			:	Optional[int] = None,lowercase_			:	bool = False,lowercase_			:	Optional[int] = None,lowercase_			:	Optional[int] = None,lowercase_			:	str = "geglu",lowercase_			:	Optional[int] = None,)->   List[Any]:





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=lowercase_,attention_head_dim=lowercase_,in_channels=lowercase_,num_layers=lowercase_,dropout=lowercase_,norm_num_groups=lowercase_,cross_attention_dim=lowercase_,attention_bias=lowercase_,sample_size=lowercase_,num_vector_embeds=lowercase_,activation_fn=lowercase_,num_embeds_ada_norm=lowercase_,)
                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
        A__   				=       0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        A__   				=       [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])`
        A__   				=       [1, 0]


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





        '''simple docstring'''







        A__   				=       hidden_states

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

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

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=lowercase_		)




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class 			A						(							nn.Module			):







    """simple docstring"""





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





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.Linear(3,4		)
        A__   				=       nn.BatchNormad(4		)
        A__   				=       nn.Linear(4,5		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       ModelForTest()
        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,model.state_dict()		)
            A__   				=       os.path.join(lowercase_,'index.json'		)
            self.assertTrue(os.path.isfile(lowercase_		)		)
            # TODO: add tests on what is inside the index

            for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                A__   				=       os.path.join(lowercase_,F'{key}.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                # TODO: add tests on the fact weights are properly loaded


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       [torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
            A__   				=       torch.randn(2,3,dtype=lowercase_		)
            with TemporaryDirectory() as tmp_dir:
                A__   				=       offload_weight(lowercase_,'weight',lowercase_,{}		)
                A__   				=       os.path.join(lowercase_,'weight.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                self.assertDictEqual(lowercase_,{'weight': {'shape': [2, 3], 'dtype': str(lowercase_		).split('.'		)[1]}}		)

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       ModelForTest()
        A__   				=       model.state_dict()
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' not in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        A__   				=       {k: v for k, v in state_dict.items() if 'weight' in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            # Duplicates are removed
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)


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





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

