

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor


class     lowerCAmelCase				(    unittest.TestCase				):


       '''simple docstring'''







       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  bool = False     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    10

        if only_cross_attention:
         lowerCamelCase      						=    12
        else:
         # when only cross attention is not set, the cross attention dim must be the same as the query dim
         lowerCamelCase      						=    query_dim

        return {
            "query_dim": query_dim,
            "cross_attention_dim": cross_attention_dim,
            "heads": 2,
            "dim_head": 4,
            "added_kv_proj_dim": 6,
            "norm_num_groups": 1,
            "only_cross_attention": only_cross_attention,
            "processor": AttnAddedKVProcessor(),
        }

       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[str]						,	__snake_case					:  Any     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    2

        lowerCamelCase      						=    torch.rand(__snake_case						,	__snake_case						,	3						,	2     )
        lowerCamelCase      						=    torch.rand(__snake_case						,	4						,	__snake_case     )
        lowerCamelCase      						=    None

        return {
            "hidden_states": hidden_states,
            "encoder_hidden_states": encoder_hidden_states,
            "attention_mask": attention_mask,
        }





       def   lowerCamelCase__						(	self					:  Dict     )   -> int:







        '''simple docstring'''

        torch.manual_seed(0     )

        lowerCamelCase      						=    self.get_constructor_arguments(only_cross_attention=__snake_case     )
        lowerCamelCase      						=    Attention(**__snake_case     )

        self.assertTrue(attn.to_k is not None     )
        self.assertTrue(attn.to_v is not None     )

        lowerCamelCase      						=    self.get_forward_arguments(
            query_dim=constructor_args['query_dim']						,	added_kv_proj_dim=constructor_args['added_kv_proj_dim']     )

        lowerCamelCase      						=    attn(**__snake_case     )

        # only self attention

        torch.manual_seed(0     )

        lowerCamelCase      						=    self.get_constructor_arguments(only_cross_attention=__snake_case     )
        lowerCamelCase      						=    Attention(**__snake_case     )

        self.assertTrue(attn.to_k is None     )
        self.assertTrue(attn.to_v is None     )

        lowerCamelCase      						=    self.get_forward_arguments(
            query_dim=constructor_args['query_dim']						,	added_kv_proj_dim=constructor_args['added_kv_proj_dim']     )

        lowerCamelCase      						=    attn(**__snake_case     )

        self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()     )


class     lowerCAmelCase				(    unittest.TestCase				):


       '''simple docstring'''







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







        '''simple docstring'''

        lowerCamelCase      						=    DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'						,	safety_checker=__snake_case     )

        lowerCamelCase      						=    pipe(
            'foo'						,	num_inference_steps=2						,	generator=torch.Generator('cpu'     ).manual_seed(0     )						,	output_type='np'						,	).images

        # the initial conversion succeeds
        lowerCamelCase      						=    DiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe'						,	device_map='sequential'						,	safety_checker=__snake_case     )

        lowerCamelCase      						=    pipe(
            'foo'						,	num_inference_steps=2						,	generator=torch.Generator('cpu'     ).manual_seed(0     )						,	output_type='np'						,	).images

        with tempfile.TemporaryDirectory() as tmpdir:
         # save the converted model
         pipe.save_pretrained(__snake_case     )

         # can also load the converted weights
         lowerCamelCase      						=    DiffusionPipeline.from_pretrained(__snake_case						,	device_map='sequential'						,	safety_checker=__snake_case     )

        lowerCamelCase      						=    pipe(
            'foo'						,	num_inference_steps=2						,	generator=torch.Generator('cpu'     ).manual_seed(0     )						,	output_type='np'						,	).images

        self.assertTrue(np.allclose(__snake_case						,	__snake_case						,	atol=1e-5     )     )
        self.assertTrue(np.allclose(__snake_case						,	__snake_case						,	atol=1e-5     )     )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase :	List[Any]     				=    {
    'configuration_switch_transformers': [
        'SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwitchTransformersConfig',
        'SwitchTransformersOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Tuple     				=    [
         'SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwitchTransformersEncoderModel',
         'SwitchTransformersForConditionalGeneration',
         'SwitchTransformersModel',
         'SwitchTransformersPreTrainedModel',
         'SwitchTransformersTop1Router',
         'SwitchTransformersSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




