


'''simple docstring'''







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
   import torch


@require_flax
class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=14      ,							SCREAMING_SNAKE_CASE			:							Dict=7      ,							SCREAMING_SNAKE_CASE			:							Tuple=True      ,							SCREAMING_SNAKE_CASE			:							str=True      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=True      ,							SCREAMING_SNAKE_CASE			:							Any=99      ,							SCREAMING_SNAKE_CASE			:							List[Any]=32      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=5      ,							SCREAMING_SNAKE_CASE			:							Tuple=4      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=37      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]="gelu"      ,							SCREAMING_SNAKE_CASE			:							str=0.1      ,							SCREAMING_SNAKE_CASE			:							Tuple=0.1      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=512      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=0.02      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=None      ,							):
           _A     :	Any      =					parent
           _A     :	Any      =					batch_size
           _A     :	Any      =					seq_length
           _A     :	List[Any]      =					is_training
           _A     :	Any      =					use_input_mask
           _A     :	int      =					use_labels
           _A     :	List[Any]      =					vocab_size
           _A     :	str      =					d_model
           _A     :	List[Any]      =					num_hidden_layers
           _A     :	str      =					num_attention_heads
           _A     :	int      =					ffn_dim
           _A     :	Union[str, Any]      =					activation_function
           _A     :	int      =					activation_dropout
           _A     :	str      =					attention_dropout
           _A     :	Optional[Any]      =					max_position_embeddings
           _A     :	Optional[int]      =					initializer_range
           _A     :	Optional[Any]      =					None
           _A     :	List[str]      =					0
           _A     :	int      =					2
           _A     :	Optional[Any]      =					1


    def    A    (  self			:							Dict):
           _A     :	Optional[Any]      =					np.clip(ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size)      ,							3      ,							self.vocab_size)

           _A     :	Optional[int]      =					None
           if self.use_input_mask:
                  _A     :	Any      =					random_attention_mask([self.batch_size, self.seq_length])

           _A     :	Optional[Any]      =					XGLMConfig(
               vocab_size=self.vocab_size      ,							d_model=self.hidden_size      ,							num_layers=self.num_hidden_layers      ,							attention_heads=self.num_attention_heads      ,							ffn_dim=self.ffn_dim      ,							activation_function=self.activation_function      ,							activation_dropout=self.activation_dropout      ,							attention_dropout=self.attention_dropout      ,							max_position_embeddings=self.max_position_embeddings      ,							initializer_range=self.initializer_range      ,							use_cache=SCREAMING_SNAKE_CASE      ,							bos_token_id=self.bos_token_id      ,							eos_token_id=self.eos_token_id      ,							pad_token_id=self.pad_token_id      ,							)

           return (config, input_ids, input_mask)


    def    A    (  self			:							Any):
           _A     :	Any      =					self.prepare_config_and_inputs()
           _A  , _A  , _A     :	Dict      =					config_and_inputs
           _A     :	int      =					{'input_ids': input_ids, 'attention_mask': attention_mask}
           return config, inputs_dict


    def    A    (  self			:							Any):
           _A  , _A  , _A     :	Optional[int]      =					self.prepare_config_and_inputs()

           _A     :	List[str]      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _A     :	Union[str, Any]      =					ids_tensor([self.batch_size, self.seq_length]      ,							vocab_size=2)

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )


    def    A    (  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							int      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	List[str]      =					20
           _A     :	Union[str, Any]      =					model_class_name(SCREAMING_SNAKE_CASE)

           _A     :	Union[str, Any]      =					model.init_cache(input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE)
           _A     :	Dict      =					jnp.ones((input_ids.shape[0], max_decoder_length)      ,							dtype='i4')

           _A     :	List[Any]      =					jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :]      ,							(input_ids.shape[0], input_ids.shape[-1] - 1))
           _A     :	Any      =					model(
               input_ids[:, :-1]      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Union[str, Any]      =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Any      =					model(
               input_ids[:, -1:]      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=outputs_cache.past_key_values      ,							position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Any      =					model(SCREAMING_SNAKE_CASE)

           _A     :	List[Any]      =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3      ,							msg=F'Max diff is {diff}')


    def    A    (  self			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]):
           _A     :	Dict      =					20
           _A     :	Optional[int]      =					model_class_name(SCREAMING_SNAKE_CASE)

           _A     :	Tuple      =					jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]      ,							axis=-1      ,							)

           _A     :	Union[str, Any]      =					model.init_cache(input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE)
           _A     :	List[Any]      =					jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :]      ,							(input_ids.shape[0], input_ids.shape[-1] - 1))

           _A     :	Tuple      =					model(
               input_ids[:, :-1]      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							)
           _A     :	Optional[int]      =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Tuple      =					model(
               input_ids[:, -1:]      ,							past_key_values=outputs_cache.past_key_values      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	int      =					model(SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE)
           _A     :	List[Any]      =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3      ,							msg=F'Max diff is {diff}')




@require_sentencepiece
@require_flax
class  __lowerCamelCase (       a_				,					a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
    a													=    (FlaxXGLMForCausalLM,) if is_flax_available() else ()


    def    A    (  self			:							Dict):
           _A     :	Union[str, Any]      =					FlaxXGLMModelTester(self)


    def    A    (  self			:							Any):
           for model_class_name in self.all_model_classes:
                  _A  , _A  , _A     :	int      =					self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							List[Any]):
           for model_class_name in self.all_model_classes:
                  _A  , _A  , _A     :	List[Any]      =					self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    @slow
    def    A    (  self			:							List[Any]):
           _A     :	Optional[Any]      =					XGLMTokenizer.from_pretrained('XGLM'      ,							padding_side='left')
           _A     :	Any      =					tokenizer(['Hello this is a long string', 'Hey']      ,							return_tensors='np'      ,							padding=SCREAMING_SNAKE_CASE      ,							truncation=SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
           _A     :	Optional[int]      =					1
           _A     :	Optional[Any]      =					False

           _A     :	Dict      =					jax.jit(model.generate)

           _A     :	Optional[int]      =					jit_generate(inputs['input_ids']      ,							attention_mask=inputs['attention_mask']).sequences

           _A     :	Optional[Any]      =					tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,							skip_special_tokens=SCREAMING_SNAKE_CASE)

           _A     :	str      =					[
               'Hello this is a long string of questions, but I\'m not sure if I\'m',
               'Hey, I\'m a newbie to the forum and I\'',
           ]

           self.assertListEqual(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    @is_pt_flax_cross_test
    def    A    (  self			:							Dict):
           _A  , _A     :	Union[str, Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         # prepare inputs
                         _A     :	Any      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	List[Any]      =					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         _A     :	Optional[Any]      =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         _A     :	Optional[int]      =					getattr(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

                         _A  , _A     :	Dict      =					pt_inputs['input_ids'].shape
                         _A     :	int      =					np.random.randint(0      ,							seq_length - 1      ,							size=(batch_size,))
                         for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE):
                                _A     :	str      =					0
                                _A     :	List[Any]      =					1
                                _A     :	Optional[int]      =					0
                                _A     :	str      =					1
                         _A     :	List[str]      =					pt_model_class(SCREAMING_SNAKE_CASE).eval()
                         # Flax models don't use the `use_cache` option and cache is not returned as a default.
                         # So we disable `use_cache` here for PyTorch model.
                         _A     :	Dict      =					False
                         _A     :	List[Any]      =					model_class(SCREAMING_SNAKE_CASE      ,							dtype=jnp.floataa)
                         _A     :	Optional[int]      =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,							SCREAMING_SNAKE_CASE)
                         _A     :	Union[str, Any]      =					fx_state

                         with torch.no_grad():
                                _A     :	List[Any]      =					pt_model(**SCREAMING_SNAKE_CASE).to_tuple()

                         _A     :	Any      =					fx_model(**SCREAMING_SNAKE_CASE).to_tuple()
                         self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE)      ,							'Output lengths differ between Flax and PyTorch')
                         for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assert_almost_equals(fx_output[:, -1]      ,							pt_output[:, -1].numpy()      ,							4e-2)

                         with tempfile.TemporaryDirectory() as tmpdirname:
                                pt_model.save_pretrained(SCREAMING_SNAKE_CASE)
                                _A     :	List[str]      =					model_class.from_pretrained(SCREAMING_SNAKE_CASE      ,							from_pt=SCREAMING_SNAKE_CASE)

                         _A     :	Union[str, Any]      =					fx_model_loaded(**SCREAMING_SNAKE_CASE).to_tuple()
                         self.assertEqual(
                             len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE)      ,							'Output lengths differ between Flax and PyTorch')
                         for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assert_almost_equals(fx_output_loaded[:, -1]      ,							pt_output[:, -1].numpy()      ,							4e-2)


    @is_pt_flax_cross_test
    def    A    (  self			:							List[Any]):
           _A  , _A     :	List[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         # prepare inputs
                         _A     :	Optional[Any]      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	int      =					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         _A     :	Any      =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         _A     :	Tuple      =					getattr(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

                         _A     :	List[str]      =					pt_model_class(SCREAMING_SNAKE_CASE).eval()
                         _A     :	List[str]      =					False
                         _A     :	str      =					model_class(SCREAMING_SNAKE_CASE      ,							dtype=jnp.floataa)

                         _A     :	int      =					load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE      ,							fx_model.params)
                         _A  , _A     :	Optional[int]      =					pt_inputs['input_ids'].shape
                         _A     :	List[Any]      =					np.random.randint(0      ,							seq_length - 1      ,							size=(batch_size,))
                         for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE):
                                _A     :	Union[str, Any]      =					0
                                _A     :	str      =					1
                                _A     :	Dict      =					0
                                _A     :	Tuple      =					1

                         # make sure weights are tied in PyTorch
                         pt_model.tie_weights()

                         with torch.no_grad():
                                _A     :	Union[str, Any]      =					pt_model(**SCREAMING_SNAKE_CASE).to_tuple()

                         _A     :	Optional[Any]      =					fx_model(**SCREAMING_SNAKE_CASE).to_tuple()
                         self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE)      ,							'Output lengths differ between Flax and PyTorch')
                         for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assert_almost_equals(fx_output[:, -1]      ,							pt_output[:, -1].numpy()      ,							4e-2)

                         with tempfile.TemporaryDirectory() as tmpdirname:
                                fx_model.save_pretrained(SCREAMING_SNAKE_CASE)
                                _A     :	Union[str, Any]      =					pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE      ,							from_flax=SCREAMING_SNAKE_CASE)

                         with torch.no_grad():
                                _A     :	str      =					pt_model_loaded(**SCREAMING_SNAKE_CASE).to_tuple()

                         self.assertEqual(
                             len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE)      ,							'Output lengths differ between Flax and PyTorch')
                         for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assert_almost_equals(fx_output[:, -1]      ,							pt_output[:, -1].numpy()      ,							4e-2)


    @slow
    def    A    (  self			:							Union[str, Any]):
           for model_class_name in self.all_model_classes:
                  _A     :	Optional[int]      =					model_class_name.from_pretrained('facebook/xglm-564M')
                  _A     :	str      =					model(np.ones((1, 1)))
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)










'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

A      :     Optional[int]										= logging.get_logger(__name__)






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			bool = True   ):
       _A     :	Any      =					{}
       for name in model.attn_processors.keys():
              _A     :	Any      =					name.endswith('attn2.processor'   ) and not (
                  name.startswith('transformer_in'   ) or 'temp_attentions' in name.split('.'   )
              )
              _A     :	Dict      =					model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith('mid_block'   ):
                     _A     :	Optional[int]      =					model.config.block_out_channels[-1]
              elif name.startswith('up_blocks'   ):
                     _A     :	str      =					int(name[len('up_blocks.'   )]   )
                     _A     :	Any      =					list(reversed(model.config.block_out_channels   )   )[block_id]
              elif name.startswith('down_blocks'   ):
                     _A     :	str      =					int(name[len('down_blocks.'   )]   )
                     _A     :	int      =					model.config.block_out_channels[block_id]
              elif name.startswith('transformer_in'   ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     _A     :	Dict      =					8 * model.config.attention_head_dim

              _A     :	int      =					LoRAAttnProcessor(hidden_size=lowerCamelCase  ,cross_attention_dim=lowerCamelCase   )
              _A     :	Tuple      =					lora_attn_procs[name].to(model.device   )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs


@skip_mps
class  __lowerCamelCase (       a_				,					a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    UNetaDConditionModel
    a													=    "sample"


    @property
    def    A    (  self			:							Tuple):
           _A     :	List[Any]      =					4
           _A     :	Tuple      =					4
           _A     :	int      =					4
           _A     :	Dict      =					(32, 32)

           _A     :	Any      =					floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)

           return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


    @property
    def    A    (  self			:							Tuple):
           return (4, 4, 32, 32)


    @property
    def    A    (  self			:							str):
           return (4, 4, 32, 32)


    def    A    (  self			:							List[Any]):
           _A     :	List[str]      =					{
               'block_out_channels': (32, 64),
               'down_block_types': (
                   'CrossAttnDownBlock3D',
                   'DownBlock3D',
               ),
               'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
               'cross_attention_dim': 32,
               'attention_head_dim': 8,
               'out_channels': 4,
               'in_channels': 4,
               'layers_per_block': 1,
               'sample_size': 32,
           }
           _A     :	int      =					self.dummy_input
           return init_dict, inputs_dict


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							Tuple):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)

           model.enable_xformers_memory_efficient_attention()

           assert (
               model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
               == "XFormersAttnProcessor"
           ), "xformers is not enabled"


    def    A    (  self			:							str):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Union[str, Any]      =					32

           _A     :	int      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	List[str]      =					model(**SCREAMING_SNAKE_CASE)

                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	int      =					output.sample

           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           _A     :	Optional[int]      =					inputs_dict['sample'].shape
           self.assertEqual(output.shape      ,							SCREAMING_SNAKE_CASE      ,							'Input and output shapes do not match')


    def    A    (  self			:							Dict):
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  # Warmup pass when using mps (see #372)
                  if torch_device == "mps" and isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         model(**self.dummy_input)

                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Optional[int]      =					first.sample

                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					second.sample

           _A     :	Dict      =					first.cpu().numpy()
           _A     :	Dict      =					second.cpu().numpy()
           _A     :	Optional[int]      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	Dict      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	List[Any]      =					np.amax(np.abs(out_a - out_a))
           self.assertLessEqual(SCREAMING_SNAKE_CASE      ,							1e-5)


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[int]      =					8

           _A     :	Optional[Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           model.set_attention_slice('auto')
           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice('max')
           with torch.no_grad():
                  _A     :	Optional[Any]      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice(2)
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Tuple      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Dict      =					8

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	List[str]      =					create_lora_layers(SCREAMING_SNAKE_CASE)

           # make sure we can set a list of attention processors
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           # test that attn processors can be set to itself
           model.set_attn_processor(model.attn_processors)

           with torch.no_grad():
                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (samplea - samplea).abs().max() < 3e-3
           assert (samplea - samplea).abs().max() < 3e-3

           # sample 2 and sample 3 should be different
           assert (samplea - samplea).abs().max() > 3e-3


    def    A    (  self			:							str):
           _A  , _A     :	Optional[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Tuple      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	List[Any]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 1e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Union[str, Any]):
           _A  , _A     :	Any      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Any      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Any      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE      ,							safe_serialization=SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.safetensors')))
                  torch.manual_seed(0)
                  _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 3e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Optional[int]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	List[Any]      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							weight_name='pytorch_lora_weights.bin')


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[Any]      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Union[str, Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  with self.assertRaises(SCREAMING_SNAKE_CASE) as e:
                         new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							use_safetensors=SCREAMING_SNAKE_CASE)
                  self.assertIn('Error no file named pytorch_lora_weights.safetensors'      ,							str(e.exception))


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	str      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	str      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample

           model.set_attn_processor(AttnProcessor())

           with torch.no_grad():
                  _A     :	List[Any]      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - new_sample).abs().max() < 1e-4
           assert (sample - old_sample).abs().max() < 3e-3


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							List[str]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					4

           torch.manual_seed(0)
           _A     :	str      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           _A     :	str      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           # default
           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.enable_xformers_memory_efficient_attention()
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.disable_xformers_memory_efficient_attention()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - on_sample).abs().max() < 1e-4
           assert (sample - off_sample).abs().max() < 1e-4


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	List[Any]      =					32

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)[0]

           self.assertEqual(output.shape      ,							output_a.shape      ,							'Shape doesn\'t match')
           assert np.abs(output.cpu() - output_a.cpu()).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






