





'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, 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()
class 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       AutoencoderKL
       _snake_case      :	str									=       'sample'
       _snake_case      :	Union[str, Any]									=       1e-2





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

            '''simple docstring'''




            _UpperCamelCase							       =			4
            _UpperCamelCase							       =			3
            _UpperCamelCase							       =			(32, 32)

            _UpperCamelCase							       =			floats_tensor((batch_size, num_channels) + sizes	).to(lowerCAmelCase__	)

            return {"sample": image}





       @property
       def snake_case__		(				self  :						str	)       ->	Optional[Any]:

            '''simple docstring'''




            return (3, 32, 32)





       @property
       def snake_case__		(				self  :						int	)       ->	Tuple:

            '''simple docstring'''




            return (3, 32, 32)





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

            '''simple docstring'''




            _UpperCamelCase							       =			{
                '''block_out_channels''': [32, 64],
                '''in_channels''': 3,
                '''out_channels''': 3,
                '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                '''latent_channels''': 4,
            }
            _UpperCamelCase							       =			self.dummy_input
            return init_dict, inputs_dict





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

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





       @unittest.skipIf(torch_device == '''mps'''			,					'''Gradient checkpointing skipped on MPS'''	)
       def snake_case__		(				self  :						Any	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase							       =			self.model_class(**lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)

            assert not model.is_gradient_checkpointing and model.training

            _UpperCamelCase							       =			model(**lowerCAmelCase__	).sample
            # run the backwards pass on the model. For backwards pass, for simplicity purpose,
            # we won't calculate the loss and rather backprop on out.sum()
            model.zero_grad()

            _UpperCamelCase							       =			torch.randn_like(lowerCAmelCase__	)
            _UpperCamelCase							       =			(out - labels).mean()
            loss.backward()

            # re-instantiate the model now enabling gradient checkpointing
            _UpperCamelCase							       =			self.model_class(**lowerCAmelCase__	)
            # clone model
            model_a.load_state_dict(model.state_dict()	)
            model_a.to(lowerCAmelCase__	)
            model_a.enable_gradient_checkpointing()

            assert model_a.is_gradient_checkpointing and model_a.training

            _UpperCamelCase							       =			model_a(**lowerCAmelCase__	).sample
            # run the backwards pass on the model. For backwards pass, for simplicity purpose,
            # we won't calculate the loss and rather backprop on out.sum()
            model_a.zero_grad()
            _UpperCamelCase							       =			(out_a - labels).mean()
            loss_a.backward()

            # compare the output and parameters gradients
            self.assertTrue((loss - loss_a).abs() < 1e-5	)
            _UpperCamelCase							       =			dict(model.named_parameters()	)
            _UpperCamelCase							       =			dict(model_a.named_parameters()	)
            for name, param in named_params.items():
                 self.assertTrue(torch_all_close(param.grad.data			,					named_params_a[name].grad.data			,					atol=5e-5	)	)





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''			,					output_loading_info=lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertEqual(len(loading_info['''missing_keys''']	)			,					0	)

            model.to(lowerCAmelCase__	)
            _UpperCamelCase							       =			model(**self.dummy_input	)

            assert image is not None, "Make sure output is not None"







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

            '''simple docstring'''




            _UpperCamelCase							       =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)
            model.eval()

            if torch_device == "mps":
                 _UpperCamelCase							       =			torch.manual_seed(0	)
            else:
                 _UpperCamelCase							       =			torch.Generator(device=lowerCAmelCase__	).manual_seed(0	)

            _UpperCamelCase							       =			torch.randn(
                1			,					model.config.in_channels			,					model.config.sample_size			,					model.config.sample_size			,					generator=torch.manual_seed(0	)			,					)
            _UpperCamelCase							       =			image.to(lowerCAmelCase__	)
            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__			,					generator=lowerCAmelCase__	).sample

            _UpperCamelCase							       =			output[0, -1, -3:, -3:].flatten().cpu()

            # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
            # the expected output slices are not the same for CPU and GPU.
            if torch_device == "mps":
                 _UpperCamelCase							       =			torch.tensor(
                     [
                         -4.0_0_7_8e-0_1,
                         -3.8_3_2_3e-0_4,
                         -1.2_6_8_1e-0_1,
                         -1.1_4_6_2e-0_1,
                         2.0_0_9_5e-0_1,
                         1.0_8_9_3e-0_1,
                         -8.8_2_4_7e-0_2,
                         -3.0_3_6_1e-0_1,
                         -9.8_6_4_4e-0_3,
                     ]	)
            elif torch_device == "cpu":
                 _UpperCamelCase							       =			torch.tensor(
                     [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]	)
            else:
                 _UpperCamelCase							       =			torch.tensor(
                     [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]	)

            self.assertTrue(torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					rtol=1e-2	)	)

@slow
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





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

            '''simple docstring'''




            return f"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase__	) for s in shape]	)}.npy"""





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

            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Any=0			,					lowerCAmelCase__  :						int=(4, 3, 512, 512)			,					lowerCAmelCase__  :						Tuple=False	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			torch.floataa if fpaa else torch.floataa
            _UpperCamelCase							       =			torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCAmelCase__			,					lowerCAmelCase__	)	)	).to(lowerCAmelCase__	).to(lowerCAmelCase__	)
            return image





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]="CompVis/stable-diffusion-v1-4"			,					lowerCAmelCase__  :						Dict=False	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			'''fp16''' if fpaa else None
            _UpperCamelCase							       =			torch.floataa if fpaa else torch.floataa

            _UpperCamelCase							       =			AutoencoderKL.from_pretrained(
                lowerCAmelCase__			,					subfolder='''vae'''			,					torch_dtype=lowerCAmelCase__			,					revision=lowerCAmelCase__			,					)
            model.to(lowerCAmelCase__	).eval()

            return model





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Tuple=0	)       ->	List[Any]:

            '''simple docstring'''




            if torch_device == "mps":
                 return torch.manual_seed(lowerCAmelCase__	)
            return torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
               [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=3e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
               [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-2	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
               [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						str	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=3e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
               [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
               [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)			,					fpaa=lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=5e-3	)





       @parameterized.expand([(13,), (16,), (27,)]	)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()			,					reason='''xformers is not required when using PyTorch 2.0.'''	)
       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						Optional[int]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)			,					fpaa=lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            model.enable_xformers_memory_efficient_attention()
            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-1	)





       @parameterized.expand([(13,), (16,), (37,)]	)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()			,					reason='''xformers is not required when using PyTorch 2.0.'''	)
       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            model.enable_xformers_memory_efficient_attention()
            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-2	)







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
               [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.encode(lowerCAmelCase__	).latent_dist
                 _UpperCamelCase							       =			dist.sample(generator=lowerCAmelCase__	)

            assert list(sample.shape	) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

            _UpperCamelCase							       =			sample[0, -1, -3:, -3:].flatten().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            _UpperCamelCase							       =			3e-3 if torch_device != '''mps''' else 1e-2
            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=lowerCAmelCase__	)









import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__UpperCamelCase   :			Dict          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      enum.Enum					):
							UpperCamelCase__   	=    0
							UpperCamelCase__   	=    1







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''generated'''
							def __init__(		self		:Any						, *__magic_name__		:Tuple						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								super().__init__(*__magic_name__						, **__magic_name__							)

								self.check_model_type(
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING							)
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Any=None						, __magic_name__		:Optional[Any]=None						, __magic_name__		:Any=None						, __magic_name__		:List[str]=None						, __magic_name__		:Tuple=None						, __magic_name__		:str=None						, **__magic_name__		:List[Any]						, ):







								'''simple docstring'''







								a									=				{}
								if truncation is not None:
									a									=				truncation

								a									=				generate_kwargs

								a									=				{}
								if return_tensors is not None and return_type is None:
									a									=				ReturnType.TENSORS if return_tensors else ReturnType.TEXT
								if return_type is not None:
									a									=				return_type

								if clean_up_tokenization_spaces is not None:
									a									=				clean_up_tokenization_spaces

								if stop_sequence is not None:
									a									=				self.tokenizer.encode(__magic_name__						, add_special_tokens=__magic_name__							)
									if len(__magic_name__							) > 1:
										warnings.warn(
										    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
										    """ the stop sequence will be used as the stop sequence string in the interim."""							)
									a									=				stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								return True
							def  lowerCamelCase__				(		self		:Dict						, *__magic_name__		:Optional[int]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				self.model.config.prefix if self.model.config.prefix is not None else """"""
								if isinstance(args[0]						, __magic_name__							):
									if self.tokenizer.pad_token_id is None:
										raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""							)
									a									=				([prefix + arg for arg in args[0]],)
									a									=				True

								elif isinstance(args[0]						, __magic_name__							):
									a									=				(prefix + args[0],)
									a									=				False
								else:
									raise ValueError(
									    F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'							)
								a									=				self.tokenizer(*__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, return_tensors=self.framework							)
								# This is produced by tokenizers but is an invalid generate kwargs
								if "token_type_ids" in inputs:
									del inputs["token_type_ids"]
								return inputs
							def __call__(		self		:Tuple						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







								a									=				super().__call__(*__magic_name__						, **__magic_name__							)
								if (
								    isinstance(args[0]						, __magic_name__							)
								    and all(isinstance(__magic_name__						, __magic_name__							) for el in args[0]							)
								    and all(len(__magic_name__							) == 1 for res in result							)
								):
									return [res[0] for res in result]
								return result
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[Any]						, __magic_name__		:List[str]=TruncationStrategy.DO_NOT_TRUNCATE						, **__magic_name__		:Any							):







								'''simple docstring'''







								a									=				self._parse_and_tokenize(__magic_name__						, truncation=__magic_name__						, **__magic_name__							)
								return inputs
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, **__magic_name__		:int							):







								'''simple docstring'''







								if self.framework == "pt":
									a							,					a									=				model_inputs["""input_ids"""].shape
								elif self.framework == "tf":
									a							,					a									=				tf.shape(model_inputs["""input_ids"""]							).numpy()

								a									=				generate_kwargs.get("""min_length"""						, self.model.config.min_length							)
								a									=				generate_kwargs.get("""max_length"""						, self.model.config.max_length							)
								self.check_inputs(__magic_name__						, generate_kwargs["""min_length"""]						, generate_kwargs["""max_length"""]							)
								a									=				self.model.generate(**__magic_name__						, **__magic_name__							)
								a									=				output_ids.shape[0]
								if self.framework == "pt":
									a									=				output_ids.reshape(__magic_name__						, out_b // in_b						, *output_ids.shape[1:]							)
								elif self.framework == "tf":
									a									=				tf.reshape(__magic_name__						, (in_b, out_b // in_b, *output_ids.shape[1:])							)
								return {"output_ids": output_ids}





							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Dict						, __magic_name__		:Any=ReturnType.TEXT						, __magic_name__		:int=False							):







								'''simple docstring'''







								a									=				[]
								for output_ids in model_outputs["output_ids"][0]:
									if return_type == ReturnType.TENSORS:
										a									=				{F'{self.return_name}_token_ids': output_ids}
									elif return_type == ReturnType.TEXT:
										a									=				{
										    F'{self.return_name}_text': self.tokenizer.decode(
										        __magic_name__						, skip_special_tokens=__magic_name__						, clean_up_tokenization_spaces=__magic_name__						, )
										}
									records.append(__magic_name__							)
								return records







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''summary'''
							def __call__(		self		:Any						, *__magic_name__		:List[str]						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								return super().__call__(*__magic_name__						, **__magic_name__							)





							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								if max_length < min_length:
									logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.'							)

								if input_length < max_length:
									logger.warning(
									    F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
									    """a summarization task, where outputs shorter than the input are typically wanted, you might """
									    F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'							)







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''translation'''
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								if input_length > 0.9 * max_length:
									logger.warning(
									    F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
									    """increasing your max_length manually, e.g. translator('...', max_length=400)"""							)
								return True
							def  lowerCamelCase__				(		self		:str						, *__magic_name__		:Union[str, Any]						, __magic_name__		:Any=TruncationStrategy.DO_NOT_TRUNCATE						, __magic_name__		:Optional[Any]=None						, __magic_name__		:List[str]=None							):







								'''simple docstring'''







								if getattr(self.tokenizer						, """_build_translation_inputs"""						, __magic_name__							):
									return self.tokenizer._build_translation_inputs(
									    *__magic_name__						, return_tensors=self.framework						, truncation=__magic_name__						, src_lang=__magic_name__						, tgt_lang=__magic_name__							)
								else:
									return super()._parse_and_tokenize(*__magic_name__						, truncation=__magic_name__							)
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=None						, __magic_name__		:Union[str, Any]=None						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a							,					a									=				super()._sanitize_parameters(**__magic_name__							)
								if src_lang is not None:
									a									=				src_lang
								if tgt_lang is not None:
									a									=				tgt_lang
								if src_lang is None and tgt_lang is None:
									# Backward compatibility, direct arguments use is preferred.
									a									=				kwargs.get("""task"""						, self.task							)
									a									=				task.split("""_"""							)
									if task and len(__magic_name__							) == 4:
										# translation, XX, to YY
										a									=				items[1]
										a									=				items[3]
								return preprocess_params, forward_params, postprocess_params





							def __call__(		self		:Optional[Any]						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







								return super().__call__(*__magic_name__						, **__magic_name__							)

