





'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   _lowercase						(							__lowerCamelCase		,					__lowerCamelCase		,					__lowerCamelCase		,					unittest.TestCase				):
			_UpperCamelCase       		=   StableDiffusionInstructPixaPixPipeline
			_UpperCamelCase       		=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			_UpperCamelCase       		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCamelCase       		=   IMAGE_TO_IMAGE_IMAGE_PARAMS
			_UpperCamelCase       		=   IMAGE_TO_IMAGE_IMAGE_PARAMS


			def 					snake_case				( self					):
							torch.manual_seed(0					)
							A       :       int							=						UNetaDConditionModel(
							    block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=8    ,   out_channels=4    ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,   cross_attention_dim=32    ,   )
							A       :       int							=						PNDMScheduler(skip_prk_steps=UpperCamelCase_					)
							torch.manual_seed(0					)
							A       :       str							=						AutoencoderKL(
							    block_out_channels=[32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,   latent_channels=4    ,   )
							torch.manual_seed(0					)
							A       :       Optional[Any]							=						CLIPTextConfig(
							    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_000    ,   )
							A       :       List[str]							=						CLIPTextModel(UpperCamelCase_					)
							A       :       str							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							A       :       Optional[Any]							=						{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components


			def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0					):
							A       :       Optional[Any]							=						floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
							A       :       List[str]							=						image.cpu().permute(0    ,   2    ,   3    ,   1					)[0]
							A       :       Tuple							=						Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('''RGB'''					)
							if str(UpperCamelCase_					).startswith('''mps'''					):
											A       :       List[str]							=						torch.manual_seed(UpperCamelCase_					)
							else:
											A       :       List[str]							=						torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
							A       :       Tuple							=						{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''image_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs


			def 					snake_case				( self					):
							A       :       Tuple							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							A       :       List[Any]							=						self.get_dummy_components()
							A       :       List[str]							=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_					)
							A       :       str							=						sd_pipe.to(UpperCamelCase_					)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

							A       :       Optional[int]							=						self.get_dummy_inputs(UpperCamelCase_					)
							A       :       Dict							=						sd_pipe(**UpperCamelCase_					).images
							A       :       Tuple							=						image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							A       :       Union[str, Any]							=						np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       Any							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							A       :       Optional[Any]							=						self.get_dummy_components()
							A       :       Union[str, Any]							=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_					)
							A       :       Optional[int]							=						sd_pipe.to(UpperCamelCase_					)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

							A       :       Optional[Any]							=						self.get_dummy_inputs(UpperCamelCase_					)
							A       :       Optional[Any]							=						'''french fries'''
							A       :       Optional[int]							=						sd_pipe(**UpperCamelCase_    ,   negative_prompt=UpperCamelCase_					)
							A       :       List[Any]							=						output.images
							A       :       str							=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							A       :       List[Any]							=						np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       int							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							A       :       Any							=						self.get_dummy_components()
							A       :       Optional[int]							=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_					)
							A       :       str							=						sd_pipe.to(UpperCamelCase_					)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

							A       :       Dict							=						self.get_dummy_inputs(UpperCamelCase_					)
							A       :       Dict							=						[inputs['''prompt''']] * 2

							A       :       Union[str, Any]							=						np.array(inputs['''image''']					).astype(np.floataa					) / 255.0
							A       :       Optional[Any]							=						torch.from_numpy(UpperCamelCase_					).unsqueeze(0					).to(UpperCamelCase_					)
							A       :       Any							=						image / 2 + 0.5
							A       :       Tuple							=						image.permute(0    ,   3    ,   1    ,   2					)
							A       :       int							=						image.repeat(2    ,   1    ,   1    ,   1					)

							A       :       Union[str, Any]							=						sd_pipe(**UpperCamelCase_					).images
							A       :       Tuple							=						image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							A       :       Union[str, Any]							=						np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       List[Any]							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							A       :       Optional[Any]							=						self.get_dummy_components()
							A       :       Optional[int]							=						EulerAncestralDiscreteScheduler(
							    beta_start=0.00085    ,   beta_end=0.012    ,   beta_schedule='''scaled_linear'''					)
							A       :       Optional[int]							=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_					)
							A       :       Optional[int]							=						sd_pipe.to(UpperCamelCase_					)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

							A       :       Any							=						self.get_dummy_inputs(UpperCamelCase_					)
							A       :       str							=						sd_pipe(**UpperCamelCase_					).images
							A       :       Optional[Any]							=						image[0, -3:, -3:, -1]

							A       :       Any							=						[round(UpperCamelCase_    ,   4					) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_					) for x in slice]					)					)

							assert image.shape == (1, 32, 32, 3)
							A       :       List[Any]							=						np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 					snake_case				( self					):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


			def 					snake_case				( self					):
							A       :       Union[str, Any]							=						self.get_dummy_components()
							A       :       Tuple							=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_					)
							A       :       str							=						VaeImageProcessor(do_resize=UpperCamelCase_    ,   do_normalize=UpperCamelCase_					)
							A       :       str							=						pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)

							A       :       int							=						pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_    ,   input_image_type='''pt'''					)					)[0]

							A       :       Union[str, Any]							=						components['''vae''']
							A       :       str							=						self.get_dummy_inputs_by_type(UpperCamelCase_    ,   input_image_type='''pt'''					)

							for image_param in self.image_latents_params:
											if image_param in inputs.keys():
															A       :       int							=						vae.encode(inputs[image_param]					).latent_dist.mode()

							A       :       Dict							=						pipe(**UpperCamelCase_					)[0]

							A       :       List[Any]							=						np.abs(out - out_latents_inputs					).max()
							self.assertLess(UpperCamelCase_    ,   1E-4    ,   '''passing latents as image input generate different result from passing image'''					)







@slow
@require_torch_gpu
class   _lowercase						(							unittest.TestCase				):


			def 					snake_case				( self					):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


			def 					snake_case				( self    ,   _UpperCAmelCase=0					):
							A       :       List[str]							=						torch.manual_seed(UpperCamelCase_					)
							A       :       Any							=						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''					)
							A       :       int							=						{
							    '''prompt''': '''turn him into a cyborg''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''image_guidance_scale''': 1.0,
							    '''output_type''': '''numpy''',
							}
							return inputs


			def 					snake_case				( self					):
							A       :       List[str]							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''    ,   safety_checker=UpperCamelCase_					)
							pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing()

							A       :       List[str]							=						self.get_inputs()
							A       :       int							=						pipe(**UpperCamelCase_					).images
							A       :       str							=						image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							A       :       Tuple							=						np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]					)

							assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       List[str]							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''    ,   safety_checker=UpperCamelCase_					)
							A       :       str							=						LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
							pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing()

							A       :       Optional[int]							=						self.get_inputs()
							A       :       Tuple							=						pipe(**UpperCamelCase_					).images
							A       :       Optional[Any]							=						image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							A       :       List[str]							=						np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]					)

							assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       List[Any]							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''    ,   safety_checker=UpperCamelCase_					)
							A       :       Tuple							=						DDIMScheduler.from_config(pipe.scheduler.config					)
							pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing()

							A       :       Optional[int]							=						self.get_inputs()
							A       :       Dict							=						pipe(**UpperCamelCase_					).images
							A       :       Any							=						image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							A       :       List[Any]							=						np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]					)

							assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 					snake_case				( self					):
							A       :       str							=						0

							def callback_fn(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					) -> None:
											A       :       Union[str, Any]							=						True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
															A       :       Optional[int]							=						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A       :       List[str]							=						latents[0, -3:, -3:, -1]
															A       :       List[Any]							=						np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]					)

															assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
											elif step == 2:
															A       :       Optional[int]							=						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A       :       Optional[Any]							=						latents[0, -3:, -3:, -1]
															A       :       Optional[Any]							=						np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]					)

															assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

							A       :       Tuple							=						False

							A       :       Optional[int]							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''    ,   safety_checker=UpperCamelCase_    ,   torch_dtype=torch.floataa					)
							A       :       str							=						pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing()

							A       :       Union[str, Any]							=						self.get_inputs()
							pipe(**UpperCamelCase_    ,   callback=UpperCamelCase_    ,   callback_steps=1					)
							assert callback_fn.has_been_called
							assert number_of_steps == 3


			def 					snake_case				( self					):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							A       :       Optional[Any]							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''    ,   safety_checker=UpperCamelCase_    ,   torch_dtype=torch.floataa					)
							A       :       Union[str, Any]							=						pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing(1					)
							pipe.enable_sequential_cpu_offload()

							A       :       Tuple							=						self.get_inputs()
							A       :       List[Any]							=						pipe(**UpperCamelCase_					)

							A       :       List[Any]							=						torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9


			def 					snake_case				( self					):
							A       :       Union[str, Any]							=						self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							A       :       Optional[int]							=						inputs['''image'''].resize((504, 504)					)

							A       :       List[Any]							=						'''timbrooks/instruct-pix2pix'''
							A       :       int							=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_    ,   safety_checker=UpperCamelCase_    ,   )
							pipe.to(UpperCamelCase_					)
							pipe.set_progress_bar_config(disable=UpperCamelCase_					)
							pipe.enable_attention_slicing()

							A       :       Any							=						pipe(**UpperCamelCase_					)
							A       :       Optional[int]							=						output.images[0]

							A       :       List[str]							=						image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							A       :       int							=						np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3







'''simple docstring'''






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     snake_case_     =	"""0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Any=None		)		->  List[Any]:
    if rng is None:
        A       :       int							=						random.Random()

    A       :       int							=						1
    for dim in shape:
        total_dims *= dim

    A       :       Union[str, Any]							=						[]
    for _ in range(UpperCamelCase__		):
        values.append(rng.randint(0    ,       vocab_size - 1		)		)

    A       :       Union[str, Any]							=						np.array(UpperCamelCase__    ,       dtype=jnp.intaa		).reshape(UpperCamelCase__		)

    return output
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Optional[Any]=None		)		->  Dict:
    A       :       Union[str, Any]							=						ids_tensor(UpperCamelCase__    ,       vocab_size=2    ,       rng=UpperCamelCase__		)
    # make sure that at least one token is attended to for each batch
    A       :       Any							=						1
    return attn_mask


@require_flax
class   _lowercase						:
   _UpperCamelCase       		=   None
   _UpperCamelCase       		=   ()


   def 					snake_case				( self					):
       A,	A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       A       :       Dict							=						2
       A       :       List[str]							=						inputs['''input_ids'''].shape[-1] // 2
       A       :       int							=						inputs['''input_ids'''][:max_batch_size, :sequence_length]

       A       :       Optional[Any]							=						jnp.ones_like(_UpperCAmelCase					)
       A       :       int							=						attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       A       :       Optional[int]							=						input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           A       :       str							=						config.eos_token_id
       return config, input_ids, attention_mask, max_length


   @is_pt_flax_cross_test
   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Union[str, Any]							=						max_length
       A       :       str							=						0

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
           A       :       int							=						getattr(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       Union[str, Any]							=						pt_model_class(_UpperCAmelCase					).eval()
           A       :       Tuple							=						load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,   flax_model.params					)

           A       :       Union[str, Any]							=						flax_model.generate(_UpperCAmelCase					).sequences
           A       :       Optional[int]							=						pt_model.generate(torch.tensor(_UpperCAmelCase    ,   dtype=torch.long					)					)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               A       :       List[Any]							=						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,   flax_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       str							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Dict							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       List[str]							=						True
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Tuple							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Tuple							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[str]							=						self._get_input_ids_and_config()
       A       :       Any							=						False
       A       :       str							=						max_length
       A       :       Optional[int]							=						2

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       List[Any]							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[int]							=						self._get_input_ids_and_config()
       A       :       Dict							=						False
       A       :       List[Any]							=						max_length
       A       :       Optional[int]							=						2
       A       :       int							=						2

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       List[str]							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[0]    ,   input_ids.shape[0] * config.num_return_sequences					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       Any							=						True
       A       :       Any							=						max_length
       A       :       Any							=						0.8
       A       :       List[Any]							=						10
       A       :       List[Any]							=						0.3
       A       :       Union[str, Any]							=						1
       A       :       Any							=						8
       A       :       List[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       List[Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       int							=						max_length
       A       :       Dict							=						1
       A       :       Tuple							=						8
       A       :       Optional[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       str							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       Optional[int]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       int							=						self._get_input_ids_and_config()
       A       :       List[Any]							=						max_length
       A       :       Optional[Any]							=						2
       A       :       Tuple							=						1
       A       :       List[Any]							=						8
       A       :       Dict							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Union[str, Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Union[str, Any]							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Dict							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Union[str, Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       int							=						False
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Dict							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Optional[int]							=						True
       A       :       int							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Union[str, Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Optional[Any]							=						jit(model.generate					)
           A       :       Dict							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[int]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Any							=						2
       A       :       Any							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Optional[Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[str]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)







@require_flax
class   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       A       :       List[Any]							=						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''					)
       A       :       Dict							=						FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''					)

       A       :       Union[str, Any]							=						'''Hello world'''
       A       :       Union[str, Any]							=						tokenizer(_UpperCAmelCase    ,   return_tensors='''np'''					).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(_UpperCAmelCase    ,   '''do_samples'''					):
           model.generate(_UpperCAmelCase    ,   do_samples=_UpperCAmelCase					)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(_UpperCAmelCase    ,   '''foo'''					):
           A       :       List[Any]							=						{'''foo''': '''bar'''}
           model.generate(_UpperCAmelCase    ,   **_UpperCAmelCase					)
