

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     IFPipeline
			_UpperCAmelCase   :							Tuple										=     TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
			_UpperCAmelCase   :							Any										=     TEXT_TO_IMAGE_BATCH_PARAMS
			_UpperCAmelCase   :							Union[str, Any]										=     PipelineTesterMixin.required_optional_params - {'''latents'''}



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self._get_dummy_components()



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: int  		=       torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}

									return inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != "cuda"   ,						reason="float16 requires CUDA")
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1E-1)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									self._test_save_load_local()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2   ,						)




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,						reason="XFormers attention is only available with CUDA and `xformers` installed"   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







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




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									# if

									SCREAMING_SNAKE_CASE_: str  		=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,						variant="fp16"   ,						torch_dtype=torch.floataa)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFSuperResolutionPipeline.from_pretrained(
									    "DeepFloyd/IF-II-L-v1.0"   ,						variant="fp16"   ,						torch_dtype=torch.floataa   ,						text_encoder=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to("cuda")

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       pipe_a.encode_prompt("anime turtle"   ,						device="cuda")

									del pipe_a.tokenizer
									del pipe_a.text_encoder
									gc.collect()

									SCREAMING_SNAKE_CASE_: Any  		=       None
									SCREAMING_SNAKE_CASE_: int  		=       None

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									SCREAMING_SNAKE_CASE_: Any  		=       IFImgaImgPipeline(**pipe_a.components)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFImgaImgSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_imgaimg(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFInpaintingPipeline(**pipe_a.components)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       IFInpaintingSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_inpainting(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       output.images[0]

									assert image.shape == (64, 64, 3)

									SCREAMING_SNAKE_CASE_: Any  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									SCREAMING_SNAKE_CASE_: Any  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: Dict  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int]):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

									assert image.shape == (64, 64, 3)

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									SCREAMING_SNAKE_CASE_: int  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						original_image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Any  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: Dict  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: Any  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(1)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						mask_image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       output.images[0]

									assert image.shape == (64, 64, 3)

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(1)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						mask_image=lowerCAmelCase__   ,						original_image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: List[Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)







def 			A_      (    ):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()









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

						lowerCAmelCase							:  Any   	=						"""0.12"""  # assumed parallelism: 8


if is_torch_available():
						import torch







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       random.Random()

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						for dim in shape:
												total_dims *= dim

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												values.append(rng.randint(0				,	vocab_size - 1    )    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.array(_UpperCAmelCase				,	dtype=jnp.intaa    ).reshape(_UpperCAmelCase    )

						return output







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       ids_tensor(_UpperCAmelCase				,	vocab_size=2				,	rng=_UpperCAmelCase    )
						# make sure that at least one token is attended to for each batch
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						return attn_mask



@require_flax
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     None
			_UpperCAmelCase   :							List[Any]										=     ()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

									# generate max 5 tokens
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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()`
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.eos_token_id
									return config, input_ids, attention_mask, max_length



			@is_pt_flax_cross_test
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       False
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_model.generate(lowerCAmelCase__).sequences
															SCREAMING_SNAKE_CASE_: str  		=       pt_model.generate(torch.tensor(lowerCAmelCase__   ,						dtype=torch.long))

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

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

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

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

															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[str]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Dict  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: int  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: Any  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: str  		=       False
									SCREAMING_SNAKE_CASE_: int  		=       max_length
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Tuple  		=       True
									SCREAMING_SNAKE_CASE_: List[str]  		=       max_length
									SCREAMING_SNAKE_CASE_: Any  		=       0.8
									SCREAMING_SNAKE_CASE_: Any  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       0.3
									SCREAMING_SNAKE_CASE_: Tuple  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: int  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: Dict  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: List[str]  		=       2
									SCREAMING_SNAKE_CASE_: str  		=       1
									SCREAMING_SNAKE_CASE_: Tuple  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: int  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Dict  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Any  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_input_ids_and_config()

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

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Any  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Hello world"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer(lowerCAmelCase__   ,						return_tensors="np").input_ids

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

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(lowerCAmelCase__   ,						"foo"):
															SCREAMING_SNAKE_CASE_: str  		=       {"foo": "bar"}
															model.generate(lowerCAmelCase__   ,						**lowerCAmelCase__)






