
"""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 						a__		( a_,     a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							StableDiffusionInstructPixaPixPipeline
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							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      ,       )
          lowercase       :							List[str]									=				PNDMScheduler(skip_prk_steps=__a							)
          torch.manual_seed(0							)
          lowercase       :							Dict									=				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							)
          lowercase       :							List[Any]									=				CLIPTextConfig(
              bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_000      ,       )
          lowercase       :							List[str]									=				CLIPTextModel(__a							)
          lowercase       :							Dict									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

          lowercase       :							List[str]									=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(__a							)							).to(__a							)
          lowercase       :							int									=				image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
          lowercase       :							str									=				Image.fromarray(np.uinta(__a							)							).convert("RGB"							)
          if str(__a							).startswith("mps"							):
                 lowercase       :							List[str]									=				torch.manual_seed(__a							)
          else:
                 lowercase       :							Optional[Any]									=				torch.Generator(device=__a							).manual_seed(__a							)
          lowercase       :							Union[str, Any]									=				{
              "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      __magic_name__     (     self							):
          lowercase       :							List[str]									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Tuple									=				self.get_dummy_components()
          lowercase       :							str									=				StableDiffusionInstructPixaPixPipeline(**__a							)
          lowercase       :							List[Any]									=				sd_pipe.to(__a							)
          sd_pipe.set_progress_bar_config(disable=__a							)

          lowercase       :							List[Any]									=				self.get_dummy_inputs(__a							)
          lowercase       :							Any									=				sd_pipe(**__a							).images
          lowercase       :							Union[str, Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 32, 32, 3)
          lowercase       :							List[str]									=				np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Tuple									=				self.get_dummy_components()
          lowercase       :							Union[str, Any]									=				StableDiffusionInstructPixaPixPipeline(**__a							)
          lowercase       :							Tuple									=				sd_pipe.to(__a							)
          sd_pipe.set_progress_bar_config(disable=__a							)

          lowercase       :							List[Any]									=				self.get_dummy_inputs(__a							)
          lowercase       :							Any									=				"french fries"
          lowercase       :							Optional[Any]									=				sd_pipe(**__a      ,       negative_prompt=__a							)
          lowercase       :							Optional[int]									=				output.images
          lowercase       :							Tuple									=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							List[Any]									=				np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							List[Any]									=				self.get_dummy_components()
          lowercase       :							Tuple									=				StableDiffusionInstructPixaPixPipeline(**__a							)
          lowercase       :							Any									=				sd_pipe.to(__a							)
          sd_pipe.set_progress_bar_config(disable=__a							)

          lowercase       :							Optional[int]									=				self.get_dummy_inputs(__a							)
          lowercase       :							Dict									=				[inputs["prompt"]] * 2

          lowercase       :							Dict									=				np.array(inputs["image"]							).astype(np.floataa							) / 255.0
          lowercase       :							Dict									=				torch.from_numpy(__a							).unsqueeze(0							).to(__a							)
          lowercase       :							int									=				image / 2 + 0.5
          lowercase       :							Optional[int]									=				image.permute(0      ,       3      ,       1      ,       2							)
          lowercase       :							List[str]									=				image.repeat(2      ,       1      ,       1      ,       1							)

          lowercase       :							str									=				sd_pipe(**__a							).images
          lowercase       :							List[str]									=				image[-1, -3:, -3:, -1]

          assert image.shape == (2, 32, 32, 3)
          lowercase       :							Optional[int]									=				np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							str									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Tuple									=				self.get_dummy_components()
          lowercase       :							Tuple									=				EulerAncestralDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="scaled_linear"							)
          lowercase       :							Union[str, Any]									=				StableDiffusionInstructPixaPixPipeline(**__a							)
          lowercase       :							Any									=				sd_pipe.to(__a							)
          sd_pipe.set_progress_bar_config(disable=__a							)

          lowercase       :							List[Any]									=				self.get_dummy_inputs(__a							)
          lowercase       :							Tuple									=				sd_pipe(**__a							).images
          lowercase       :							Optional[int]									=				image[0, -3:, -3:, -1]

          lowercase       :							str									=				[round(__a      ,       4							) for x in image_slice.flatten().tolist()]
          print(",".join([str(__a							) for x in slice]							)							)

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							Union[str, Any]									=				np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)

   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.get_dummy_components()
          lowercase       :							int									=				StableDiffusionInstructPixaPixPipeline(**__a							)
          lowercase       :							Optional[int]									=				VaeImageProcessor(do_resize=__a      ,       do_normalize=__a							)
          lowercase       :							Dict									=				pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          lowercase       :							Any									=				pipe(**self.get_dummy_inputs_by_type(__a      ,       input_image_type="pt"							)							)[0]

          lowercase       :							int									=				components["vae"]
          lowercase       :							Tuple									=				self.get_dummy_inputs_by_type(__a      ,       input_image_type="pt"							)

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

          lowercase       :							Optional[int]									=				pipe(**__a							)[0]

          lowercase       :							Any									=				np.abs(out - out_latents_inputs							).max()
          self.assertLess(__a      ,       1E-4      ,       "passing latents as image input generate different result from passing image"							)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self      ,       _a=0							):
          lowercase       :							Any									=				torch.manual_seed(__a							)
          lowercase       :							Union[str, Any]									=				load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"							)
          lowercase       :							Any									=				{
              "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      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix"      ,       safety_checker=__a							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing()

          lowercase       :							Tuple									=				self.get_inputs()
          lowercase       :							Any									=				pipe(**__a							).images
          lowercase       :							List[str]									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Tuple									=				np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]							)

          assert np.abs(expected_slice - image_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix"      ,       safety_checker=__a							)
          lowercase       :							Tuple									=				LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing()

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

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Tuple									=				np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]							)

          assert np.abs(expected_slice - image_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix"      ,       safety_checker=__a							)
          lowercase       :							Any									=				DDIMScheduler.from_config(pipe.scheduler.config							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing()

          lowercase       :							Tuple									=				self.get_inputs()
          lowercase       :							str									=				pipe(**__a							).images
          lowercase       :							Union[str, Any]									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Dict									=				np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]							)

          assert np.abs(expected_slice - image_slice							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				0

          def callback_fn(_a      ,       _a      ,       _a							) -> None:
                 lowercase       :							List[str]									=				True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 1:
                        lowercase       :							Union[str, Any]									=				latents.detach().cpu().numpy()
                        assert latents.shape == (1, 4, 64, 64)
                        lowercase       :							Union[str, Any]									=				latents[0, -3:, -3:, -1]
                        lowercase       :							int									=				np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]							)

                        assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
                 elif step == 2:
                        lowercase       :							Dict									=				latents.detach().cpu().numpy()
                        assert latents.shape == (1, 4, 64, 64)
                        lowercase       :							Optional[int]									=				latents[0, -3:, -3:, -1]
                        lowercase       :							Optional[int]									=				np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]							)

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

          lowercase       :							int									=				False

          lowercase       :							Tuple									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix"      ,       safety_checker=__a      ,       torch_dtype=torch.floataa							)
          lowercase       :							Any									=				pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing()

          lowercase       :							Union[str, Any]									=				self.get_inputs()
          pipe(**__a      ,       callback=__a      ,       callback_steps=1							)
          assert callback_fn.has_been_called
          assert number_of_steps == 3
   def      __magic_name__     (     self							):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          lowercase       :							Optional[int]									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix"      ,       safety_checker=__a      ,       torch_dtype=torch.floataa							)
          lowercase       :							Any									=				pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing(1							)
          pipe.enable_sequential_cpu_offload()

          lowercase       :							List[str]									=				self.get_inputs()
          lowercase       :							Union[str, Any]									=				pipe(**__a							)

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

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.get_inputs()
          # resize to resolution that is divisible by 8 but not 16 or 32
          lowercase       :							List[Any]									=				inputs["image"].resize((504, 504)							)

          lowercase       :							Dict									=				"timbrooks/instruct-pix2pix"
          lowercase       :							Dict									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              __a      ,       safety_checker=__a      ,       )
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)
          pipe.enable_attention_slicing()

          lowercase       :							Any									=				pipe(**__a							)
          lowercase       :							Tuple									=				output.images[0]

          lowercase       :							List[Any]									=				image[255:258, 383:386, -1]

          assert image.shape == (504, 504, 3)
          lowercase       :							Optional[Any]									=				np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]							)

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



'''simple docstring'''


import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__lowerCAmelCase						:							int    							=						[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase						:							List[Any]    							=						logging.getLogger()





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      argparse.ArgumentParser()
	parser.add_argument('''-f'''							)
	__UpperCAmelCase      					=      parser.parse_args()
	return args.f





def    lowerCAmelCase       (		UpperCamelCase__					: Union[str, Any]     ,       UpperCamelCase__					: Any="eval"							):


	"""simple docstring"""
	__UpperCAmelCase      					=      os.path.join(UpperCamelCase__     ,       f"""{split}_results.json"""							)
	if os.path.exists(UpperCamelCase__							):
		with open(UpperCamelCase__     ,       '''r'''							) as f:
			return json.load(UpperCamelCase__							)
	raise ValueError(f"""can't find {path}"""							)


__lowerCAmelCase						:							str    							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				List[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_flax_glue.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)





							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_clm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		1_0_0						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_summarization_flax.main()
									__UpperCAmelCase      					=      get_results(__a  ,		split='''test'''						)
									self.assertGreaterEqual(result['''test_rouge1''']  ,		1_0						)
									self.assertGreaterEqual(result['''test_rouge2''']  ,		2						)
									self.assertGreaterEqual(result['''test_rougeL''']  ,		7						)
									self.assertGreaterEqual(result['''test_rougeLsum''']  ,		7						)





							@slow
							def    snake_case__    (   self					:				Any						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						str:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_ta_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						Tuple:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__UpperCAmelCase      					=      7 if get_gpu_count() > 1 else 2

								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_flax_ner.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		0.3						)






							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

								with patch.object(__a  ,		'''argv'''  ,		__a						):
									run_qa.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		3_0						)
									self.assertGreaterEqual(result['''eval_exact''']  ,		3_0						)
