






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


__UpperCAmelCase							:   List[Any]							=  [
    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)

__UpperCAmelCase							:   Tuple							=  logging.getLogger()





def 					lowercase_ (    )     -> Dict:





    '''simple docstring'''


    snake_case__      :List[Any]         =   argparse.ArgumentParser()
    parser.add_argument("-f"      )
    snake_case__      :Tuple         =   parser.parse_args()
    return args.f





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any="eval"      )     -> Union[str, Any]:





    '''simple docstring'''


    snake_case__      :Any         =   os.path.join(__snake_case	,	F'{split}_results.json'      )
    if os.path.exists(__snake_case      ):
        with open(__snake_case	,	"r"      ) as f:
            return json.load(__snake_case      )
    raise ValueError(F'can\'t find {path}'      )


__UpperCAmelCase							:   Any							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 					_snake_case						(			_A ):






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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_flax_glue.main()
         snake_case__      :Optional[int]         =   get_results(UpperCamelCase       )
         self.assertGreaterEqual(result["eval_accuracy"]					,0.75       )






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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_clm_flax.main()
         snake_case__      :Tuple         =   get_results(UpperCamelCase       )
         self.assertLess(result["eval_perplexity"]					,100       )






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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_summarization_flax.main()
         snake_case__      :str         =   get_results(UpperCamelCase					,split="test"       )
         self.assertGreaterEqual(result["test_rouge1"]					,10       )
         self.assertGreaterEqual(result["test_rouge2"]					,2       )
         self.assertGreaterEqual(result["test_rougeL"]					,7       )
         self.assertGreaterEqual(result["test_rougeLsum"]					,7       )






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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_mlm_flax.main()
         snake_case__      :Optional[Any]         =   get_results(UpperCamelCase       )
         self.assertLess(result["eval_perplexity"]					,42       )






 @slow
 def    lowerCAmelCase_							(		self       )						->    Optional[int]:
     snake_case__      :Dict         =   self.get_auto_remove_tmp_dir()
     snake_case__      :Any         =   f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_ta_mlm_flax.main()
         snake_case__      :Union[str, Any]         =   get_results(UpperCamelCase       )
         self.assertGreaterEqual(result["eval_accuracy"]					,0.42       )






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

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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_flax_ner.main()
         snake_case__      :List[str]         =   get_results(UpperCamelCase       )
         self.assertGreaterEqual(result["eval_accuracy"]					,0.75       )
         self.assertGreaterEqual(result["eval_f1"]					,0.3       )






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

     with patch.object(UpperCamelCase					,"argv"					,UpperCamelCase       ):
         run_qa.main()
         snake_case__      :Optional[int]         =   get_results(UpperCamelCase       )
         self.assertGreaterEqual(result["eval_f1"]					,30       )
         self.assertGreaterEqual(result["eval_exact"]					,30       )







import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard


@slow
@require_flax
class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    int:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__    ,						snake_case__      :Tuple         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-canny"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__    ,						snake_case__      :Any         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :List[str]         =   controlnet_params

					snake_case__      :Union[str, Any]         =   "bird"
					snake_case__      :Optional[int]         =   jax.device_count()
					snake_case__      :Tuple         =   pipe.prepare_text_inputs([prompts] * num_samples       )

					snake_case__      :Union[str, Any]         =   load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"       )
					snake_case__      :str         =   pipe.prepare_image_inputs([canny_image] * num_samples       )

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :str         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :int         =   replicate(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )

					snake_case__      :str         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

					snake_case__      :List[str]         =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
					snake_case__      :Any         =   images[0, 253:256, 253:256, -1]

					snake_case__      :Union[str, Any]         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[Any]         =   jnp.array(
					    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__    ,						snake_case__      :List[str]         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-openpose"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__    ,						snake_case__      :Optional[Any]         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :str         =   controlnet_params

					snake_case__      :int         =   "Chef in the kitchen"
					snake_case__      :List[Any]         =   jax.device_count()
					snake_case__      :Dict         =   pipe.prepare_text_inputs([prompts] * num_samples       )

					snake_case__      :Any         =   load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"       )
					snake_case__      :Optional[int]         =   pipe.prepare_image_inputs([pose_image] * num_samples       )

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :Any         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :Dict         =   replicate(UpperCamelCase       )
					snake_case__      :Tuple         =   shard(UpperCamelCase       )
					snake_case__      :Optional[int]         =   shard(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

					snake_case__      :int         =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
					snake_case__      :List[str]         =   images[0, 253:256, 253:256, -1]

					snake_case__      :Tuple         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[str]         =   jnp.array(
					    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2