






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


SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    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)

SCREAMING_SNAKE_CASE__      :			int							=		logging.getLogger()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				__magic_name__		:Tuple        =      parser.parse_args()
				return args.f

def      __lowercase					( snake_case,  snake_case="eval" ):







				"""simple docstring"""






				__magic_name__		:str        =      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}''' )


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_glue.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_clm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					1_0_0     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[str]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_summarization_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase       ,					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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_mlm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					4_2     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[Any]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_ta_mlm_flax.main()
												__magic_name__		:Dict        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.42     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__magic_name__		:int        =      7 if get_gpu_count() > 1 else 2

								__magic_name__		:str        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:str        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_ner.main()
												__magic_name__		:Any        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					0.3     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:int        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_qa.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					3_0     )
												self.assertGreaterEqual(result['''eval_exact''']       ,					3_0     )














import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      argparse.ArgumentParser()
				parser.add_argument(
				    '''-m''',  '''--pretrained_model_name_or_path''',  type=snake_case,  default=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  )
				parser.add_argument(
				    '''-c''',  '''--caption''',  type=snake_case,  default='''robotic cat with wings''',  help='''Text used to generate images.''',  )
				parser.add_argument(
				    '''-n''',  '''--images_num''',  type=snake_case,  default=4,  help='''How much images to generate.''',  )
				parser.add_argument(
				    '''-s''',  '''--seed''',  type=snake_case,  default=4_2,  help='''Seed for random process.''',  )
				parser.add_argument(
				    '''-ci''',  '''--cuda_id''',  type=snake_case,  default=0,  help='''cuda_id.''',  )
				__magic_name__		:Optional[Any]        =      parser.parse_args()
				return args

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not len(snake_case ) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.''' )

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

				for i, img in enumerate(snake_case ):
								grid.paste(snake_case,  box=(i % cols * w, i // cols * h) )
				return grid

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      torch.Generator(pipeline.device ).manual_seed(snake_case )
				__magic_name__		:str        =      pipeline(
				    snake_case,  guidance_scale=snake_case,  num_inference_steps=snake_case,  generator=snake_case,  num_images_per_prompt=snake_case,  ).images
				__magic_name__		:Tuple        =      int(math.sqrt(snake_case ) )
				__magic_name__		:Union[str, Any]        =      image_grid(snake_case,  rows=_rows,  cols=num_images_per_prompt // _rows )
				return grid, images


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
SCREAMING_SNAKE_CASE__      :			int							=		os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))






