


import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


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

			from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
						def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
													with tempfile.TemporaryDirectory() as tmpdirname:
																				# pipeline has Flax weights
																				a_ :		Optional[Any]							=  FlaxDiffusionPipeline.from_pretrained(
																				    'hf-internal-testing/tiny-stable-diffusion-pipe'					,							safety_checker=SCREAMING_SNAKE_CASE__					,							cache_dir=SCREAMING_SNAKE_CASE__					)

																				a_ :		Optional[Any]							=  [t[-1] for t in os.walk(os.path.join(SCREAMING_SNAKE_CASE__					,							os.listdir(SCREAMING_SNAKE_CASE__					)[0]					,							'snapshots'					)					)]
																				a_ :		int							=  [item for sublist in all_root_files for item in sublist]

																				# None of the downloaded files should be a PyTorch file even if we have some here:
																				# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
																				assert not any(f.endswith('.bin'					) for f in files					)
@slow
@require_flax
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
						def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
													a_ :		Optional[int]							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-pipe'					,							safety_checker=SCREAMING_SNAKE_CASE__					)

													a_ :		int							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		Any							=  jax.random.PRNGKey(0					)
													a_ :		Union[str, Any]							=  4

													a_ :		Optional[Any]							=  jax.device_count()
													a_ :		List[Any]							=  num_samples * [prompt]
													a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

													# shard inputs and rng
													a_ :		List[str]							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		Tuple							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		List[Any]							=  shard(SCREAMING_SNAKE_CASE__					)

													a_ :		int							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

													assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
													if jax.device_count() == 8:
																				assert np.abs(np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa					).sum() - 4.1514745					) < 1E-3
																				assert np.abs(np.abs(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 49947.875					) < 5E-1

													a_ :		Optional[Any]							=  pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]					)					)					)
													assert len(SCREAMING_SNAKE_CASE__					) == num_samples
						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Union[str, Any]:
													a_ :		Optional[int]							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='flax'					,							safety_checker=SCREAMING_SNAKE_CASE__					)

													a_ :		int							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		List[Any]							=  jax.random.PRNGKey(0					)
													a_ :		str							=  5_0

													a_ :		int							=  jax.device_count()
													a_ :		Tuple							=  num_samples * [prompt]
													a_ :		Any							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

													# shard inputs and rng
													a_ :		int							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		int							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		Tuple							=  shard(SCREAMING_SNAKE_CASE__					)

													a_ :		Tuple							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

													assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													if jax.device_count() == 8:
																				assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa					).sum() - 0.05652401)					) < 1E-3
																				assert np.abs((np.abs(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2383808.2)					) < 5E-1
						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Tuple:
													a_ :		Tuple							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=SCREAMING_SNAKE_CASE__					)

													a_ :		List[Any]							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		Optional[Any]							=  jax.random.PRNGKey(0					)
													a_ :		List[str]							=  5_0

													a_ :		Union[str, Any]							=  jax.device_count()
													a_ :		Tuple							=  num_samples * [prompt]
													a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

													# shard inputs and rng
													a_ :		Any							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		Optional[Any]							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		str							=  shard(SCREAMING_SNAKE_CASE__					)

													a_ :		List[Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

													assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													if jax.device_count() == 8:
																				assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa					).sum() - 0.04003906)					) < 1E-3
																				assert np.abs((np.abs(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1
						def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      int:
													a_ :		List[str]							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					)

													a_ :		Optional[Any]							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		Optional[int]							=  jax.random.PRNGKey(0					)
													a_ :		List[str]							=  5_0

													a_ :		Union[str, Any]							=  jax.device_count()
													a_ :		str							=  num_samples * [prompt]
													a_ :		Dict							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

													# shard inputs and rng
													a_ :		Tuple							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		List[str]							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)

													a_ :		Optional[int]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

													assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													if jax.device_count() == 8:
																				assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa					).sum() - 0.04003906)					) < 1E-3
																				assert np.abs((np.abs(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1
						def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
													a_ :		Union[str, Any]							=  FlaxDDIMScheduler(
													    beta_start=0.00085					,							beta_end=0.012					,							beta_schedule='scaled_linear'					,							set_alpha_to_one=SCREAMING_SNAKE_CASE__					,							steps_offset=1					,							)

													a_ :		int							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							scheduler=SCREAMING_SNAKE_CASE__					,							safety_checker=SCREAMING_SNAKE_CASE__					,							)
													a_ :		Union[str, Any]							=  scheduler.create_state()

													a_ :		str							=  scheduler_state

													a_ :		Optional[int]							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		str							=  jax.random.PRNGKey(0					)
													a_ :		List[Any]							=  5_0

													a_ :		List[str]							=  jax.device_count()
													a_ :		Dict							=  num_samples * [prompt]
													a_ :		Tuple							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

													# shard inputs and rng
													a_ :		List[Any]							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		Union[str, Any]							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)

													a_ :		Optional[Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

													assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													if jax.device_count() == 8:
																				assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa					).sum() - 0.045043945)					) < 1E-3
																				assert np.abs((np.abs(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2347693.5)					) < 5E-1

						def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
													a_ :		Optional[int]							=  (
													    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
													    ' field, close up, split lighting, cinematic'
													)

													a_ :		Dict							=  jax.device_count()
													a_ :		Optional[int]							=  num_samples * [prompt]
													a_ :		str							=  jax.random.split(jax.random.PRNGKey(0					)					,							SCREAMING_SNAKE_CASE__					)

													a_ :		List[str]							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=SCREAMING_SNAKE_CASE__					,							)

													a_ :		Dict							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		str							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)
													a_ :		int							=  shard(SCREAMING_SNAKE_CASE__					)
													a_ :		str							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images
													assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													a_ :		Optional[int]							=  images[2, 0, 2_5_6, 1_0:1_7, 1]

													# With memory efficient attention
													a_ :		Tuple							=  FlaxStableDiffusionPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=SCREAMING_SNAKE_CASE__					,							use_memory_efficient_attention=SCREAMING_SNAKE_CASE__					,							)

													a_ :		Any							=  replicate(SCREAMING_SNAKE_CASE__					)
													a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)
													a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)
													a_ :		Union[str, Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images
													assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
													a_ :		str							=  images[2, 0, 2_5_6, 1_0:1_7, 1]

													# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
													# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
													assert abs(slice_eff - slice					).max() < 1E-2






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_				:			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)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

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


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


