





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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

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




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=argparse.ArgumentParser()
   parser.add_argument("-f"  )
   UpperCAmelCase_											=parser.parse_args()
   return args.f





class      A       (	__lowercase						):



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=logging.StreamHandler(sys.stdout       )
         logger.addHandler(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						List[str]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=get_gpu_count()

         if n_gpu > 1:
            pass
            # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
            # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
            # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
            # cmd = [sys.executable] + distributed_args + args
            # execute_subprocess_async(cmd, env=self.get_env())
            # XXX: test the results - need to save them first into .json file
         else:
            args.insert(0		,  "run_glue_deebert.py"       )
            with patch.object(_lowerCAmelCase		,  "argv"		,  _lowerCAmelCase       ):
               UpperCAmelCase_											=run_glue_deebert.main()
               for value in result.values():
                  self.assertGreaterEqual(_lowerCAmelCase		,  0.6_66       )






      @slow
      @require_torch_non_multi_gpu
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											="\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
         self.run_and_check(_lowerCAmelCase       )

         UpperCAmelCase_											="\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
         self.run_and_check(_lowerCAmelCase       )

         UpperCAmelCase_											="\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
         self.run_and_check(_lowerCAmelCase       )











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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            UpperCAmelCase_											=FlaxDiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe"		,  safety_checker=_lowerCAmelCase		,  cache_dir=_lowerCAmelCase       )

            UpperCAmelCase_											=[t[-1] for t in os.walk(os.path.join(_lowerCAmelCase		,  os.listdir(_lowerCAmelCase       )[0]		,  "snapshots"       )       )]
            UpperCAmelCase_											=[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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=4

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images

         assert images.shape == (num_samples, 1, 64, 64, 3)
         if jax.device_count() == 8:
            assert np.abs(np.abs(images[0, 0, :2, :2, -2:]		,  dtype=np.floataa       ).sum() - 4.1_51_47_45       ) < 1e-3
            assert np.abs(np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 4_99_47.8_75       ) < 5e-1

         UpperCAmelCase_											=pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]       )       )       )
         assert len(_lowerCAmelCase       ) == num_samples



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="flax"		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images

         assert images.shape == (num_samples, 1, 512, 512, 3)
         if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,  dtype=np.floataa       ).sum() - 0.05_65_24_01)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_38_38_08.2)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images

         assert images.shape == (num_samples, 1, 512, 512, 3)
         if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,  dtype=np.floataa       ).sum() - 0.04_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images

         assert images.shape == (num_samples, 1, 512, 512, 3)
         if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,  dtype=np.floataa       ).sum() - 0.04_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxDDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  set_alpha_to_one=_lowerCAmelCase		,  steps_offset=1		,  )

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  scheduler=_lowerCAmelCase		,  safety_checker=_lowerCAmelCase		,  )
         UpperCAmelCase_											=scheduler.create_state()

         UpperCAmelCase_											=scheduler_state

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images

         assert images.shape == (num_samples, 1, 512, 512, 3)
         if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,  dtype=np.floataa       ).sum() - 0.0_45_04_39_45)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_34_76_93.5)       ) < 5e-1






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=jax.random.split(jax.random.PRNGKey(0       )		,  _lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase		,  )

         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images
         assert images.shape == (num_samples, 1, 512, 512, 3)
         UpperCAmelCase_											=images[2, 0, 256, 10:17, 1]

         # With memory efficient attention
         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase		,  use_memory_efficient_attention=_lowerCAmelCase		,  )

         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images
         assert images_eff.shape == (num_samples, 1, 512, 512, 3)
         UpperCAmelCase_											=images[2, 0, 256, 10:17, 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




