



'''simple docstring'''





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase_	 =						"bert-base-cased"
lowercase_	 =						"google/pegasus-xsum"
lowercase_	 =						[" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase_	 =						["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase_	 =						"patrickvonplaten/t5-tiny-random"
lowercase_	 =						"sshleifer/bart-tiny-random"
lowercase_	 =						"sshleifer/tiny-mbart"
lowercase_	 =						"sshleifer/tiny-marian-en-de"




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						'''\n'''.join(__A)
     Path(__A).open('''w''').writelines(__A)




def lowerCAmelCase						(__A):



     """simple docstring"""



     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__A						,			F'''{split}.source''')						,			__A)
          _dump_articles(os.path.join(__A						,			F'''{split}.target''')						,			__A)
     return tmp_dir






class __A    (						A		):



 '''simple docstring'''







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,     )
 @slow
 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						AutoTokenizer.from_pretrained(A )
      _a											=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
      _a											=						max(len(tokenizer.encode(A ) ) for a in ARTICLES )
      _a											=						max(len(tokenizer.encode(A ) ) for a in SUMMARIES )
      _a											=						4
      _a											=						8
      assert max_len_target > max_src_len  # Will be truncated
      assert max_len_source > max_src_len  # Will be truncated
      _a						,					_a											=						'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
      _a											=						SeqaSeqDataset(
          A				,     data_dir=A				,     type_path='''train'''				,     max_source_length=A				,     max_target_length=A				,     src_lang=A				,     tgt_lang=A				,     )
      _a											=						DataLoader(A				,     batch_size=2				,     collate_fn=train_dataset.collate_fn )
      for batch in dataloader:
           assert isinstance(A				,     A )
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_src_len
           # show that targets are the same len
           assert batch["labels"].shape[1] == max_tgt_len
           if tok_name != MBART_TINY:
                continue
           # check language codes in correct place
           _a											=						shift_tokens_right(batch['''labels''']				,     tokenizer.pad_token_id )
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch



 @parameterized.expand([BART_TINY, BERT_BASE_CASED] )
 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						AutoTokenizer.from_pretrained(A )
      _a											=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
      _a											=						max(len(tokenizer.encode(A ) ) for a in ARTICLES )
      _a											=						max(len(tokenizer.encode(A ) ) for a in SUMMARIES )
      _a											=						4
      _a											=						LegacySeqaSeqDataset(
          A				,     data_dir=A				,     type_path='''train'''				,     max_source_length=20				,     max_target_length=A				,     )
      _a											=						DataLoader(A				,     batch_size=2				,     collate_fn=train_dataset.collate_fn )
      for batch in dataloader:
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_len_source
           assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
           # show that targets were truncated
           assert batch["labels"].shape[1] == trunc_target  # Truncated
           assert max_len_target > trunc_target  # Truncated
           break  # No need to test every batch



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )

      _a											=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
      _a											=						tmp_dir.joinpath('''train.source''' ).open().readlines()
      _a											=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
      pack_data_dir(A				,     A				,     128				,     A )
      _a											=						{x.name for x in tmp_dir.iterdir()}
      _a											=						{x.name for x in save_dir.iterdir()}
      _a											=						save_dir.joinpath('''train.source''' ).open().readlines()
      # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
      # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
      assert len(A ) < len(A )
      assert len(A ) == 1
      assert len(packed_examples[0] ) == sum(len(A ) for x in orig_examples )
      assert orig_paths == new_paths



 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,     reason='''This test requires fairseq''' )
 def       a__						(self )					->  int:


      """simple docstring"""



      if not FAIRSEQ_AVAILABLE:
           return
      _a						,					_a						,					_a											=						self._get_dataset(max_len=64 )
      _a											=						64
      _a											=						ds.make_dynamic_sampler(A				,     required_batch_size_multiple=A )
      _a											=						[len(A ) for x in batch_sampler]
      assert len(set(A ) ) > 1  # it's not dynamic batch size if every batch is the same length
      assert sum(A ) == len(A )  # no dropped or added examples
      _a											=						DataLoader(A				,     batch_sampler=A				,     collate_fn=ds.collate_fn				,     num_workers=2 )
      _a											=						[]
      _a											=						[]
      for batch in data_loader:
           _a											=						batch['''input_ids'''].shape
           _a											=						src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           _a											=						np.product(batch['''input_ids'''].shape )
           num_src_per_batch.append(A )
           if num_src_tokens > (max_tokens * 1.1):
                failures.append(A )
      assert num_src_per_batch[0] == max(A )
      if failures:
           raise AssertionError(f'''too many tokens in {len(A )} batches''' )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a						,					_a											=						self._get_dataset(max_len=512 )
      _a											=						2
      _a											=						ds.make_sortish_sampler(A				,     shuffle=A )

      _a											=						DataLoader(A				,     batch_size=A				,     collate_fn=ds.collate_fn				,     num_workers=2 )
      _a											=						DataLoader(A				,     batch_size=A				,     collate_fn=ds.collate_fn				,     num_workers=2				,     sampler=A )

      _a											=						tokenizer.pad_token_id

      def count_pad_tokens(A				,     A="input_ids" ):
           return [batch[k].eq(A ).sum().item() for batch in data_loader]

      assert sum(count_pad_tokens(A				,     k='''labels''' ) ) < sum(count_pad_tokens(A				,     k='''labels''' ) )
      assert sum(count_pad_tokens(A ) ) < sum(count_pad_tokens(A ) )
      assert len(A ) == len(A )



 def       a__						(self				,     A=1_000				,     A=128 )					->  int:


      """simple docstring"""



      if os.getenv('''USE_REAL_DATA'''				,     A ):
           _a											=						'''examples/seq2seq/wmt_en_ro'''
           _a											=						max_len * 2 * 64
           if not Path(A ).joinpath('''train.len''' ).exists():
                save_len_file(A				,     A )
      else:
           _a											=						'''examples/seq2seq/test_data/wmt_en_ro'''
           _a											=						max_len * 4
           save_len_file(A				,     A )

      _a											=						AutoTokenizer.from_pretrained(A )
      _a											=						SeqaSeqDataset(
          A				,     data_dir=A				,     type_path='''train'''				,     max_source_length=A				,     max_target_length=A				,     n_obs=A				,     )
      return ds, max_tokens, tokenizer



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a						,					_a						,					_a											=						self._get_dataset()
      _a											=						set(DistributedSortishSampler(A				,     256				,     num_replicas=2				,     rank=0				,     add_extra_examples=A ) )
      _a											=						set(DistributedSortishSampler(A				,     256				,     num_replicas=2				,     rank=1				,     add_extra_examples=A ) )
      assert idsa.intersection(A ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,     )
 def       a__						(self				,     A )					->  Tuple:


      """simple docstring"""



      _a											=						AutoTokenizer.from_pretrained(A				,     use_fast=A )
      if tok_name == MBART_TINY:
           _a											=						SeqaSeqDataset(
               A				,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )				,     type_path='''train'''				,     max_source_length=4				,     max_target_length=8				,     src_lang='''EN'''				,     tgt_lang='''FR'''				,     )
           _a											=						train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
           _a											=						SeqaSeqDataset(
               A				,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )				,     type_path='''train'''				,     max_source_length=4				,     max_target_length=8				,     )
           _a											=						train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(A ) == 1 if tok_name == BART_TINY else len(A ) == 0











'''simple docstring'''





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



 def       a__						(self				,     A=0 )					->  str:


      """simple docstring"""



      _a											=						floats_tensor((1, 3, 128, 128)				,     rng=random.Random(A ) )
      _a											=						np.random.RandomState(A )
      _a											=						{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''strength''': 0.75,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087] )
      assert np.abs(image_slice - expected_slice ).max() < 1E-1



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=A )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      # warmup pass to apply optimizations
      _a											=						pipe(**self.get_dummy_inputs() )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						ort.SessionOptions()
      _a											=						False
      return options



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      # using the PNDM scheduler by default
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''CompVis/stable-diffusion-v1-4'''				,     revision='''onnx'''				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      _a											=						LMSDiscreteScheduler.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx''' )
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=A				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=20				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



