




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


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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  Optional[Any]		       =     "\n".join(_A      )
  Path(_A      ).open('''w'''      ).writelines(_A      )



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:




  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  _lowerCAmelCase     (  lowercase__	):

      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,				)
      @slow
      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     AutoTokenizer.from_pretrained(__lowerCamelCase		)
        lowerCAmelCase__				:  str		       =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        lowerCAmelCase__				:  List[Any]		       =     max(len(tokenizer.encode(__lowerCamelCase		)		) for a in ARTICLES		)
        lowerCAmelCase__				:  List[Any]		       =     max(len(tokenizer.encode(__lowerCamelCase		)		) for a in SUMMARIES		)
        lowerCAmelCase__				:  Tuple		       =     4
        lowerCAmelCase__				:  List[str]		       =     8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        lowerCAmelCase__				:  int		       =     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
        lowerCAmelCase__				:  Optional[int]		       =     SeqaSeqDataset(
            __lowerCamelCase      ,				data_dir=__lowerCamelCase      ,				type_path='''train'''      ,				max_source_length=__lowerCamelCase      ,				max_target_length=__lowerCamelCase      ,				src_lang=__lowerCamelCase      ,				tgt_lang=__lowerCamelCase      ,				)
        lowerCAmelCase__				:  List[Any]		       =     DataLoader(__lowerCamelCase      ,				batch_size=2      ,				collate_fn=train_dataset.collate_fn		)
        for batch in dataloader:
          assert isinstance(__lowerCamelCase      ,				__lowerCamelCase		)
          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
          lowerCAmelCase__				:  List[str]		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     AutoTokenizer.from_pretrained(__lowerCamelCase		)
        lowerCAmelCase__				:  str		       =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        lowerCAmelCase__				:  Tuple		       =     max(len(tokenizer.encode(__lowerCamelCase		)		) for a in ARTICLES		)
        lowerCAmelCase__				:  Any		       =     max(len(tokenizer.encode(__lowerCamelCase		)		) for a in SUMMARIES		)
        lowerCAmelCase__				:  Optional[Any]		       =     4
        lowerCAmelCase__				:  int		       =     LegacySeqaSeqDataset(
            __lowerCamelCase      ,				data_dir=__lowerCamelCase      ,				type_path='''train'''      ,				max_source_length=20      ,				max_target_length=__lowerCamelCase      ,				)
        lowerCAmelCase__				:  Tuple		       =     DataLoader(__lowerCamelCase      ,				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 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''		)

        lowerCAmelCase__				:  Optional[Any]		       =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        lowerCAmelCase__				:  int		       =     tmp_dir.joinpath('''train.source'''		).open().readlines()
        lowerCAmelCase__				:  str		       =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        pack_data_dir(__lowerCamelCase      ,				__lowerCamelCase      ,				128      ,				__lowerCamelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     {x.name for x in tmp_dir.iterdir()}
        lowerCAmelCase__				:  List[Any]		       =     {x.name for x in save_dir.iterdir()}
        lowerCAmelCase__				:  Union[str, Any]		       =     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(__lowerCamelCase		) < len(__lowerCamelCase		)
        assert len(__lowerCamelCase		) == 1
        assert len(packed_examples[0]		) == sum(len(__lowerCamelCase		) for x in orig_examples		)
        assert orig_paths == new_paths

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



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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self._get_dataset(max_len=512		)
        lowerCAmelCase__				:  Optional[Any]		       =     2
        lowerCAmelCase__				:  Any		       =     ds.make_sortish_sampler(__lowerCamelCase      ,				shuffle=__lowerCamelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     DataLoader(__lowerCamelCase      ,				batch_size=__lowerCamelCase      ,				collate_fn=ds.collate_fn      ,				num_workers=2		)
        lowerCAmelCase__				:  List[Any]		       =     DataLoader(__lowerCamelCase      ,				batch_size=__lowerCamelCase      ,				collate_fn=ds.collate_fn      ,				num_workers=2      ,				sampler=__lowerCamelCase		)

        lowerCAmelCase__				:  Dict		       =     tokenizer.pad_token_id

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

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase=1000      ,				__UpperCAmelCase=128		):



        if os.getenv('''USE_REAL_DATA'''      ,				__lowerCamelCase		):
          lowerCAmelCase__				:  Optional[int]		       =     "examples/seq2seq/wmt_en_ro"
          lowerCAmelCase__				:  Optional[int]		       =     max_len * 2 * 64
          if not Path(__lowerCamelCase		).joinpath('''train.len'''		).exists():
            save_len_file(__lowerCamelCase      ,				__lowerCamelCase		)
        else:
          lowerCAmelCase__				:  Dict		       =     "examples/seq2seq/test_data/wmt_en_ro"
          lowerCAmelCase__				:  List[Any]		       =     max_len * 4
          save_len_file(__lowerCamelCase      ,				__lowerCamelCase		)

        lowerCAmelCase__				:  str		       =     AutoTokenizer.from_pretrained(__lowerCamelCase		)
        lowerCAmelCase__				:  int		       =     SeqaSeqDataset(
            __lowerCamelCase      ,				data_dir=__lowerCamelCase      ,				type_path='''train'''      ,				max_source_length=__lowerCamelCase      ,				max_target_length=__lowerCamelCase      ,				n_obs=__lowerCamelCase      ,				)
        return ds, max_tokens, tokenizer

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self._get_dataset()
        lowerCAmelCase__				:  str		       =     set(DistributedSortishSampler(__lowerCamelCase      ,				256      ,				num_replicas=2      ,				rank=0      ,				add_extra_examples=__lowerCamelCase		)		)
        lowerCAmelCase__				:  Tuple		       =     set(DistributedSortishSampler(__lowerCamelCase      ,				256      ,				num_replicas=2      ,				rank=1      ,				add_extra_examples=__lowerCamelCase		)		)
        assert idsa.intersection(__lowerCamelCase		) == set()







      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,				)
      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[Any]		       =     AutoTokenizer.from_pretrained(__lowerCamelCase      ,				use_fast=__lowerCamelCase		)
        if tok_name == MBART_TINY:
          lowerCAmelCase__				:  Tuple		       =     SeqaSeqDataset(
              __lowerCamelCase      ,				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'''      ,				)
          lowerCAmelCase__				:  List[Any]		       =     train_dataset.dataset_kwargs
          assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
          lowerCAmelCase__				:  List[str]		       =     SeqaSeqDataset(
              __lowerCamelCase      ,				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      ,				)
          lowerCAmelCase__				:  Optional[Any]		       =     train_dataset.dataset_kwargs
          assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
          assert len(__lowerCamelCase		) == 1 if tok_name == BART_TINY else len(__lowerCamelCase		) == 0







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

      @property
      def 	__magic_name__(   self		):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				scheduler=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3

