





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
      import optax
      from flax.training.common_utils import onehot

      from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
      from transformers.models.ta.modeling_flax_ta import shift_tokens_right







@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						Optional[Any]   =						FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""		)
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained("""google/mt5-small"""		)

         __snake_case:						Dict   =						tokenizer("""Hello there"""							,				return_tensors="""np"""		).input_ids
         __snake_case:						str   =						tokenizer("""Hi I am"""							,				return_tensors="""np"""		).input_ids

         __snake_case:						int   =						shift_tokens_right(A							,				model.config.pad_token_id							,				model.config.decoder_start_token_id		)

         __snake_case:						str   =						model(A							,				decoder_input_ids=A		).logits
         __snake_case:						Any   =						optax.softmax_cross_entropy(A							,				onehot(A							,				logits.shape[-1]		)		).mean()

         __snake_case:						Tuple   =						-(labels.shape[-1] * loss.item())

         __snake_case:						Tuple   =						-84.9127
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)













import pytest


__UpperCAmelCase       :   Optional[Any]				       =							"__dummy_dataset1__"

__UpperCAmelCase       :   List[str]				       =							"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"







@pytest.fixture
def 			A__							(    )   ->    Optional[int]:
  return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def 			A__							(    )   ->    Tuple:
  return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						dataset_loading_script_name
  __snake_case:						Any   =						tmp_path / """datasets""" / script_name
  script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
  __snake_case:						int   =						script_dir / F'''{script_name}.py'''
  with open(SCREAMING_SNAKE_CASE__					,					"""w""") as f:
    f.write(SCREAMING_SNAKE_CASE__)
  return str(SCREAMING_SNAKE_CASE__)






