






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
	import jax.numpy as jnp

if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :str         =   {
					    "task_specific_params": {
					        "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
					        "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
					        "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
					    }
					}
					snake_case__      :str         =   {
					    "task_specific_params.summarization.length_penalty": 1.0,
					    "task_specific_params.summarization.max_length": 128,
					    "task_specific_params.summarization.min_length": 12,
					    "task_specific_params.summarization.num_beams": 4,
					    "task_specific_params.summarization_cnn.length_penalty": 2.0,
					    "task_specific_params.summarization_cnn.max_length": 142,
					    "task_specific_params.summarization_cnn.min_length": 56,
					    "task_specific_params.summarization_cnn.num_beams": 4,
					    "task_specific_params.summarization_xsum.length_penalty": 1.0,
					    "task_specific_params.summarization_xsum.max_length": 62,
					    "task_specific_params.summarization_xsum.min_length": 11,
					    "task_specific_params.summarization_xsum.num_beams": 6,
					}

					self.assertEqual(flatten_dict(UpperCamelCase       )					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   np.random.randn(3					,4       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase       )					,x.transpose()       )       )

					snake_case__      :List[Any]         =   np.random.randn(3					,4					,5       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase					,axes=(1, 2, 0)       )					,x.transpose((1, 2, 0)       )       )       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :List[Any]         =   np.random.randn(3					,4       )
					snake_case__      :Dict         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase       )					,transpose(UpperCamelCase       ).numpy()       )       )

					snake_case__      :Union[str, Any]         =   np.random.randn(3					,4					,5       )
					snake_case__      :Tuple         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase					,axes=(1, 2, 0)       )					,transpose(UpperCamelCase					,axes=(1, 2, 0)       ).numpy()       )       )






	@require_tf
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Any         =   np.random.randn(3					,4       )
					snake_case__      :str         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase       )					,transpose(UpperCamelCase       ).numpy()       )       )

					snake_case__      :Optional[int]         =   np.random.randn(3					,4					,5       )
					snake_case__      :List[Any]         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase					,axes=(1, 2, 0)       )					,transpose(UpperCamelCase					,axes=(1, 2, 0)       ).numpy()       )       )






	@require_flax
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Optional[int]         =   np.random.randn(3					,4       )
					snake_case__      :Dict         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase       )					,np.asarray(transpose(UpperCamelCase       )       )       )       )

					snake_case__      :List[str]         =   np.random.randn(3					,4					,5       )
					snake_case__      :Optional[Any]         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(transpose(UpperCamelCase					,axes=(1, 2, 0)       )					,np.asarray(transpose(UpperCamelCase					,axes=(1, 2, 0)       )       )       )       )






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Dict         =   np.random.randn(3					,4       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(4, 3)       )					,np.reshape(UpperCamelCase					,(4, 3)       )       )       )

					snake_case__      :Optional[Any]         =   np.random.randn(3					,4					,5       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(12, 5)       )					,np.reshape(UpperCamelCase					,(12, 5)       )       )       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :List[str]         =   np.random.randn(3					,4       )
					snake_case__      :Optional[Any]         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(4, 3)       )					,reshape(UpperCamelCase					,(4, 3)       ).numpy()       )       )

					snake_case__      :Union[str, Any]         =   np.random.randn(3					,4					,5       )
					snake_case__      :Tuple         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(12, 5)       )					,reshape(UpperCamelCase					,(12, 5)       ).numpy()       )       )






	@require_tf
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   np.random.randn(3					,4       )
					snake_case__      :Optional[Any]         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(4, 3)       )					,reshape(UpperCamelCase					,(4, 3)       ).numpy()       )       )

					snake_case__      :int         =   np.random.randn(3					,4					,5       )
					snake_case__      :Any         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(12, 5)       )					,reshape(UpperCamelCase					,(12, 5)       ).numpy()       )       )






	@require_flax
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :int         =   np.random.randn(3					,4       )
					snake_case__      :List[Any]         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(4, 3)       )					,np.asarray(reshape(UpperCamelCase					,(4, 3)       )       )       )       )

					snake_case__      :Tuple         =   np.random.randn(3					,4					,5       )
					snake_case__      :Union[str, Any]         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(reshape(UpperCamelCase					,(12, 5)       )					,np.asarray(reshape(UpperCamelCase					,(12, 5)       )       )       )       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Tuple         =   np.random.randn(1					,3					,4       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase       )					,np.squeeze(UpperCamelCase       )       )       )

					snake_case__      :Any         =   np.random.randn(1					,4					,1					,5       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase					,axis=2       )					,np.squeeze(UpperCamelCase					,axis=2       )       )       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Any         =   np.random.randn(1					,3					,4       )
					snake_case__      :Dict         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase       )					,squeeze(UpperCamelCase       ).numpy()       )       )

					snake_case__      :Optional[Any]         =   np.random.randn(1					,4					,1					,5       )
					snake_case__      :Optional[Any]         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase					,axis=2       )					,squeeze(UpperCamelCase					,axis=2       ).numpy()       )       )






	@require_tf
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :int         =   np.random.randn(1					,3					,4       )
					snake_case__      :Dict         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase       )					,squeeze(UpperCamelCase       ).numpy()       )       )

					snake_case__      :Optional[Any]         =   np.random.randn(1					,4					,1					,5       )
					snake_case__      :Dict         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase					,axis=2       )					,squeeze(UpperCamelCase					,axis=2       ).numpy()       )       )






	@require_flax
	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :str         =   np.random.randn(1					,3					,4       )
					snake_case__      :Optional[Any]         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase       )					,np.asarray(squeeze(UpperCamelCase       )       )       )       )

					snake_case__      :Tuple         =   np.random.randn(1					,4					,1					,5       )
					snake_case__      :Optional[int]         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(squeeze(UpperCamelCase					,axis=2       )					,np.asarray(squeeze(UpperCamelCase					,axis=2       )       )       )       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :int         =   np.random.randn(3					,4       )
					self.assertTrue(np.allclose(expand_dims(UpperCamelCase					,axis=1       )					,np.expand_dims(UpperCamelCase					,axis=1       )       )       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :List[str]         =   np.random.randn(3					,4       )
					snake_case__      :Optional[int]         =   torch.tensor(UpperCamelCase       )
					self.assertTrue(np.allclose(expand_dims(UpperCamelCase					,axis=1       )					,expand_dims(UpperCamelCase					,axis=1       ).numpy()       )       )






	@require_tf
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :str         =   np.random.randn(3					,4       )
					snake_case__      :Optional[Any]         =   tf.constant(UpperCamelCase       )
					self.assertTrue(np.allclose(expand_dims(UpperCamelCase					,axis=1       )					,expand_dims(UpperCamelCase					,axis=1       ).numpy()       )       )






	@require_flax
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Optional[int]         =   np.random.randn(3					,4       )
					snake_case__      :Tuple         =   jnp.array(UpperCamelCase       )
					self.assertTrue(np.allclose(expand_dims(UpperCamelCase					,axis=1       )					,np.asarray(expand_dims(UpperCamelCase					,axis=1       )       )       )       )









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\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 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )