
import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Dict							=      tmp_path / "file.csv"
       snake_case       :   List[str]							=      textwrap.dedent(
           "\\n        header1,header2\n        1,2\n        10,20\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       snake_case       :   Optional[int]							=      tmp_path / "malformed_file.csv"
       snake_case       :   Union[str, Any]							=      textwrap.dedent(
           "\\n        header1,header2\n        1,2\n        10,20,\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Union[str, Any]    ):
       snake_case       :   Any							=      tmp_path / "csv_with_image.csv"
       snake_case       :   Union[str, Any]							=      textwrap.dedent(
           f"""\
        image
        {image_file}
        """    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Dict    ):
       snake_case       :   Dict							=      tmp_path / "csv_with_label.csv"
       snake_case       :   int							=      textwrap.dedent(
           "\\n        label\n        good\n        bad\n        good\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   str							=      tmp_path / "csv_with_int_list.csv"
       snake_case       :   Dict							=      textwrap.dedent(
           "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : str    ):
       snake_case       :   Dict							=      Csv()
       snake_case       :   str							=      csv._generate_tables([[csv_file, malformed_csv_file]]    )
       with pytest.raises(__lowerCamelCase				,					match="Error tokenizing data"    ):
              for _ in generator:
                     pass
       assert any(
           record.levelname == "ERROR"
           and "Failed to read file" in record.message
           and os.path.basename(__lowerCamelCase    ) in record.message
           for record in caplog.records    )


@require_pil
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]    ):
       with open(__lowerCamelCase				,					encoding="utf-8"    ) as f:
              snake_case       :   Optional[Any]							=      f.read().splitlines()[1]
       snake_case       :   Union[str, Any]							=      Csv(encoding="utf-8"				,					features=Features({"image": Image()}    )    )
       snake_case       :   Optional[Any]							=      csv._generate_tables([[csv_file_with_image]]    )
       snake_case       :   str							=      pa.concat_tables([table for _, table in generator]    )
       assert pa_table.schema.field("image"    ).type == Image()()
       snake_case       :   Union[str, Any]							=      pa_table.to_pydict()["image"]
       assert generated_content == [{"path": image_file, "bytes": None}]


def 			UpperCamelCase					(    __lowerCamelCase     : Any    ):
       with open(__lowerCamelCase				,					encoding="utf-8"    ) as f:
              snake_case       :   List[str]							=      f.read().splitlines()[1:]
       snake_case       :   str							=      Csv(encoding="utf-8"				,					features=Features({"label": ClassLabel(names=["good", "bad"]    )}    )    )
       snake_case       :   Dict							=      csv._generate_tables([[csv_file_with_label]]    )
       snake_case       :   Union[str, Any]							=      pa.concat_tables([table for _, table in generator]    )
       assert pa_table.schema.field("label"    ).type == ClassLabel(names=["good", "bad"]    )()
       snake_case       :   Union[str, Any]							=      pa_table.to_pydict()["label"]
       assert generated_content == [ClassLabel(names=["good", "bad"]    ).straint(__lowerCamelCase    ) for label in labels]




def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Union[str, Any]							=      Csv(encoding="utf-8"				,					sep=","				,					converters={"int_list": lambda __lowerCamelCase    : [int(__lowerCamelCase    ) for i in x.split()]}    )
       snake_case       :   Any							=      csv._generate_tables([[csv_file_with_int_list]]    )
       snake_case       :   Optional[Any]							=      pa.concat_tables([table for _, table in generator]    )
       assert pa.types.is_list(pa_table.schema.field("int_list"    ).type    )
       snake_case       :   List[Any]							=      pa_table.to_pydict()["int_list"]
       assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]









import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A      :    Union[str, Any]		=					{"LayoutLMv2Config", "LayoutLMv3Config"}







@is_pipeline_test
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       A__											=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              A__											=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              A__											=			{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"       )

             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier(["This is great !", "This is bad"]					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"					,      top_k=1       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             # Legacy behavior
             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				str    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]       )

             lowerCamelCase__		:				Optional[Any]    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     {"label": "LABEL_0", "score": 0.5_0_4},
                     {"label": "LABEL_0", "score": 0.5_0_4},
                 ]					,      )





       @require_torch
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             import torch

             lowerCamelCase__		:				int    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"					,      device=torch.device("cpu"       )					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @require_tf
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="tf"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @slow
       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  pipeline("text-classification"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Tuple    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       @slow
       @require_tf
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  pipeline("text-classification"					,      framework="tf"       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[Any]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Dict    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  TextClassificationPipeline(model=__lowerCamelCase					,      tokenizer=__lowerCamelCase       )
             return text_classifier, ["HuggingFace is in", "This is another test"]





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowerCamelCase__		:				List[Any]    =  "HuggingFace is in"
             lowerCamelCase__		:				Tuple    =  text_classifier(__lowerCamelCase       )

             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]       )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

             lowerCamelCase__		:				Optional[int]    =  ["HuggingFace is in ", "Paris is in France"]
             lowerCamelCase__		:				Dict    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}, {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
             self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase					,      top_k=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  len(model.config.idalabel.values()       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N, [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N]					,      )

             lowerCamelCase__		:				Optional[int]    =  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}					,      )
             self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             lowerCamelCase__		:				Any    =  [["HuggingFace is in ", "Paris is in France"]]
             with self.assertRaises(__lowerCamelCase       ):
                   text_classifier(__lowerCamelCase       )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             lowerCamelCase__		:				int    =  text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )


