"""simple docstring"""






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Any=True      ,	lowerCAmelCase__  :str="pt"	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			{"""add_prefix_space""": True} if isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	) and not line.startswith(""" """	) else {}
      lowercase														=			padding_side
      return tokenizer(
          [line]      ,	max_length=lowerCAmelCase__      ,	padding="""max_length""" if pad_to_max_length else None      ,	truncation=lowerCAmelCase__      ,	return_tensors=lowerCAmelCase__      ,	add_special_tokens=lowerCAmelCase__      ,	**lowerCAmelCase__      ,	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Optional[int]=None      ,	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			input_ids.ne(lowerCAmelCase__	).any(dim=0	)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase="train"	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=""	, ):







            """simple docstring"""



            super().__init__()
            lowercase														=			Path(__lowerCAmelCase		).joinpath(type_path + """.source"""		)
            lowercase														=			Path(__lowerCAmelCase		).joinpath(type_path + """.target"""		)
            lowercase														=			self.get_char_lens(self.src_file		)
            lowercase														=			max_source_length
            lowercase														=			max_target_length
            assert min(self.src_lens		) > 0, f'found empty line in {self.src_file}'
            lowercase														=			tokenizer
            lowercase														=			prefix
            if n_obs is not None:
                  lowercase														=			self.src_lens[:n_obs]
            lowercase														=			src_lang
            lowercase														=			tgt_lang

      def __len__(		self		):







            """simple docstring"""



            return len(self.src_lens		)

      def __getitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			index + 1  # linecache starts at 1
            lowercase														=			self.prefix + linecache.getline(str(self.src_file		)	, __lowerCAmelCase		).rstrip("""\n"""		)
            lowercase														=			linecache.getline(str(self.tgt_file		)	, __lowerCAmelCase		).rstrip("""\n"""		)
            assert source_line, f'empty source line for index {index}'
            assert tgt_line, f'empty tgt line for index {index}'

            # Need to add eos token manually for T5
            if isinstance(self.tokenizer	, __lowerCAmelCase		):
                  source_line += self.tokenizer.eos_token
                  tgt_line += self.tokenizer.eos_token

            # Pad source and target to the right
            lowercase														=			(
                self.tokenizer.question_encoder if isinstance(self.tokenizer	, __lowerCAmelCase		) else self.tokenizer
            )
            lowercase														=			self.tokenizer.generator if isinstance(self.tokenizer	, __lowerCAmelCase		) else self.tokenizer

            lowercase														=			encode_line(__lowerCAmelCase	, __lowerCAmelCase	, self.max_source_length	, """right"""		)
            lowercase														=			encode_line(__lowerCAmelCase	, __lowerCAmelCase	, self.max_target_length	, """right"""		)

            lowercase														=			source_inputs["""input_ids"""].squeeze()
            lowercase														=			target_inputs["""input_ids"""].squeeze()
            lowercase														=			source_inputs["""attention_mask"""].squeeze()
            return {
                "input_ids": source_ids,
                "attention_mask": src_mask,
                "decoder_input_ids": target_ids,
            }

      @staticmethod
      def        A__ (		__lowerCAmelCase		):







            """simple docstring"""



            return [len(__lowerCAmelCase		) for x in Path(__lowerCAmelCase		).open().readlines()]







      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			torch.stack([x["""input_ids"""] for x in batch]		)
            lowercase														=			torch.stack([x["""attention_mask"""] for x in batch]		)
            lowercase														=			torch.stack([x["""decoder_input_ids"""] for x in batch]		)
            lowercase														=			(
                self.tokenizer.generator.pad_token_id
                if isinstance(self.tokenizer	, __lowerCAmelCase		)
                else self.tokenizer.pad_token_id
            )
            lowercase														=			(
                self.tokenizer.question_encoder.pad_token_id
                if isinstance(self.tokenizer	, __lowerCAmelCase		)
                else self.tokenizer.pad_token_id
            )
            lowercase														=			trim_batch(__lowerCAmelCase	, __lowerCAmelCase		)
            lowercase							,							lowercase														=			trim_batch(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            lowercase														=			{
                """input_ids""": source_ids,
                """attention_mask""": source_mask,
                """decoder_input_ids""": y,
            }
            return batch






__lowerCAmelCase					: Union[str, Any]        =getLogger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[List]	)				->					Optional[int]:
      '''simple docstring'''


      return list(itertools.chain.from_iterable(lowerCAmelCase__	)	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					None:
      '''simple docstring'''


      lowercase														=			get_git_info()
      save_json(lowerCAmelCase__      ,	os.path.join(lowerCAmelCase__      ,	"""git_log.json"""	)	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Dict=4      ,	**lowerCAmelCase__  :Optional[int]	)				->					Union[str, Any]:
      '''simple docstring'''


      with open(lowerCAmelCase__      ,	"""w"""	) as f:
            json.dump(lowerCAmelCase__      ,	lowerCAmelCase__      ,	indent=lowerCAmelCase__      ,	**lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					List[Any]:
      '''simple docstring'''


      with open(lowerCAmelCase__	) as f:
            return json.load(lowerCAmelCase__	)


def     UpperCAmelCase__ (       )				->					int:
      '''simple docstring'''


      lowercase														=			git.Repo(search_parent_directories=lowerCAmelCase__	)
      lowercase														=			{
          """repo_id""": str(lowerCAmelCase__	),
          """repo_sha""": str(repo.head.object.hexsha	),
          """repo_branch""": str(repo.active_branch	),
          """hostname""": str(socket.gethostname()	),
      }
      return repo_infos


def     UpperCAmelCase__ (       lowerCAmelCase__  :Callable      ,	lowerCAmelCase__  :Iterable	)				->					List:
      '''simple docstring'''


      return list(map(lowerCAmelCase__      ,	lowerCAmelCase__	)	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Dict	)				->					Dict:
      '''simple docstring'''


      with open(lowerCAmelCase__      ,	"""wb"""	) as f:
            return pickle.dump(lowerCAmelCase__      ,	lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					int:
      '''simple docstring'''


      def remove_articles(lowerCAmelCase__  :str	):
            return re.sub(R"""\b(a|an|the)\b"""      ,	""" """      ,	lowerCAmelCase__	)

      def white_space_fix(lowerCAmelCase__  :str	):
            return " ".join(text.split()	)

      def remove_punc(lowerCAmelCase__  :Union[str, Any]	):
            lowercase														=			set(string.punctuation	)
            return "".join(ch for ch in text if ch not in exclude	)

      def lower(lowerCAmelCase__  :Union[str, Any]	):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__	)	)	)	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Dict	)				->					Dict:
      '''simple docstring'''


      lowercase														=			normalize_answer(lowerCAmelCase__	).split()
      lowercase														=			normalize_answer(lowerCAmelCase__	).split()
      lowercase														=			Counter(lowerCAmelCase__	) & Counter(lowerCAmelCase__	)
      lowercase														=			sum(common.values()	)
      if num_same == 0:
            return 0
      lowercase														=			1.0 * num_same / len(lowerCAmelCase__	)
      lowercase														=			1.0 * num_same / len(lowerCAmelCase__	)
      lowercase														=			(2 * precision * recall) / (precision + recall)
      return fa


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]	)				->					Dict:
      '''simple docstring'''


      return normalize_answer(lowerCAmelCase__	) == normalize_answer(lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]	)				->					Dict:
      '''simple docstring'''


      assert len(lowerCAmelCase__	) == len(lowerCAmelCase__	)
      lowercase														=			0
      for hypo, pred in zip(lowerCAmelCase__      ,	lowerCAmelCase__	):
            em += exact_match_score(lowerCAmelCase__      ,	lowerCAmelCase__	)
      if len(lowerCAmelCase__	) > 0:
            em /= len(lowerCAmelCase__	)
      return {"em": em}


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					str:
      '''simple docstring'''


      return model_prefix.startswith("""rag"""	)






def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :str	)				->					Dict:
      '''simple docstring'''


      lowercase														=			{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      lowercase														=			"""dropout_rate"""
      for p in extra_params:
            if getattr(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	):
                  if not hasattr(lowerCAmelCase__      ,	lowerCAmelCase__	) and not hasattr(lowerCAmelCase__      ,	equivalent_param[p]	):
                        logger.info("""config doesn't have a `{}` attribute""".format(lowerCAmelCase__	)	)
                        delattr(lowerCAmelCase__      ,	lowerCAmelCase__	)
                        continue
                  lowercase														=			p if hasattr(lowerCAmelCase__      ,	lowerCAmelCase__	) else equivalent_param[p]
                  setattr(lowerCAmelCase__      ,	lowerCAmelCase__      ,	getattr(lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  delattr(lowerCAmelCase__      ,	lowerCAmelCase__	)
      return hparams, config




"""simple docstring"""






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.
__lowerCAmelCase					: int        ={"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class 				_A		( unittest.TestCase			):
      snake_case__    :     Optional[int]													=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__    :     str													=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__    :     Optional[Any]													=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            snake_case__    :     List[Any]													=			{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }

      @require_torch
      def        A__ (		self		):







            """simple docstring"""



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

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

            lowercase														=			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}]		)

            lowercase														=			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}],
                ]	, )

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

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

            lowercase														=			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}]]		)

            lowercase														=			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}],
                ]	, )

            lowercase														=			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        A__ (		self		):







            """simple docstring"""



            import torch

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

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

      @require_tf
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline(
                task="""text-classification"""	, model="""hf-internal-testing/tiny-random-distilbert"""	, framework="""tf"""		)

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

      @slow
      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""		)

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

      @slow
      @require_tf
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""	, framework="""tf"""		)

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TextClassificationPipeline(model=__lowerCAmelCase	, tokenizer=__lowerCAmelCase		)
            return text_classifier, ["HuggingFace is in", "This is another test"]







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			text_classifier.model
            # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
            lowercase														=			"""HuggingFace is in"""
            lowercase														=			text_classifier(__lowerCAmelCase		)

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

            lowercase														=			["""HuggingFace is in """, """Paris is in France"""]
            lowercase														=			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
            lowercase														=			text_classifier(__lowerCAmelCase	, top_k=__lowerCAmelCase		)
            lowercase														=			len(model.config.idalabel.values()		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [[{"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}] * N, [{"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}] * N]	, )

            lowercase														=			{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
            lowercase														=			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.
            lowercase														=			[["""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
            lowercase														=			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()		)



