import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						LEDTokenizer
  lowerCamelCase__     =						LEDTokenizerFast
  lowerCamelCase__     =						True







  def    __A     (							self :       Optional[int]   )  ->	Any:
        super().setUp()
        SCREAMING_SNAKE_CASE_          =  [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        SCREAMING_SNAKE_CASE_          =  dict(zip(__magic_name__  ,     range(len(__magic_name__   )   )   )   )
        SCREAMING_SNAKE_CASE_          =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        SCREAMING_SNAKE_CASE_          =  {"unk_token": "<unk>"}

        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]   )
        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["merges_file"]   )
        with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              fp.write(json.dumps(__magic_name__   ) + "\n"   )
        with open(self.merges_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              fp.write("\n".join(__magic_name__   )   )







  def    __A     (							self :       str  ,     **__magic_name__ :       Union[str, Any]   )  ->	Tuple:
        kwargs.update(self.special_tokens_map   )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Union[str, Any]  ,     **__magic_name__ :       Optional[int]   )  ->	Tuple:
        kwargs.update(self.special_tokens_map   )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[Any]:
        return "lower newer", "lower newer"







  @cached_property
  def    __A     (							self :       Dict   )  ->	Tuple:
        return LEDTokenizer.from_pretrained("allenai/led-base-16384"   )







  @cached_property
  def    __A     (							self :       Optional[int]   )  ->	Tuple:
        return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"   )







  @require_torch
  def    __A     (							self :       Union[str, Any]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
        SCREAMING_SNAKE_CASE_          =  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     max_length=len(__magic_name__   )  ,     padding=__magic_name__  ,     return_tensors="pt"   )
              self.assertIsInstance(__magic_name__  ,     __magic_name__   )

              self.assertEqual((2, 9)  ,     batch.input_ids.shape   )
              self.assertEqual((2, 9)  ,     batch.attention_mask.shape   )
              SCREAMING_SNAKE_CASE_          =  batch.input_ids.tolist()[0]
              self.assertListEqual(__magic_name__  ,     __magic_name__   )







  @require_torch
  def    __A     (							self :       str   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     padding=__magic_name__  ,     return_tensors="pt"   )
              self.assertIn("input_ids"  ,     __magic_name__   )
              self.assertIn("attention_mask"  ,     __magic_name__   )
              self.assertNotIn("labels"  ,     __magic_name__   )
              self.assertNotIn("decoder_attention_mask"  ,     __magic_name__   )







  @require_torch
  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  [
            "Summary of the text.",
            "Another summary.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  tokenizer(text_target=__magic_name__  ,     max_length=32  ,     padding="max_length"  ,     return_tensors="pt"   )
              self.assertEqual(32  ,     targets["input_ids"].shape[1]   )







  @require_torch
  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  tokenizer(
                  ["I am a small frog" * 1_024, "I am a small frog"]  ,     padding=__magic_name__  ,     truncation=__magic_name__  ,     return_tensors="pt"   )
              self.assertIsInstance(__magic_name__  ,     __magic_name__   )
              self.assertEqual(batch.input_ids.shape  ,     (2, 5_122)   )







  @require_torch
  def    __A     (							self :       Optional[int]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  ["A long paragraph for summarization."]
        SCREAMING_SNAKE_CASE_          =  [
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     return_tensors="pt"   )
              SCREAMING_SNAKE_CASE_          =  tokenizer(text_target=__magic_name__  ,     return_tensors="pt"   )
              SCREAMING_SNAKE_CASE_          =  inputs["input_ids"]
              SCREAMING_SNAKE_CASE_          =  targets["input_ids"]
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )







  @require_torch
  def    __A     (							self :       int   )  ->	Dict:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_          =  ["Summary of the text.", "Another summary."]
              SCREAMING_SNAKE_CASE_          =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     padding=__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  [[0] * len(__magic_name__   ) for x in encoded_output["input_ids"]]
              SCREAMING_SNAKE_CASE_          =  tokenizer.pad(__magic_name__   )
              self.assertSequenceEqual(outputs["global_attention_mask"]  ,     __magic_name__   )







  def    __A     (							self :       int   )  ->	int:
        pass







  def    __A     (							self :       List[str]   )  ->	List[Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
                    SCREAMING_SNAKE_CASE_          =  self.rust_tokenizer_class.from_pretrained(__magic_name__  ,     **__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  self.tokenizer_class.from_pretrained(__magic_name__  ,     **__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  "A, <mask> AllenNLP sentence."
                    SCREAMING_SNAKE_CASE_          =  tokenizer_r.encode_plus(__magic_name__  ,     add_special_tokens=__magic_name__  ,     return_token_type_ids=__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  tokenizer_p.encode_plus(__magic_name__  ,     add_special_tokens=__magic_name__  ,     return_token_type_ids=__magic_name__   )
                    self.assertEqual(sum(tokens_r["token_type_ids"]   )  ,     sum(tokens_p["token_type_ids"]   )   )
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   )  ,     sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   )  ,     )

                    SCREAMING_SNAKE_CASE_          =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
                    SCREAMING_SNAKE_CASE_          =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )
                    self.assertSequenceEqual(tokens_p["input_ids"]  ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )
                    self.assertSequenceEqual(tokens_r["input_ids"]  ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )

                    self.assertSequenceEqual(
                        __magic_name__  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )
                    self.assertSequenceEqual(
                        __magic_name__  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


