





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       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     LEDTokenizer
      _UpperCAmelCase			:Union[str, Any]							=     LEDTokenizerFast
      _UpperCAmelCase			:Tuple							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()
       UpperCamelCase			:      Union[str, Any] 			=     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCamelCase			:      Union[str, Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Dict 			=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase			:      Any 			=     {"unk_token": "<unk>"}

       UpperCamelCase			:      int 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Dict 			=     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(A_     ) + "\n"     )
       with open(self.merges_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write("\n".join(A_     )     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return "lower newer", "lower newer"





      @cached_property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return LEDTokenizer.from_pretrained("allenai/led-base-16384"     )





      @cached_property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
       UpperCamelCase			:      Optional[Any] 			=     [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Optional[Any] 			=     tokenizer(A_ ,							max_length=len(A_     ) ,							padding=A_ ,							return_tensors="pt"     )
        self.assertIsInstance(A_ ,							A_     )

        self.assertEqual((2, 9) ,							batch.input_ids.shape     )
        self.assertEqual((2, 9) ,							batch.attention_mask.shape     )
        UpperCamelCase			:      List[Any] 			=     batch.input_ids.tolist()[0]
        self.assertListEqual(A_ ,							A_     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Tuple 			=     tokenizer(A_ ,							padding=A_ ,							return_tensors="pt"     )
        self.assertIn("input_ids" ,							A_     )
        self.assertIn("attention_mask" ,							A_     )
        self.assertNotIn("labels" ,							A_     )
        self.assertNotIn("decoder_attention_mask" ,							A_     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     [
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Union[str, Any] 			=     tokenizer(text_target=A_ ,							max_length=32 ,							padding="max_length" ,							return_tensors="pt"     )
        self.assertEqual(32 ,							targets["input_ids"].shape[1]     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      List[str] 			=     tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"] ,							padding=A_ ,							truncation=A_ ,							return_tensors="pt"     )
        self.assertIsInstance(A_ ,							A_     )
        self.assertEqual(batch.input_ids.shape ,							(2, 5122)     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     ["A long paragraph for summarization."]
       UpperCamelCase			:      List[str] 			=     [
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Optional[int] 			=     tokenizer(A_ ,							return_tensors="pt"     )
        UpperCamelCase			:      Tuple 			=     tokenizer(text_target=A_ ,							return_tensors="pt"     )
        UpperCamelCase			:      str 			=     inputs["input_ids"]
        UpperCamelCase			:      Any 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Any 			=     ["Summary of the text.", "Another summary."]
        UpperCamelCase			:      int 			=     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        UpperCamelCase			:      Dict 			=     tokenizer(A_ ,							padding=A_     )
        UpperCamelCase			:      Union[str, Any] 			=     [[0] * len(A_     ) for x in encoded_output["input_ids"]]
        UpperCamelCase			:      Tuple 			=     tokenizer.pad(A_     )
        self.assertSequenceEqual(outputs["global_attention_mask"] ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Tuple 			=     self.tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Optional[Any] 			=     "A, <mask> AllenNLP sentence."
         UpperCamelCase			:      Union[str, Any] 			=     tokenizer_r.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )
         UpperCamelCase			:      Tuple 			=     tokenizer_p.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )
         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"]     ) ,							)

         UpperCamelCase			:      str 			=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
         UpperCamelCase			:      Optional[Any] 			=     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]     )
         self.assertSequenceEqual(tokens_p["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )
         self.assertSequenceEqual(tokens_r["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )

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







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
