





'''simple docstring'''


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_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[int]								=       LEDTokenizer
 __lowerCamelCase    :				Union[str, Any]								=       LEDTokenizerFast
 __lowerCamelCase    :				int								=       True




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

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




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Optional[Any]:
     kwargs.update(self.special_tokens_map		)
     return self.tokenizer_class.from_pretrained(self.tmpdirname      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     kwargs.update(self.special_tokens_map		)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     return "lower newer", "lower newer"




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




 @cached_property
 def 				_snake_case (						self		)     ->				Tuple:
     return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"		)




 @require_torch
 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
     _lowerCAmelCase						      =   [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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




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




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




 @require_torch
 def 				_snake_case (						self		)     ->				Tuple:
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCAmelCase						      =   tokenizer(
             ["I am a small frog" * 1024, "I am a small frog"]      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  return_tensors="pt"		)
         self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)
         self.assertEqual(batch.input_ids.shape      ,  (2, 5122)		)




 @require_torch
 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   ["A long paragraph for summarization."]
     _lowerCAmelCase						      =   [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
         _lowerCAmelCase						      =   tokenizer(text_target=_lowerCAmelCase      ,  return_tensors="pt"		)
         _lowerCAmelCase						      =   inputs["input_ids"]
         _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				List[Any]:
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _lowerCAmelCase						      =   ["Summary of the text.", "Another summary."]
         _lowerCAmelCase						      =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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




 def 				_snake_case (						self		)     ->				List[str]:
     pass





 def 				_snake_case (						self		)     ->				List[str]:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             _lowerCAmelCase						      =   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)
             _lowerCAmelCase						      =   self.tokenizer_class.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)
             _lowerCAmelCase						      =   "A, <mask> AllenNLP sentence."
             _lowerCAmelCase						      =   tokenizer_r.encode_plus(_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase      ,  return_token_type_ids=_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.encode_plus(_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase      ,  return_token_type_ids=_lowerCAmelCase		)
             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"]		)      ,  )

             _lowerCAmelCase						      =   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
             _lowerCAmelCase						      =   tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)
             self.assertSequenceEqual(tokens_p["input_ids"]      ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
             self.assertSequenceEqual(tokens_r["input_ids"]      ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

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









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-12      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


