
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   (      __a   , unittest.TestCase			):
				_lowercase     						=LEDTokenizer
				_lowercase     						=LEDTokenizerFast
				_lowercase     						=True




				def 	__a		(   self	)							-> str:
							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(_UpperCamelCase							,							range(len(_UpperCamelCase	)	)	)	)
							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(_UpperCamelCase	) + "\n"	)
							with open(self.merges_file							,							"w"							,							encoding="utf-8"	) as fp:
										fp.write("\n".join(_UpperCamelCase	)	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Dict:
							kwargs.update(self.special_tokens_map	)
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Dict:
							kwargs.update(self.special_tokens_map	)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> List[Any]:
							return "lower newer", "lower newer"




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




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




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

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

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




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




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




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




				@require_torch
				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      ["A long paragraph for summarization."]
							lowerCAmelCase_         =      [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							return_tensors="pt"	)
										lowerCAmelCase_         =      tokenizer(text_target=_UpperCamelCase							,							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 	__a		(   self	)							-> Dict:
							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(_UpperCamelCase							,							padding=_UpperCamelCase	)
										lowerCAmelCase_         =      [[0] * len(_UpperCamelCase	) for x in encoded_output["input_ids"]]
										lowerCAmelCase_         =      tokenizer.pad(_UpperCamelCase	)
										self.assertSequenceEqual(outputs["global_attention_mask"]							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Union[str, Any]:
							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(_UpperCamelCase							,							**_UpperCamelCase	)
													lowerCAmelCase_         =      self.tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)
													lowerCAmelCase_         =      "A, <mask> AllenNLP sentence."
													lowerCAmelCase_         =      tokenizer_r.encode_plus(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							return_token_type_ids=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.encode_plus(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							return_token_type_ids=_UpperCamelCase	)
													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, 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(
													    _UpperCamelCase							,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
													self.assertSequenceEqual(
													    _UpperCamelCase							,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

			lowerCAmelCase_         =      [item for sublist in list(device_map.values()							) for item in sublist]

			# Duplicate check
			lowerCAmelCase_         =      []
			for i in device_map_blocks:
						if device_map_blocks.count(__lowerCAmelCase							) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(__lowerCAmelCase							)
    # Missing blocks
			lowerCAmelCase_         =      [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_         =      [i for i in device_map_blocks if i not in blocks]

			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
						    " These attention blocks were specified more than once: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
						    "blocks to a device on the device_map: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(__lowerCAmelCase							)							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(__lowerCAmelCase							)							)
			lowerCAmelCase_         =      int(ceil(n_layers / len(__lowerCAmelCase							)							)							)
			lowerCAmelCase_         =      [layers[i : i + n_blocks] for i in range(0       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)]

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



