
"""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				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				LEDTokenizer
			snake_case	      =				LEDTokenizerFast
			snake_case	      =				True




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







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

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




			def      lowerCamelCase__     (				self						:					Tuple       ,   **_snake_case						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								kwargs.update(self.special_tokens_map	)
								return self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					str       ,   **_snake_case						:					int	)      -> int:







								"""simple docstring"""
								kwargs.update(self.special_tokens_map	)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					int	)      -> int:







								"""simple docstring"""
								return "lower newer", "lower newer"




			@cached_property
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Dict:







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




			@cached_property
			def      lowerCamelCase__     (				self						:					str	)      -> int:







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




			@require_torch
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						["A long paragraph for summarization.", "Another paragraph for summarization."]
								A_				 =						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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




			@require_torch
			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[Any]:







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




			@require_torch
			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[int]:







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




			@require_torch
			def      lowerCamelCase__     (				self						:					List[str]	)      -> Optional[Any]:







								"""simple docstring"""
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													A_				 =						tokenizer(
													    ["I am a small frog" * 1_024, "I am a small frog"]       ,   padding=_snake_case       ,   truncation=_snake_case       ,   return_tensors="pt"	)
													self.assertIsInstance(_snake_case       ,   _snake_case	)
													self.assertEqual(batch.input_ids.shape       ,   (2, 5_122)	)




			@require_torch
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						["A long paragraph for summarization."]
								A_				 =						[
								    "Summary of the text.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													A_				 =						tokenizer(_snake_case       ,   return_tensors="pt"	)
													A_				 =						tokenizer(text_target=_snake_case       ,   return_tensors="pt"	)
													A_				 =						inputs["input_ids"]
													A_				 =						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      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[Any]:







								"""simple docstring"""
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													A_				 =						["Summary of the text.", "Another summary."]
													A_				 =						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								pass






			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[Any]:







								"""simple docstring"""
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
																		A_				 =						self.rust_tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)
																		A_				 =						self.tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)
																		A_				 =						"A, <mask> AllenNLP sentence."
																		A_				 =						tokenizer_r.encode_plus(_snake_case       ,   add_special_tokens=_snake_case       ,   return_token_type_ids=_snake_case	)
																		A_				 =						tokenizer_p.encode_plus(_snake_case       ,   add_special_tokens=_snake_case       ,   return_token_type_ids=_snake_case	)
																		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"]	)       ,   )

																		A_				 =						tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																		A_				 =						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(
																		    _snake_case       ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
																		self.assertSequenceEqual(
																		    _snake_case       ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)






"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:



		class __lowerCAmelCase				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					int       ,   **_snake_case						:					Optional[Any]	)      -> str:







										"""simple docstring"""
										pass
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						hashlib.mda(image.tobytes()				)
					return m.hexdigest()[:10]
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						np.array(__a				)
					A_				 =						npimg.shape
					return {"hash": hashimage(__a				), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
			snake_case	      =				dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Dict       ,   _snake_case						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						MaskGenerationPipeline(model=_snake_case       ,   image_processor=_snake_case	)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								pass




			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"	)
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						pipeline("mask-generation"       ,   model="facebook/sam-vit-huge"	)

								A_				 =						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
								    ]       ,   )
								# fmt: on






			@require_torch
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						"facebook/sam-vit-huge"
								A_				 =						pipeline("mask-generation"       ,   model=_snake_case	)

								A_				 =						image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"       ,   pred_iou_thresh=1       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								    ]       ,   )




