





"""simple docstring"""





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


UpperCamelCase_					:						List[Any]				= logging.get_logger(__name__)

UpperCamelCase_					:						List[str]				= {'''vocab_file''': '''vocab.txt'''}

UpperCamelCase_					:						Union[str, Any]				= {
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

UpperCamelCase_					:						Optional[int]				= {
    '''facebook/esm2_t6_8M_UR50D''': 1024,
    '''facebook/esm2_t12_35M_UR50D''': 1024,
}
def 							A_     (__a				):

					'''simple docstring'''





					with open(__a				,   "r"				) as f:
										A_				 =						f.read().splitlines()
										return [l.strip() for l in lines]




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					List[str]       ,   _snake_case						:					List[str]       ,   _snake_case						:					int="<unk>"       ,   _snake_case						:					Union[str, Any]="<cls>"       ,   _snake_case						:					List[str]="<pad>"       ,   _snake_case						:					int="<mask>"       ,   _snake_case						:					Tuple="<eos>"       ,   **_snake_case						:					Optional[int]       ,   )      -> int:







								"""simple docstring"""
								super().__init__(**_snake_case	)
								A_				 =						load_vocab_file(_snake_case	)
								A_				 =						dict(enumerate(self.all_tokens	)	)
								A_				 =						{tok: ind for ind, tok in enumerate(self.all_tokens	)}
								A_				 =						unk_token
								A_				 =						cls_token
								A_				 =						pad_token
								A_				 =						mask_token
								A_				 =						eos_token
								A_				 =						self.all_tokens
								self._create_trie(self.unique_no_split_tokens	)




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







								"""simple docstring"""
								return self._id_to_token.get(_snake_case       ,   self.unk_token	)




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







								"""simple docstring"""
								return self._token_to_id.get(_snake_case       ,   self._token_to_id.get(self.unk_token	)	)




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







								"""simple docstring"""
								return text.split()




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Any=False	)      -> Union[str, Any]:







								"""simple docstring"""
								return len(self._id_to_token	)




			def      lowerCamelCase__     (				self						:					int	)      -> Dict:







								"""simple docstring"""
								return {token: i for i, token in enumerate(self.all_tokens	)}




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







								"""simple docstring"""
								return self._token_to_id.get(_snake_case       ,   self._token_to_id.get(self.unk_token	)	)




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







								"""simple docstring"""
								return self._id_to_token.get(_snake_case       ,   self.unk_token	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								A_				 =						[self.cls_token_id]
								A_				 =						[self.eos_token_id]  # No sep token in ESM vocabulary
								if token_ids_a is None:
													if self.eos_token_id is None:
																		return cls + token_ids_a
													else:
																		return cls + token_ids_a + sep
								elif self.eos_token_id is None:
													raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"	)
								return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					List       ,   _snake_case						:					Optional[List] = None       ,   _snake_case						:					bool = False	)      -> List[int]:







								"""simple docstring"""
								if already_has_special_tokens:
													if token_ids_a is not None:
																		raise ValueError(
																		    "You should not supply a second sequence if the provided sequence of "
																		    "ids is already formatted with special tokens for the model."	)

													return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
								A_				 =						[1] + ([0] * len(_snake_case	)) + [1]
								if token_ids_a is not None:
													mask += [0] * len(_snake_case	) + [1]
								return mask




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







								"""simple docstring"""
								A_				 =						os.path.join(_snake_case       ,   (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"	)
								with open(_snake_case       ,   "w"	) as f:
													f.write("\n".join(self.all_tokens	)	)
								return (vocab_file,)




			@property
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								return self.get_vocab_size(with_added_tokens=_snake_case	)






			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					Union[List[str], List[AddedToken]]       ,   _snake_case						:					bool = False	)      -> int:







								"""simple docstring"""
								return super()._add_tokens(_snake_case       ,   special_tokens=_snake_case	)






"""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},
								    ]       ,   )




