

"""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


__A						       =   logging.get_logger(__name__)

__A						       =   {"""vocab_file""": """vocab.txt"""}

__A						       =   {
    """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""",
    },
}

__A						       =   {
    """facebook/esm2_t6_8M_UR50D""": 1024,
    """facebook/esm2_t12_35M_UR50D""": 1024,
}






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		with open(__SCREAMING_SNAKE_CASE ,			"r"			) as f:
				__lowerCAmelCase:	List[str]   				=      f.read().splitlines()
				return [l.strip() for l in lines]





class    snake_case     (    lowerCAmelCase__  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  ["input_ids", "attention_mask"]

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



						'''simple docstring'''


						super().__init__(**a__)
						__lowerCAmelCase:	Optional[Any]   				=      load_vocab_file(a__)
						__lowerCAmelCase:	str   				=      dict(enumerate(self.all_tokens))
						__lowerCAmelCase:	List[Any]   				=      {tok: ind for ind, tok in enumerate(self.all_tokens)}
						__lowerCAmelCase:	str   				=      unk_token
						__lowerCAmelCase:	int   				=      cls_token
						__lowerCAmelCase:	int   				=      pad_token
						__lowerCAmelCase:	List[str]   				=      mask_token
						__lowerCAmelCase:	List[Any]   				=      eos_token
						__lowerCAmelCase:	List[Any]   				=      self.all_tokens
						self._create_trie(self.unique_no_split_tokens)

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Union[str, Any])->       Union[str, Any]:



						'''simple docstring'''


						return self._id_to_token.get(a__					,  self.unk_token)

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Optional[Any])->       List[Any]:



						'''simple docstring'''


						return self._token_to_id.get(a__					,  self._token_to_id.get(self.unk_token))

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Tuple					,  **UpperCamelCase__							:    Dict)->       Dict:



						'''simple docstring'''


						return text.split()

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Optional[Any]=False)->       str:



						'''simple docstring'''


						return len(self._id_to_token)

				def 							lowercase_					(     self							:    Optional[int])->       Any:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						return self._token_to_id.get(a__					,  self._token_to_id.get(self.unk_token))

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    int)->       Any:



						'''simple docstring'''


						return self._id_to_token.get(a__					,  self.unk_token)

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str] = None)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      [self.cls_token_id]
						__lowerCAmelCase:	Optional[int]   				=      [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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Optional[int] = None					,  UpperCamelCase__							:    List[str] = False)->       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]
						__lowerCAmelCase:	Optional[Any]   				=      [1] + ([0] * len(a__)) + [1]
						if token_ids_a is not None:
								mask += [0] * len(a__) + [1]
						return mask

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    str)->       Optional[int]:



						'''simple docstring'''


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

				@property
				def 							lowercase_					(     self							:    str)->       List[Any]:



						'''simple docstring'''


						return self.get_vocab_size(with_added_tokens=a__)




				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Union[str, Any] = False)->       Optional[Any]:



						'''simple docstring'''


						return super()._add_tokens(a__					,  special_tokens=a__)





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[str]=3					,  UpperCamelCase__							:    List[Any]=1_8					,  UpperCamelCase__							:    List[Any]=3_0					,  UpperCamelCase__							:    List[str]=4_0_0					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    List[str]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    int=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__							:    str=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__							:    List[str]=True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:	Union[str, Any]   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	Optional[int]   				=      parent
						__lowerCAmelCase:	List[str]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	List[str]   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	List[Any]   				=      do_center_crop
						__lowerCAmelCase:	Optional[int]   				=      crop_size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	List[str]   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std
						__lowerCAmelCase:	str   				=      do_convert_rgb

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}




				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    Dict=False)->       List[str]:



						'''simple docstring'''


						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
								__lowerCAmelCase:	Optional[int]   				=      []
								for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        2_5_5					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
						else:
								__lowerCAmelCase:	List[str]   				=      []
								for i in range(self.batch_size):
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
										image_inputs.append(np.random.randint(2_5_5					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

						if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowerCAmelCase:	Union[str, Any]   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						if torchify:
								__lowerCAmelCase:	str   				=      [torch.from_numpy(UpperCamelCase__) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      ChineseCLIPImageProcessingTester(self					,  do_center_crop=UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_2_4, "width": 2_2_4})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

				def 							lowercase_					(     self							:    List[str])->       Optional[int]:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	Optional[int]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	int   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Any   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      3

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       Any:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						pass




				def 							lowercase_					(     self							:    Tuple)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )


