


"""simple docstring"""







from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict[str, torch.Tensor]    ):


		'''simple docstring'''







		lowerCAmelCase					:					str								=							[]
		lowerCAmelCase					:					str								=							[]
		lowerCAmelCase					:					Optional[int]								=							[]

		for rt in rc.restypes:
				lowerCAmelCase					:					Tuple								=							rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
				restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
				lowerCAmelCase					:					int								=							{name: i for i, name in enumerate(_a    )}
				restype_atomaa_to_atomaa_list.append(
				    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

				restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

		# Add dummy mapping for restype 'UNK'
		restype_atomaa_to_atomaa_list.append([0] * 1_4    )
		restype_atomaa_to_atomaa_list.append([0] * 3_7    )
		restype_atomaa_mask_list.append([0.0] * 1_4    )

		lowerCAmelCase					:					Tuple								=							torch.tensor(
		    _a    ,				dtype=torch.intaa    ,				device=protein["aatype"].device    ,				)
		lowerCAmelCase					:					List[str]								=							torch.tensor(
		    _a    ,				dtype=torch.intaa    ,				device=protein["aatype"].device    ,				)
		lowerCAmelCase					:					Tuple								=							torch.tensor(
		    _a    ,				dtype=torch.floataa    ,				device=protein["aatype"].device    ,				)
		lowerCAmelCase					:					Optional[Any]								=							protein["""aatype"""].to(torch.long    )

		# create the mapping for (residx, atom14) --> atom37, i.e. an array
		# with shape (num_res, 14) containing the atom37 indices for this protein
		lowerCAmelCase					:					str								=							restype_atomaa_to_atomaa[protein_aatype]
		lowerCAmelCase					:					List[Any]								=							restype_atomaa_mask[protein_aatype]

		lowerCAmelCase					:					Tuple								=							residx_atomaa_mask
		lowerCAmelCase					:					Optional[Any]								=							residx_atomaa_to_atomaa.long()

		# create the gather indices for mapping back
		lowerCAmelCase					:					Any								=							restype_atomaa_to_atomaa[protein_aatype]
		lowerCAmelCase					:					Union[str, Any]								=							residx_atomaa_to_atomaa.long()

		# create the corresponding mask
		lowerCAmelCase					:					Optional[Any]								=							torch.zeros([2_1, 3_7]    ,				dtype=torch.floataa    ,				device=protein["aatype"].device    )
		for restype, restype_letter in enumerate(rc.restypes    ):
				lowerCAmelCase					:					int								=							rc.restype_atoa[restype_letter]
				lowerCAmelCase					:					List[str]								=							rc.residue_atoms[restype_name]
				for atom_name in atom_names:
						lowerCAmelCase					:					Tuple								=							rc.atom_order[atom_name]
						lowerCAmelCase					:					Any								=							1

		lowerCAmelCase					:					List[str]								=							restype_atomaa_mask[protein_aatype]
		lowerCAmelCase					:					Dict								=							residx_atomaa_mask

		return protein





def  a__      (    SCREAMING_SNAKE_CASE   :   Dict[str, torch.Tensor]    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[int]								=							tree_map(lambda SCREAMING_SNAKE_CASE    : torch.tensor(_a    ,				device=batch["aatype"].device    )    ,				_a    ,				np.ndarray    )
		lowerCAmelCase					:					Optional[Any]								=							tensor_tree_map(lambda SCREAMING_SNAKE_CASE    : np.array(_a    )    ,				make_atomaa_masks(_a    )    )
		return out









"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ]

             # Simple input tests
             try:
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
             except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"						)

             lowerCAmelCase					:					Tuple								=							None  # Hotfixing padding = None
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)		,					1						)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)		,					1						)





