


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     List[Any]			= CodeGenTokenizer
    A_  :     str			= CodeGenTokenizerFast
    A_  :     Any			= True
    A_  :     Optional[Any]			= {'add_prefix_space': True}
    A_  :     Optional[Any]			= False





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCAmelCase_							:      List[str]						=   [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
           '''<|endoftext|>''',
       ]
       UpperCAmelCase_							:      Optional[Any]						=   dict(zip(__snake_case       ,   range(len(__snake_case	)	)	)	)
       UpperCAmelCase_							:      int						=   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       UpperCAmelCase_							:      Optional[Any]						=   {'''unk_token''': '''<unk>'''}

       UpperCAmelCase_							:      List[Any]						=   os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['''vocab_file''']	)
       UpperCAmelCase_							:      Optional[int]						=   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				:   str       ,   **__snake_case				:   str	):





       '''simple docstring'''
       kwargs.update(self.special_tokens_map	)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   **__snake_case				:   Optional[Any]	):





       '''simple docstring'''
       kwargs.update(self.special_tokens_map	)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   '''lower newer'''
       UpperCAmelCase_							:      Union[str, Any]						=   '''lower newer'''
       return input_text, output_text





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   CodeGenTokenizer(self.vocab_file       ,   self.merges_file       ,   **self.special_tokens_map	)
       UpperCAmelCase_							:      str						=   '''lower newer'''
       UpperCAmelCase_							:      Any						=   ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       UpperCAmelCase_							:      Union[str, Any]						=   tokenizer.tokenize(__snake_case       ,   add_prefix_space=__snake_case	)
       self.assertListEqual(__snake_case       ,   __snake_case	)

       UpperCAmelCase_							:      str						=   tokens + [tokenizer.unk_token]
       UpperCAmelCase_							:      str						=   [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       if not self.test_rust_tokenizer:
          return

       UpperCAmelCase_							:      Any						=   self.get_tokenizer()
       UpperCAmelCase_							:      Optional[int]						=   self.get_rust_tokenizer(add_prefix_space=__snake_case	)

       UpperCAmelCase_							:      Optional[int]						=   '''lower newer'''

       # Testing tokenization
       UpperCAmelCase_							:      Dict						=   tokenizer.tokenize(__snake_case       ,   add_prefix_space=__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   rust_tokenizer.tokenize(__snake_case	)
       self.assertListEqual(__snake_case       ,   __snake_case	)

       # Testing conversion to ids without special tokens
       UpperCAmelCase_							:      Optional[int]						=   tokenizer.encode(__snake_case       ,   add_special_tokens=__snake_case       ,   add_prefix_space=__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   rust_tokenizer.encode(__snake_case       ,   add_special_tokens=__snake_case	)
       self.assertListEqual(__snake_case       ,   __snake_case	)

       # Testing conversion to ids with special tokens
       UpperCAmelCase_							:      Union[str, Any]						=   self.get_rust_tokenizer(add_prefix_space=__snake_case	)
       UpperCAmelCase_							:      Any						=   tokenizer.encode(__snake_case       ,   add_prefix_space=__snake_case	)
       UpperCAmelCase_							:      str						=   rust_tokenizer.encode(__snake_case	)
       self.assertListEqual(__snake_case       ,   __snake_case	)

       # Testing the unknown token
       UpperCAmelCase_							:      List[str]						=   tokens + [rust_tokenizer.unk_token]
       UpperCAmelCase_							:      str						=   [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__snake_case	)       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   int       ,   *__snake_case				:   str       ,   **__snake_case				:   str	):





       '''simple docstring'''
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   Union[str, Any]=15	):





       '''simple docstring'''
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
             UpperCAmelCase_							:      int						=   self.rust_tokenizer_class.from_pretrained(__snake_case       ,   **__snake_case	)

             # Simple input
             UpperCAmelCase_							:      str						=   '''This is a simple input'''
             UpperCAmelCase_							:      List[Any]						=   ['''This is a simple input 1''', '''This is a simple input 2''']
             UpperCAmelCase_							:      Any						=   ('''This is a simple input''', '''This is a pair''')
             UpperCAmelCase_							:      List[str]						=   [
                 ('''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
             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 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   CodeGenTokenizer.from_pretrained(self.tmpdirname       ,   pad_token='''<pad>'''	)

       # Simple input
       UpperCAmelCase_							:      Optional[int]						=   '''This is a simple input'''
       UpperCAmelCase_							:      Optional[Any]						=   ['''This is a simple input looooooooong''', '''This is a simple input''']
       UpperCAmelCase_							:      str						=   ('''This is a simple input''', '''This is a pair''')
       UpperCAmelCase_							:      List[str]						=   [
           ('''This is a simple input loooooong''', '''This is a simple input'''),
           ('''This is a simple pair loooooong''', '''This is a simple pair'''),
       ]

       UpperCAmelCase_							:      int						=   tokenizer.pad_token_id

       UpperCAmelCase_							:      List[Any]						=   tokenizer(__snake_case       ,   padding='''max_length'''       ,   max_length=30       ,   return_tensors='''np'''	)
       UpperCAmelCase_							:      Any						=   tokenizer(__snake_case       ,   padding=__snake_case       ,   truncate=__snake_case       ,   return_tensors='''np'''	)
       UpperCAmelCase_							:      List[Any]						=   tokenizer(*__snake_case       ,   padding='''max_length'''       ,   max_length=60       ,   return_tensors='''np'''	)
       UpperCAmelCase_							:      Optional[int]						=   tokenizer(__snake_case       ,   padding=__snake_case       ,   truncate=__snake_case       ,   return_tensors='''np'''	)

       # s
       # test single string max_length padding
       self.assertEqual(out_s['''input_ids'''].shape[-1]       ,   30	)
       self.assertTrue(pad_token_id in out_s['''input_ids''']	)
       self.assertTrue(0 in out_s['''attention_mask''']	)

       # s2
       # test automatic padding
       self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,   33	)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]	)
       self.assertFalse(0 in out_sa['''attention_mask'''][0]	)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]	)
       self.assertTrue(0 in out_sa['''attention_mask'''][1]	)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p['''input_ids'''].shape[-1]       ,   60	)
       self.assertTrue(pad_token_id in out_p['''input_ids''']	)
       self.assertTrue(0 in out_p['''attention_mask''']	)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,   52	)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]	)
       self.assertFalse(0 in out_pa['''attention_mask'''][0]	)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]	)
       self.assertTrue(0 in out_pa['''attention_mask'''][1]	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   '''$$$'''
       UpperCAmelCase_							:      Tuple						=   CodeGenTokenizer.from_pretrained(self.tmpdirname       ,   bos_token=__snake_case       ,   add_bos_token=__snake_case	)

       UpperCAmelCase_							:      Optional[int]						=   '''This is a simple input'''
       UpperCAmelCase_							:      Union[str, Any]						=   ['''This is a simple input 1''', '''This is a simple input 2''']

       UpperCAmelCase_							:      Optional[Any]						=   tokenizer.bos_token_id

       UpperCAmelCase_							:      Dict						=   tokenizer(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   tokenizer(__snake_case	)

       self.assertEqual(out_s.input_ids[0]       ,   __snake_case	)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids	)	)

       UpperCAmelCase_							:      str						=   tokenizer.decode(out_s.input_ids	)
       UpperCAmelCase_							:      List[str]						=   tokenizer.batch_decode(out_sa.input_ids	)

       self.assertEqual(decode_s.split()[0]       ,   __snake_case	)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa	)	)





    @slow
    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''	)

       UpperCAmelCase_							:      int						=   '''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
       UpperCAmelCase_							:      Tuple						=   '''\nif len_a > len_b:      result = a\nelse:      result = b'''

       UpperCAmelCase_							:      List[Any]						=   tokenizer.encode(__snake_case	)
       UpperCAmelCase_							:      str						=   ['''^#''', re.escape('''<|endoftext|>'''	), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
       UpperCAmelCase_							:      str						=   tokenizer.decode(__snake_case       ,   truncate_before_pattern=__snake_case	)
       self.assertEqual(__snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       pass



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     int
    A_  :     int
    A_  :     float
    A_  :     float
    A_  :     Tuple[int]





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
       assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
       assert len(self.x.shape	) == len(self.y.shape	) == len(self.z.shape	) == len(self.origin.shape	) == 2





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return torch.from_numpy(np.array([self.width, self.height]       ,   dtype=np.floataa	)	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]       ,   dtype=np.floataa	)	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

       UpperCAmelCase_							:      str						=   self.get_image_coords()
       UpperCAmelCase_							:      List[str]						=   torch.broadcast_to(coords.unsqueeze(0	)       ,   [batch_size * inner_batch_size, *coords.shape]	)
       UpperCAmelCase_							:      Tuple						=   self.get_camera_rays(__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   self.resolution()
       UpperCAmelCase_							:      Optional[Any]						=   self.fov()

       UpperCAmelCase_							:      int						=   (flat.float() / (res - 1)) * 2 - 1
       UpperCAmelCase_							:      Optional[Any]						=   fracs * torch.tan(fov / 2	)

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[Any]						=   (
           self.z.view(__snake_case       ,   1       ,   3	)
           + self.x.view(__snake_case       ,   1       ,   3	) * fracs[:, :, :1]
           + self.y.view(__snake_case       ,   1       ,   3	) * fracs[:, :, 1:]
       )
       UpperCAmelCase_							:      Union[str, Any]						=   directions / directions.norm(dim=-1       ,   keepdim=__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   torch.stack(
           [
               torch.broadcast_to(self.origin.view(__snake_case       ,   1       ,   3	)       ,   [batch_size, directions.shape[1], 3]	),
               directions,
           ]       ,   dim=2       ,   )
       return rays.view(__snake_case       ,   *__snake_case       ,   2       ,   3	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   int	):





       '''simple docstring'''
       assert width * self.height == height * self.width, "The aspect ratio should not change."
       return DifferentiableProjectiveCamera(
           origin=self.origin       ,   x=self.x       ,   y=self.y       ,   z=self.z       ,   width=__snake_case       ,   height=__snake_case       ,   x_fov=self.x_fov       ,   y_fov=self.y_fov       ,   )





def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[Any]						=   []
   UpperCAmelCase_							:      int						=   []
   UpperCAmelCase_							:      Any						=   []
   UpperCAmelCase_							:      Tuple						=   []
   for theta in np.linspace(0			,       2 * np.pi			,       num=2_0       ):
      UpperCAmelCase_							:      Tuple						=   np.array([np.sin(__lowercase       ), np.cos(__lowercase       ), -0.5]       )
      z /= np.sqrt(np.sum(z**2       )       )
      UpperCAmelCase_							:      str						=   -z * 4
      UpperCAmelCase_							:      List[Any]						=   np.array([np.cos(__lowercase       ), -np.sin(__lowercase       ), 0.0]       )
      UpperCAmelCase_							:      Tuple						=   np.cross(__lowercase			,       __lowercase       )
      origins.append(__lowercase       )
      xs.append(__lowercase       )
      ys.append(__lowercase       )
      zs.append(__lowercase       )
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       x=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       y=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       z=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       width=__lowercase			,       height=__lowercase			,       x_fov=0.7			,       y_fov=0.7			,       shape=(1, len(__lowercase       ))			,       )