




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE__       :      Tuple  =   128022
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   128028




@require_sentencepiece
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      MaMaaaTokenizer
    lowerCamelCase_      :	List[str]	     =      False
    lowerCamelCase_      :	Any	     =      False
    lowerCamelCase_      :	List[str]	     =      True







    def     _lowercase     (    self     )     ->      List[str]:
           super().setUp()

           lowerCamelCase   :      Union[str, Any]			 =					["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
           lowerCamelCase   :      str			 =					dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__     )     )     )     )
           lowerCamelCase   :      List[str]			 =					Path(self.tmpdirname     )
           save_json(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["vocab_file"]     )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                  copyfile(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["spm_file"]     )

           lowerCamelCase   :      Tuple			 =					MaMaaaTokenizer.from_pretrained(self.tmpdirname     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[Any]:
           return (
               "This is a test",
               "This is a test",
           )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					"</s>"
           lowerCamelCase   :      int			 =					0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__     )     ,  UpperCamelCase__     )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__     )     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Tuple			 =					self.get_tokenizer()
           lowerCamelCase   :      int			 =					list(tokenizer.get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]     ,  "</s>"     )
           self.assertEqual(vocab_keys[1]     ,  "<unk>"     )
           self.assertEqual(vocab_keys[-1]     ,  "<s>"     )
           self.assertEqual(len(UpperCamelCase__     )     ,  tokenizer.vocab_size + len(tokenizer.get_added_vocab()     )     )







    @unittest.skip("Skip this test while all models are still to be uploaded."     )
    def     _lowercase     (    self     )     ->      int:
           pass







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[Any]			 =					self.get_tokenizer()

           lowerCamelCase   :      Optional[Any]			 =					tokenizer.tokenize("This is a test"     )
           self.assertListEqual(UpperCamelCase__     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__     )     ,  [2, 3, 4, 5, 6]     ,  )

           lowerCamelCase   :      List[Any]			 =					tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]     )
           self.assertListEqual(UpperCamelCase__     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           lowerCamelCase   :      str			 =					tokenizer.convert_tokens_to_string(UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  "This is a test"     )







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           # fmt: off
           lowerCamelCase   :      Optional[int]			 =					{"input_ids": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__     ,  model_name="facebook/m2m100_418M"     ,  revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"     ,  )




@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """facebook/m2m100_418M"""
    lowerCamelCase_      :	Optional[int]	     =      [
        """In my opinion, there are two levels of response from the French government.""",
        """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
    ]
    lowerCamelCase_      :	List[Any]	     =      [
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
    ]

    # fmt: off
    lowerCamelCase_      :	List[str]	     =      [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]







    @classmethod
    def     _lowercase     (    cls     )     ->      List[Any]:
           lowerCamelCase   :      MaMaaaTokenizer			 =					MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name     ,  src_lang="en"     ,  tgt_lang="fr"     )
           lowerCamelCase   :      Dict			 =					1
           return cls







    def     _lowercase     (    self     )     ->      Tuple:
           self.assertEqual(self.tokenizer.get_lang_id("ar"     )     ,  12_8006     )
           self.assertEqual(self.tokenizer.get_lang_id("en"     )     ,  12_8022     )
           self.assertEqual(self.tokenizer.get_lang_id("ro"     )     ,  12_8076     )
           self.assertEqual(self.tokenizer.get_lang_id("mr"     )     ,  12_8063     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[str]			 =					self.tokenizer.get_vocab()
           self.assertEqual(len(UpperCamelCase__     )     ,  self.tokenizer.vocab_size     )
           self.assertEqual(vocab["<unk>"]     ,  3     )
           self.assertIn(self.tokenizer.get_lang_token("en"     )     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Any			 =					"en"
           lowerCamelCase   :      Optional[Any]			 =					self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           self.assertIn(UpperCamelCase__     ,  self.tokenizer.all_special_ids     )
           # fmt: off
           lowerCamelCase   :      Any			 =					[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
           # fmt: on
           lowerCamelCase   :      List[str]			 =					self.tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					self.tokenizer.decode(generated_ids[1:]     ,  skip_special_tokens=UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertNotIn(self.tokenizer.eos_token     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					tempfile.mkdtemp()
           lowerCamelCase   :      List[Any]			 =					self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					MaMaaaTokenizer.from_pretrained(UpperCamelCase__     )
           self.assertDictEqual(new_tok.lang_token_to_id     ,  UpperCamelCase__     )







    @require_torch
    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Any			 =					"en"
           lowerCamelCase   :      Optional[Any]			 =					"fr"

           lowerCamelCase   :      List[Any]			 =					self.tokenizer(self.src_text     ,  text_target=self.tgt_text     ,  padding=UpperCamelCase__     ,  return_tensors="pt"     )

           lowerCamelCase   :      Dict			 =					shift_tokens_right(
               batch["labels"]     ,  self.tokenizer.pad_token_id     ,  self.tokenizer.eos_token_id     )

           for k in batch:
                  lowerCamelCase   :      Any			 =					batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







    @require_torch
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Union[str, Any]			 =					"mr"
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("mr"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )

           lowerCamelCase   :      List[str]			 =					"zh"
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("zh"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )







    @require_torch
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					"mr"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("mr"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )

           lowerCamelCase   :      Dict			 =					"zh"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("zh"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )







    @require_torch
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      List[Any]			 =					self.tokenizer._build_translation_inputs("A test"     ,  return_tensors="pt"     ,  src_lang="en"     ,  tgt_lang="ar"     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  {
                   # en_XX, A, test, EOS
                   "input_ids": [[12_8022, 58, 4183, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 12_8006,
               }     ,  )












from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


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



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


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



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


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



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

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''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=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )


