"""simple docstring"""



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__:Dict			=	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__:Optional[Any]			=	128022
SCREAMING_SNAKE_CASE__:List[Any]			=	128028






@require_sentencepiece
class 				snake_case__     (   A_,		unittest.TestCase   ):
 _snake_case   :			Tuple           =		MaMaaaTokenizer
 _snake_case   :			Optional[Any]           =		False
 _snake_case   :			Optional[int]           =		False
 _snake_case   :			Union[str, Any]           =		True


 def 		a__ (				self		):
        super().setUp()

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

        __a     				=  MaMaaaTokenizer.from_pretrained(self.tmpdirname		)
        tokenizer.save_pretrained(self.tmpdirname		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        return MaMaaaTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return (
            "This is a test",
            "This is a test",
        )


 def 		a__ (				self		):
        __a     				=  "</s>"
        __a     				=  0

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


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        __a     				=  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(snake_case__		)  ,						tokenizer.vocab_size + len(tokenizer.get_added_vocab()		)		)


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


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()

        __a     				=  tokenizer.tokenize("This is a test"		)
        self.assertListEqual(snake_case__  ,						["▁This", "▁is", "▁a", "▁t", "est"]		)

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

        __a     				=  tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]		)
        self.assertListEqual(snake_case__  ,						["▁This", "▁is", "▁a", "▁t", "est"]		)

        __a     				=  tokenizer.convert_tokens_to_string(snake_case__		)
        self.assertEqual(snake_case__  ,						"This is a test"		)






 @slow
 def 		a__ (				self		):
        __a     				=  {"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 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=snake_case__  ,						model_name="facebook/m2m100_418M"  ,						revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"  ,						)






@require_torch
@require_sentencepiece
@require_tokenizers
class 				snake_case__     (   unittest.TestCase   ):
 _snake_case   :			int           =		"facebook/m2m100_418M"
 _snake_case   :			int           =		[
     "In my opinion, there are two levels of response from the French government.",
     "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
 ]
 _snake_case   :			Union[str, 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
 _snake_case   :			Optional[Any]           =		[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]


 @classmethod
 def 		a__ (				cls		):
        __a     				=  MaMaaaTokenizer.from_pretrained(
            cls.checkpoint_name  ,						src_lang="en"  ,						tgt_lang="fr"		)
        __a     				=  1
        return cls


 def 		a__ (				self		):
        self.assertEqual(self.tokenizer.get_lang_id("ar"		)  ,						128006		)
        self.assertEqual(self.tokenizer.get_lang_id("en"		)  ,						128022		)
        self.assertEqual(self.tokenizer.get_lang_id("ro"		)  ,						128076		)
        self.assertEqual(self.tokenizer.get_lang_id("mr"		)  ,						128063		)


 def 		a__ (				self		):
        __a     				=  self.tokenizer.get_vocab()
        self.assertEqual(len(snake_case__		)  ,						self.tokenizer.vocab_size		)
        self.assertEqual(vocab["<unk>"]  ,						3		)
        self.assertIn(self.tokenizer.get_lang_token("en"		)  ,						snake_case__		)


 def 		a__ (				self		):
        __a     				=  "en"
        __a     				=  self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
        self.assertListEqual(self.expected_src_tokens  ,						snake_case__		)


 def 		a__ (				self		):
        self.assertIn(snake_case__  ,						self.tokenizer.all_special_ids		)
        # fmt: off
        __a     				=  [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
        # fmt: on
        __a     				=  self.tokenizer.decode(snake_case__  ,						skip_special_tokens=snake_case__		)
        __a     				=  self.tokenizer.decode(generated_ids[1:]  ,						skip_special_tokens=snake_case__		)
        self.assertEqual(snake_case__  ,						snake_case__		)
        self.assertNotIn(self.tokenizer.eos_token  ,						snake_case__		)


 def 		a__ (				self		):
        __a     				=  tempfile.mkdtemp()
        __a     				=  self.tokenizer.lang_token_to_id
        self.tokenizer.save_pretrained(snake_case__		)
        __a     				=  MaMaaaTokenizer.from_pretrained(snake_case__		)
        self.assertDictEqual(new_tok.lang_token_to_id  ,						snake_case__		)


 @require_torch
 def 		a__ (				self		):
        __a     				=  "en"
        __a     				=  "fr"

        __a     				=  self.tokenizer(self.src_text  ,						text_target=self.tgt_text  ,						padding=snake_case__  ,						return_tensors="pt"		)

        __a     				=  shift_tokens_right(
            batch["labels"]  ,						self.tokenizer.pad_token_id  ,						self.tokenizer.eos_token_id		)

        for k in batch:
               __a     				=  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 		a__ (				self		):
        __a     				=  "mr"
        self.assertListEqual(self.tokenizer.prefix_tokens  ,						[self.tokenizer.get_lang_id("mr"		)]		)
        self.assertListEqual(self.tokenizer.suffix_tokens  ,						[self.tokenizer.eos_token_id]		)

        __a     				=  "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 		a__ (				self		):
        __a     				=  "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		)]		)

        __a     				=  "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 		a__ (				self		):
        __a     				=  self.tokenizer._build_translation_inputs("A test"  ,						return_tensors="pt"  ,						src_lang="en"  ,						tgt_lang="ar"		)

        self.assertEqual(
            nested_simplify(snake_case__		)  ,						{
                # en_XX, A, test, EOS
                "input_ids": [[128022, 58, 4183, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 128006,
            }  ,						)

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
