
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			=  CodeGenTokenizer
 _A			=  CodeGenTokenizerFast
 _A			=  True
 _A			=  {"""add_prefix_space""": True}
 _A			=  False







 def      _lowerCamelCase       (      self	:List[str]		)	->				Dict:
        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[int]         =      dict(zip(snake_case_  ,     range(len(snake_case_		)		)		)		)
        __UpperCamelCase			:     int         =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        __UpperCamelCase			:     List[Any]         =      {"unk_token": "<unk>"}

        __UpperCamelCase			:     Dict         =      os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]		)
        __UpperCamelCase			:     List[Any]         =      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	:List[Any]  ,     **a	:List[str]		)	->				Any:
        kwargs.update(self.special_tokens_map		)
        return CodeGenTokenizer.from_pretrained(self.tmpdirname  ,     **snake_case_		)







 def      _lowerCamelCase       (      self	:Optional[int]  ,     **a	:List[str]		)	->				List[Any]:
        kwargs.update(self.special_tokens_map		)
        return CodeGenTokenizerFast.from_pretrained(self.tmpdirname  ,     **snake_case_		)







 def      _lowerCamelCase       (      self	:str  ,     a	:Dict		)	->				Any:
        __UpperCamelCase			:     List[str]         =      "lower newer"
        __UpperCamelCase			:     Optional[Any]         =      "lower newer"
        return input_text, output_text







 def      _lowerCamelCase       (      self	:Optional[Any]		)	->				str:
        __UpperCamelCase			:     Union[str, Any]         =      CodeGenTokenizer(self.vocab_file  ,     self.merges_file  ,     **self.special_tokens_map		)
        __UpperCamelCase			:     Union[str, Any]         =      "lower newer"
        __UpperCamelCase			:     Any         =      ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
        __UpperCamelCase			:     Optional[Any]         =      tokenizer.tokenize(snake_case_  ,     add_prefix_space=snake_case_		)
        self.assertListEqual(snake_case_  ,     snake_case_		)

        __UpperCamelCase			:     Union[str, Any]         =      tokens + [tokenizer.unk_token]
        __UpperCamelCase			:     Any         =      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_		)  ,     snake_case_		)







 def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Any:
        if not self.test_rust_tokenizer:
               return

        __UpperCamelCase			:     Optional[Any]         =      self.get_tokenizer()
        __UpperCamelCase			:     List[Any]         =      self.get_rust_tokenizer(add_prefix_space=snake_case_		)

        __UpperCamelCase			:     Tuple         =      "lower newer"

        # Testing tokenization
        __UpperCamelCase			:     Dict         =      tokenizer.tokenize(snake_case_  ,     add_prefix_space=snake_case_		)
        __UpperCamelCase			:     Tuple         =      rust_tokenizer.tokenize(snake_case_		)
        self.assertListEqual(snake_case_  ,     snake_case_		)

        # Testing conversion to ids without special tokens
        __UpperCamelCase			:     Optional[Any]         =      tokenizer.encode(snake_case_  ,     add_special_tokens=snake_case_  ,     add_prefix_space=snake_case_		)
        __UpperCamelCase			:     Optional[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			:     Any         =      self.get_rust_tokenizer(add_prefix_space=snake_case_		)
        __UpperCamelCase			:     Tuple         =      tokenizer.encode(snake_case_  ,     add_prefix_space=snake_case_		)
        __UpperCamelCase			:     Any         =      rust_tokenizer.encode(snake_case_		)
        self.assertListEqual(snake_case_  ,     snake_case_		)

        # Testing the unknown token
        __UpperCamelCase			:     Optional[Any]         =      tokens + [rust_tokenizer.unk_token]
        __UpperCamelCase			:     int         =      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case_		)  ,     snake_case_		)







 def      _lowerCamelCase       (      self	:Dict  ,     *a	:int  ,     **a	:Any		)	->				Tuple:
        # 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	:Optional[int]  ,     a	:Union[str, Any]=1_5		)	->				List[Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                      __UpperCamelCase			:     str         =      self.rust_tokenizer_class.from_pretrained(snake_case_  ,     **snake_case_		)

                      # Simple input
                      __UpperCamelCase			:     List[str]         =      "This is a simple input"
                      __UpperCamelCase			:     Any         =      ["This is a simple input 1", "This is a simple input 2"]
                      __UpperCamelCase			:     Union[str, Any]         =      ("This is a simple input", "This is a pair")
                      __UpperCamelCase			:     Dict         =      [
                          ("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	:Tuple		)	->				Dict:
        __UpperCamelCase			:     List[Any]         =      CodeGenTokenizer.from_pretrained(self.tmpdirname  ,     pad_token="<pad>"		)

        # Simple input
        __UpperCamelCase			:     int         =      "This is a simple input"
        __UpperCamelCase			:     int         =      ["This is a simple input looooooooong", "This is a simple input"]
        __UpperCamelCase			:     Any         =      ("This is a simple input", "This is a pair")
        __UpperCamelCase			:     Optional[int]         =      [
            ("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			:     Any         =      tokenizer(snake_case_  ,     padding="max_length"  ,     max_length=3_0  ,     return_tensors="np"		)
        __UpperCamelCase			:     Any         =      tokenizer(snake_case_  ,     padding=snake_case_  ,     truncate=snake_case_  ,     return_tensors="np"		)
        __UpperCamelCase			:     Any         =      tokenizer(*snake_case_  ,     padding="max_length"  ,     max_length=6_0  ,     return_tensors="np"		)
        __UpperCamelCase			:     Tuple         =      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]  ,     3_0		)
        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]  ,     3_3		)
        # 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]  ,     6_0		)
        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]  ,     5_2		)
        # 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	:List[Any]		)	->				List[str]:
        __UpperCamelCase			:     int         =      "$$$"
        __UpperCamelCase			:     List[str]         =      CodeGenTokenizer.from_pretrained(self.tmpdirname  ,     bos_token=snake_case_  ,     add_bos_token=snake_case_		)

        __UpperCamelCase			:     Union[str, Any]         =      "This is a simple input"
        __UpperCamelCase			:     str         =      ["This is a simple input 1", "This is a simple input 2"]

        __UpperCamelCase			:     Union[str, Any]         =      tokenizer.bos_token_id

        __UpperCamelCase			:     Dict         =      tokenizer(snake_case_		)
        __UpperCamelCase			:     Tuple         =      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			:     Optional[Any]         =      tokenizer.decode(out_s.input_ids		)
        __UpperCamelCase			:     Optional[Any]         =      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	:List[Any]		)	->				Optional[Any]:
        __UpperCamelCase			:     Union[str, Any]         =      CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"		)

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







 def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				List[str]:
        pass

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase						:      Any									= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return image
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     int         =      [image]

							if isinstance(image[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      image[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														__UpperCamelCase			:     Any         =      [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
														__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
														__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(image[0]		,						torch.Tensor):
														__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

							if isinstance(mask[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      mask[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__UpperCamelCase			:     List[str]         =      [np.array(m.convert("L").resize((w, h)		,						resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      mask.astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     List[str]         =      0
														__UpperCamelCase			:     Dict         =      1
														__UpperCamelCase			:     List[Any]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(mask[0]		,						torch.Tensor):
														__UpperCamelCase			:     Optional[Any]         =      torch.cat(_lowerCamelCase		,						dim=0)
							return mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
								__UpperCamelCase			:     Tuple         =      image

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(a  ,     a		) and len(a		) != batch_size:
															raise ValueError(
															    f'You have passed a list of generators of length {len(a		)}, but requested an effective batch'
															    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

								__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
								__UpperCamelCase			:     str         =      generator[0] if isinstance(a  ,     a		) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															if t < t_last:
																						# predict the noise residual
																						__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
																						# compute previous image: x_t -> x_t-1
																						__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

								__UpperCamelCase			:     Union[str, Any]         =      (image / 2 + 0.5).clamp(0  ,     1		)
								__UpperCamelCase			:     Tuple         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()
								if output_type == "pil":
															__UpperCamelCase			:     str         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)