





'''simple docstring'''






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 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      CodeGenTokenizer
   lowerCamelCase__    							=      CodeGenTokenizerFast
   lowerCamelCase__    							=      True
   lowerCamelCase__    							=      {'''add_prefix_space''': True}
   lowerCamelCase__    							=      False

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

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    snake_case__     :					Tuple    =  [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
        """<|endoftext|>""",
    ]
    snake_case__     :					Optional[Any]    =  dict(zip(__SCREAMING_SNAKE_CASE	,		range(len(__SCREAMING_SNAKE_CASE       )       )       )       )
    snake_case__     :					Optional[Any]    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    snake_case__     :					str    =  {"""unk_token""": """<unk>"""}

    snake_case__     :					Any    =  os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""vocab_file"""]       )
    snake_case__     :					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(__SCREAMING_SNAKE_CASE       ) + """\n"""       )
    with open(self.merges_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     fp.write("""\n""".join(__SCREAMING_SNAKE_CASE       )       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    kwargs.update(self.special_tokens_map       )
    return CodeGenTokenizer.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    kwargs.update(self.special_tokens_map       )
    return CodeGenTokenizerFast.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Tuple    =  """lower newer"""
    snake_case__     :					Optional[Any]    =  """lower newer"""
    return input_text, output_text

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  CodeGenTokenizer(self.vocab_file	,		self.merges_file	,		**self.special_tokens_map       )
    snake_case__     :					str    =  """lower newer"""
    snake_case__     :					List[str]    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    snake_case__     :					Union[str, Any]    =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE	,		add_prefix_space=__SCREAMING_SNAKE_CASE       )
    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  tokens + [tokenizer.unk_token]
    snake_case__     :					Any    =  [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    if not self.test_rust_tokenizer:
     return

    snake_case__     :					int    =  self.get_tokenizer()
    snake_case__     :					Union[str, Any]    =  self.get_rust_tokenizer(add_prefix_space=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Union[str, Any]    =  """lower newer"""

    # Testing tokenization
    snake_case__     :					Optional[Any]    =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE	,		add_prefix_space=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

    # Testing conversion to ids without special tokens
    snake_case__     :					Any    =  tokenizer.encode(__SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE	,		add_prefix_space=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE       )
    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

    # Testing conversion to ids with special tokens
    snake_case__     :					str    =  self.get_rust_tokenizer(add_prefix_space=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[Any]    =  tokenizer.encode(__SCREAMING_SNAKE_CASE	,		add_prefix_space=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       )
    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

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

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    # 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 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=1_5       ):
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
      snake_case__     :					Optional[Any]    =  self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

      # Simple input
      snake_case__     :					str    =  """This is a simple input"""
      snake_case__     :					Any    =  ["""This is a simple input 1""", """This is a simple input 2"""]
      snake_case__     :					Optional[int]    =  ("""This is a simple input""", """This is a pair""")
      snake_case__     :					Union[str, Any]    =  [
          ("""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(__SCREAMING_SNAKE_CASE	,		tokenizer_r.encode	,		__SCREAMING_SNAKE_CASE	,		max_length=__SCREAMING_SNAKE_CASE	,		padding="""max_length"""       )

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

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

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

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

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Any    =  CodeGenTokenizer.from_pretrained(self.tmpdirname	,		pad_token="""<pad>"""       )

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

    snake_case__     :					Optional[Any]    =  tokenizer.pad_token_id

    snake_case__     :					int    =  tokenizer(__SCREAMING_SNAKE_CASE	,		padding="""max_length"""	,		max_length=3_0	,		return_tensors="""np"""       )
    snake_case__     :					str    =  tokenizer(__SCREAMING_SNAKE_CASE	,		padding=__SCREAMING_SNAKE_CASE	,		truncate=__SCREAMING_SNAKE_CASE	,		return_tensors="""np"""       )
    snake_case__     :					Tuple    =  tokenizer(*__SCREAMING_SNAKE_CASE	,		padding="""max_length"""	,		max_length=6_0	,		return_tensors="""np"""       )
    snake_case__     :					Optional[Any]    =  tokenizer(__SCREAMING_SNAKE_CASE	,		padding=__SCREAMING_SNAKE_CASE	,		truncate=__SCREAMING_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 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  """$$$"""
    snake_case__     :					Dict    =  CodeGenTokenizer.from_pretrained(self.tmpdirname	,		bos_token=__SCREAMING_SNAKE_CASE	,		add_bos_token=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  """This is a simple input"""
    snake_case__     :					Optional[int]    =  ["""This is a simple input 1""", """This is a simple input 2"""]

    snake_case__     :					List[str]    =  tokenizer.bos_token_id

    snake_case__     :					List[str]    =  tokenizer(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  tokenizer(__SCREAMING_SNAKE_CASE       )

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

    snake_case__     :					str    =  tokenizer.decode(out_s.input_ids       )
    snake_case__     :					Optional[int]    =  tokenizer.batch_decode(out_sa.input_ids       )

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

   @slow
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""       )

    snake_case__     :					Optional[int]    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
    snake_case__     :					List[str]    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

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


   def 		__UpperCamelCase						(							self       ):
    pass










from typing import List, Optional, Tuple, Union

import torch

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

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

