
import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta 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							):
	__lowerCAmelCase  :     Optional[Any]        =			RobertaTokenizer
	__lowerCAmelCase  :     Union[str, Any]        =			RobertaTokenizerFast
	__lowerCAmelCase  :     Tuple        =			True
	__lowerCAmelCase  :     Any        =			{"cls_token": "<s>"}


	def      lowercase__     (					self):


			'''simple docstring'''




			super().setUp()

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

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


	def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			kwargs.update(self.special_tokens_map)
			return self.tokenizer_class.from_pretrained(self.tmpdirname					,  **A_)


	def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			kwargs.update(self.special_tokens_map)
			return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,  **A_)


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			lowercase__  :   Optional[Any]    		=      """lower newer"""
			lowercase__  :   int    		=      """lower newer"""
			return input_text, output_text


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   int    		=      self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map)
			lowercase__  :   Optional[Any]    		=      """lower newer"""
			lowercase__  :   Tuple    		=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
			lowercase__  :   List[str]    		=      tokenizer.tokenize(A_)  # , add_prefix_space=True)
			self.assertListEqual(A_					,  A_)

			lowercase__  :   Tuple    		=      tokens + [tokenizer.unk_token]
			lowercase__  :   str    		=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(A_)					,  A_)


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   str    		=      self.get_tokenizer()

			self.assertListEqual(tokenizer.encode("""Hello world!"""					,  add_special_tokens=A_)					,  [0, 3_14_14, 2_32, 3_28, 2])
			self.assertListEqual(
			    tokenizer.encode("""Hello world! cécé herlolip 418"""					,  add_special_tokens=A_)					,  [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]					,  )


	@slow
	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Dict    		=      self.tokenizer_class.from_pretrained("""roberta-base""")

			lowercase__  :   str    		=      tokenizer.encode("""sequence builders"""					,  add_special_tokens=A_)
			lowercase__  :   int    		=      tokenizer.encode("""multi-sequence build"""					,  add_special_tokens=A_)

			lowercase__  :   Dict    		=      tokenizer.encode(
			    """sequence builders"""					,  add_special_tokens=A_					,  add_prefix_space=A_)
			lowercase__  :   str    		=      tokenizer.encode(
			    """sequence builders"""					,  """multi-sequence build"""					,  add_special_tokens=A_					,  add_prefix_space=A_)

			lowercase__  :   Tuple    		=      tokenizer.build_inputs_with_special_tokens(A_)
			lowercase__  :   Tuple    		=      tokenizer.build_inputs_with_special_tokens(A_					,  A_)

			assert encoded_sentence == encoded_text_from_decode
			assert encoded_pair == encoded_pair_from_decode


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[Any]    		=      self.get_tokenizer()

			lowercase__  :   Tuple    		=      """Encode this sequence."""
			lowercase__  :   Optional[int]    		=      tokenizer.byte_encoder[""" """.encode("""utf-8""")[0]]

			# Testing encoder arguments
			lowercase__  :   Dict    		=      tokenizer.encode(A_					,  add_special_tokens=A_					,  add_prefix_space=A_)
			lowercase__  :   int    		=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
			self.assertNotEqual(A_					,  A_)

			lowercase__  :   Optional[int]    		=      tokenizer.encode(A_					,  add_special_tokens=A_					,  add_prefix_space=A_)
			lowercase__  :   int    		=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
			self.assertEqual(A_					,  A_)

			tokenizer.add_special_tokens({"""bos_token""": """<s>"""})
			lowercase__  :   Union[str, Any]    		=      tokenizer.encode(A_					,  add_special_tokens=A_)
			lowercase__  :   Optional[Any]    		=      tokenizer.convert_ids_to_tokens(encoded[1])[0]
			self.assertNotEqual(A_					,  A_)

			# Testing spaces after special tokens
			lowercase__  :   int    		=      """<mask>"""
			tokenizer.add_special_tokens(
			    {"""mask_token""": AddedToken(A_					,  lstrip=A_					,  rstrip=A_)})  # mask token has a left space
			lowercase__  :   int    		=      tokenizer.convert_tokens_to_ids(A_)

			lowercase__  :   Optional[Any]    		=      """Encode <mask> sequence"""
			lowercase__  :   Any    		=      """Encode <mask>sequence"""

			lowercase__  :   Tuple    		=      tokenizer.encode(A_)
			lowercase__  :   Union[str, Any]    		=      encoded.index(A_)
			lowercase__  :   int    		=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
			self.assertEqual(A_					,  A_)

			lowercase__  :   str    		=      tokenizer.encode(A_)
			lowercase__  :   List[str]    		=      encoded.index(A_)
			lowercase__  :   int    		=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
			self.assertNotEqual(A_					,  A_)


	def      lowercase__     (					self):


			'''simple docstring'''




			pass


	def      lowercase__     (					self):


			'''simple docstring'''




			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
							lowercase__  :   List[Any]    		=      self.rust_tokenizer_class.from_pretrained(A_					,  **A_)
							lowercase__  :   Dict    		=      self.tokenizer_class.from_pretrained(A_					,  **A_)
							lowercase__  :   Union[str, Any]    		=      """A, <mask> AllenNLP sentence."""
							lowercase__  :   str    		=      tokenizer_r.encode_plus(A_					,  add_special_tokens=A_					,  return_token_type_ids=A_)
							lowercase__  :   List[str]    		=      tokenizer_p.encode_plus(A_					,  add_special_tokens=A_					,  return_token_type_ids=A_)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r["""token_type_ids"""])					,  sum(tokens_p["""token_type_ids"""]))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])					,  sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])					,  )

							lowercase__  :   List[Any]    		=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
							lowercase__  :   List[str]    		=      tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p["""input_ids"""]					,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r["""input_ids"""]					,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    A_					,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
							self.assertSequenceEqual(
							    A_					,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])


	def      lowercase__     (					self):


			'''simple docstring'''




			for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2):
					lowercase__  :   str    		=      self.rust_tokenizer_class.from_pretrained(
					    self.tmpdirname					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)

					lowercase__  :   List[Any]    		=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
					lowercase__  :   Optional[Any]    		=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

					self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]					,  A_)

					self.assertEqual(post_processor_state["""add_prefix_space"""]					,  A_)
					self.assertEqual(post_processor_state["""trim_offsets"""]					,  A_)



	def      lowercase__     (					self):


			'''simple docstring'''




			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
							lowercase__  :   List[Any]    		=      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
							lowercase__  :   Optional[Any]    		=      f'{text_of_1_token} {text_of_1_token}'

							lowercase__  :   Tuple    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   Any    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (len(A_) + 1, len(A_) + 1 + len(A_))					,  )

							lowercase__  :   Union[str, Any]    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   Optional[int]    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (len(A_) + 1, len(A_) + 1 + len(A_))					,  )

							lowercase__  :   List[Any]    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   int    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (len(A_), len(A_) + 1 + len(A_))					,  )

							lowercase__  :   Tuple    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   Union[str, Any]    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (len(A_), len(A_) + 1 + len(A_))					,  )

							lowercase__  :   Tuple    		=      f' {text}'

							# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
							#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
							# )
							# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
							# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
							# self.assertEqual(
							#     encoding.offset_mapping[1],
							#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
							# )

							lowercase__  :   Union[str, Any]    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   List[Any]    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (1 + len(A_) + 1, 1 + len(A_) + 1 + len(A_))					,  )

							lowercase__  :   Optional[int]    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   str    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (1 + len(A_), 1 + len(A_) + 1 + len(A_))					,  )

							lowercase__  :   str    		=      self.rust_tokenizer_class.from_pretrained(
							    A_					,  use_fast=A_					,  add_prefix_space=A_					,  trim_offsets=A_)
							lowercase__  :   Any    		=      tokenizer_r(A_					,  return_offsets_mapping=A_					,  add_special_tokens=A_)
							self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A_)))
							self.assertEqual(
							    encoding.offset_mapping[1]					,  (1 + len(A_), 1 + len(A_) + 1 + len(A_))					,  )








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			2


 @register_to_config
 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_2					,  SCREAMING_SNAKE_CASE_ = 1_00					,  SCREAMING_SNAKE_CASE_ = 1.0_0_7					,  SCREAMING_SNAKE_CASE_ = 80					,  SCREAMING_SNAKE_CASE_ = 0.0_5					,  SCREAMING_SNAKE_CASE_ = 50					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

   # setable values
   lowercase__  :   int    		=      None
   lowercase__  :   np.IntTensor    		=      None
   lowercase__  :   torch.FloatTensor    		=      None  # sigma(t_i)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   return sample


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__  :   Dict    		=      num_inference_steps
   lowercase__  :   List[Any]    		=      np.arange(0					,  self.num_inference_steps)[::-1].copy()
   lowercase__  :   Optional[Any]    		=      torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      [
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa					,  device=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if self.config.s_min <= sigma <= self.config.s_max:
     lowercase__  :   Optional[Any]    		=      min(self.config.s_churn / self.num_inference_steps					,  2**0.5 - 1)
   else:
     lowercase__  :   Tuple    		=      0

   # sample eps ~ N(0, S_noise^2 * I)
   lowercase__  :   Dict    		=      self.config.s_noise * randn_tensor(sample.shape					,  generator=SCREAMING_SNAKE_CASE_).to(sample.device)
   lowercase__  :   int    		=      sigma + gamma * sigma
   lowercase__  :   Optional[int]    		=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[Any]    		=      sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()






