
from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

SCREAMING_SNAKE_CASE__							=    {
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

SCREAMING_SNAKE_CASE__							=    datasets.load_dataset("""xnli""", split="""test+validation""")

SCREAMING_SNAKE_CASE__							=    0
SCREAMING_SNAKE_CASE__							=    0
SCREAMING_SNAKE_CASE__							=    0
SCREAMING_SNAKE_CASE__							=    0




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Any      )					->						Tuple:
				if spm_diff == list(reversed(SCREAMING_SNAKE_CASE      )      ):
								# AAA -> AA+A vs A+AA case.
								return True
				elif len(SCREAMING_SNAKE_CASE      ) == len(SCREAMING_SNAKE_CASE      ) and fast.decode(SCREAMING_SNAKE_CASE      ) == fast.decode(SCREAMING_SNAKE_CASE      ):
								# Second order OK
								# Barrich -> Barr + ich vs Bar + rich
								return True
				__lowercase								= slow.encode(slow.decode(SCREAMING_SNAKE_CASE      )      )
				__lowercase								= fast.encode(fast.decode(SCREAMING_SNAKE_CASE      )      )
				if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
								# Type 3 error.
								# Snehagatha ->
								#       Sne, h, aga, th, a
								#       Sne, ha, gat, ha
								# Encoding the wrong with sp does not even recover what spm gave us
								# It fits tokenizer however...
								return True
				return False




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				str      )					->						List[str]:
				__lowercase								= fast.encode_plus(SCREAMING_SNAKE_CASE      )[0]
				__lowercase								= enc.offsets
				__lowercase		, __lowercase								= offsets[idx], offsets[idx - 1]
				if curr is not None and line[curr[0] : curr[1]] == "\u200f":
								return True
				if prev is not None and line[prev[0] : prev[1]] == "\u200f":
								return True




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Any      )					->						Optional[int]:
				# Encoding can be the same with same result AAA -> A + AA vs AA + A
				# We can check that we use at least exactly the same number of tokens.
				for i, (spm_id, tok_id) in enumerate(zip(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )      ):
								if spm_id != tok_id:
												break
				__lowercase								= i
				for i, (spm_id, tok_id) in enumerate(zip(reversed(SCREAMING_SNAKE_CASE      )   ,							reversed(SCREAMING_SNAKE_CASE      )      )      ):
								if spm_id != tok_id:
												break
				__lowercase								= len(SCREAMING_SNAKE_CASE      ) - i

				__lowercase								= spm_ids[first:last]
				__lowercase								= tok_ids[first:last]

				if check_diff(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return True

				if check_LTR_mark(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return True

				if last - first > 5:
								# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
								__lowercase								= Counter(spm_ids[first:last]      )
								__lowercase								= Counter(tok_ids[first:last]      )

								__lowercase								= {spm_ for (spm_, si) in spms.items() if toks.get(spm_   ,							0      ) == si}
								__lowercase								= 3
								for i in range(last - first - min_width      ):
												if all(spm_ids[first + i + j] in removable_tokens for j in range(SCREAMING_SNAKE_CASE      )      ):
																__lowercase								= [
																    k
																    for k in range(last - first - min_width      )
																    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																]
																for j in possible_matches:
																				if check_diff(spm_ids[first : first + i]   ,							tok_ids[first : first + j]   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) and check_details(
																				    SCREAMING_SNAKE_CASE   ,							spm_ids[first + i : last]   ,							tok_ids[first + j : last]   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							):
																								return True

				print(F"""Spm: {[fast.decode([spm_ids[i]]      ) for i in range(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )]}"""      )
				try:
								print(F"""Tok: {[fast.decode([tok_ids[i]]      ) for i in range(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )]}"""      )
				except Exception:
								pass

				__lowercase								= fast.decode(spm_ids[:first]      )
				__lowercase								= fast.decode(spm_ids[last:]      )
				__lowercase								= fast.decode(spm_ids[first:last]      )
				print()
				print(SCREAMING_SNAKE_CASE      )
				return False




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						List[Any]:
				global perfect
				global imperfect
				global wrong
				global total

				__lowercase								= slow.encode(SCREAMING_SNAKE_CASE      )
				__lowercase								= fast.encode(SCREAMING_SNAKE_CASE      )

				__lowercase								= False
				total += 1

				if slow_ids != fast_ids:
								if check_details(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
												__lowercase								= True
												imperfect += 1
								else:
												wrong += 1
				else:
								perfect += 1

				if total % 10000 == 0:
								print(F"""({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"""      )

				if skip_assert:
								return

				assert (
				    slow_ids == fast_ids
				), F"""line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(SCREAMING_SNAKE_CASE      )}\n{fast.tokenize(SCREAMING_SNAKE_CASE      )}"""




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Dict:
				global batch_total
				for i in range(len(SCREAMING_SNAKE_CASE      )      ):
								# premise, all languages
								for text in dataset[i]["premise"].values():
												test_string(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )

								# hypothesis, all languages
								for text in dataset[i]["hypothesis"]["translation"]:
												test_string(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
							for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
														SCREAMING_SNAKE_CASE__							=    list(slow_class.max_model_input_sizes.keys())
														for checkpoint in checkpoint_names:
																					SCREAMING_SNAKE_CASE__							=    0
																					SCREAMING_SNAKE_CASE__							=    0
																					SCREAMING_SNAKE_CASE__							=    0
																					SCREAMING_SNAKE_CASE__							=    0

																					print(F'''========================== Checking {name}: {checkpoint} ==========================''')
																					SCREAMING_SNAKE_CASE__							=    slow_class.from_pretrained(checkpoint, force_download=True)
																					SCREAMING_SNAKE_CASE__							=    fast_class.from_pretrained(checkpoint, force_download=True)
																					test_tokenizer(slow, fast)
																					print(F'''Accuracy {perfect * 100 / total:.2f}''')







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Union[str, Any]    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





