




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   List[Any]     =  "config.json"




     @register_to_config
     def __init__(     self      :			int    ,_UpperCamelCase      :			int=2    ,_UpperCamelCase      :			Optional[Any]=5    ,_UpperCamelCase      :			List[str]=(2, 5)    ,_UpperCamelCase      :			List[Any]="for diffusion"    ,_UpperCamelCase      :			Optional[int]=[1, 3]    ,)		->       Any:


         '''simple docstring'''





         pass






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   List[str]     =  "config.json"




     @register_to_config
     def __init__(     self      :			List[Any]    ,_UpperCamelCase      :			str=2    ,_UpperCamelCase      :			List[str]=5    ,_UpperCamelCase      :			Optional[Any]=(2, 5)    ,_UpperCamelCase      :			Union[str, Any]="for diffusion"    ,_UpperCamelCase      :			Tuple=[1, 3]    ,)		->       List[str]:


         '''simple docstring'''





         pass






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Optional[Any]     =  "config.json"




     @register_to_config
     def __init__(     self      :			str    ,_UpperCamelCase      :			Optional[int]=2    ,_UpperCamelCase      :			List[str]=5    ,_UpperCamelCase      :			List[Any]=(2, 5)    ,_UpperCamelCase      :			Dict="for diffusion"    ,_UpperCamelCase      :			Union[str, Any]=[1, 3]    ,_UpperCamelCase      :			Dict=[1, 3]    ,)		->       Any:


         '''simple docstring'''





         pass






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   List[Any]     =  "config.json"




     @register_to_config
     def __init__(     self      :			Union[str, Any]    ,_UpperCamelCase      :			str=2    ,_UpperCamelCase      :			List[str]=5    ,_UpperCamelCase      :			List[Any]=(2, 5)    ,_UpperCamelCase      :			int="for diffusion"    ,_UpperCamelCase      :			Any=[1, 5]    ,_UpperCamelCase      :			Optional[Any]=[5, 4]    ,)		->       List[Any]:


         '''simple docstring'''





         pass






class 				__a (			unittest.TestCase					):


     """simple docstring"""


     def   __A    (     self      :			Dict						)		->       Optional[Any]:


         '''simple docstring'''





         with self.assertRaises(_UpperCamelCase						):
             ConfigMixin.load_config("""dummy_path"""						)




     def   __A    (     self      :			int						)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SampleObject()
         SCREAMING_SNAKE_CASE__       =obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # init ignore private arguments
         SCREAMING_SNAKE_CASE__       =SampleObject(_name_or_path="""lalala"""						)
         SCREAMING_SNAKE_CASE__       =obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # can override default
         SCREAMING_SNAKE_CASE__       =SampleObject(c=6						)
         SCREAMING_SNAKE_CASE__       =obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # can use positional arguments.
         SCREAMING_SNAKE_CASE__       =SampleObject(1    ,c=6						)
         SCREAMING_SNAKE_CASE__       =obj.config
         assert config["a"] == 1
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SampleObject()
         SCREAMING_SNAKE_CASE__       =obj.config

         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         with tempfile.TemporaryDirectory() as tmpdirname:
             obj.save_config(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__       =SampleObject.from_config(SampleObject.load_config(_UpperCamelCase						)						)
             SCREAMING_SNAKE_CASE__       =new_obj.config

         # unfreeze configs
         SCREAMING_SNAKE_CASE__       =dict(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =dict(_UpperCamelCase						)

         assert config.pop("""c"""						) == (2, 5)  # instantiated as tuple
         assert new_config.pop("""c"""						) == [2, 5]  # saved & loaded as list because of json
         config.pop("""_use_default_values"""						)
         assert config == new_config




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =DDIMScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""						)

         assert ddim.__class__ == DDIMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""




     def   __A    (     self      :			Dict						)		->       Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =EulerDiscreteScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""						)

         assert euler.__class__ == EulerDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""




     def   __A    (     self      :			str						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =EulerAncestralDiscreteScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""						)

         assert euler.__class__ == EulerAncestralDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""




     def   __A    (     self      :			str						)		->       Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =PNDMScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""						)

         assert pndm.__class__ == PNDMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""




     def   __A    (     self      :			Optional[int]						)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =DDPMScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""    ,prediction_type="""sample"""    ,beta_end=8    ,)

         with CaptureLogger(_UpperCamelCase						) as cap_logger_a:
             SCREAMING_SNAKE_CASE__       =DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""    ,beta_start=8_8						)

         assert ddpm.__class__ == DDPMScheduler
         assert ddpm.config.prediction_type == "sample"
         assert ddpm.config.beta_end == 8
         assert ddpm_a.config.beta_start == 8_8

         # no warning should be thrown
         assert cap_logger.out == ""
         assert cap_logger_a.out == ""




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =logging.get_logger("""diffusers.configuration_utils"""						)
         # 30 for warning
         logger.setLevel(3_0						)

         with CaptureLogger(_UpperCamelCase						) as cap_logger:
             SCREAMING_SNAKE_CASE__       =DPMSolverMultistepScheduler.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-torch"""    ,subfolder="""scheduler"""						)

         assert dpm.__class__ == DPMSolverMultistepScheduler
         # no warning should be thrown
         assert cap_logger.out == ""




     def   __A    (     self      :			str						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SampleObject()

         SCREAMING_SNAKE_CASE__       ={k: v for k, v in config.config.items() if not k.startswith("""_"""						)}

         # make sure that default config has all keys in `_use_default_values`
         assert set(config_dict.keys()						) == set(config.config._use_default_values						)

         with tempfile.TemporaryDirectory() as tmpdirname:
             config.save_config(_UpperCamelCase						)

             # now loading it with SampleObject2 should put f into `_use_default_values`
             SCREAMING_SNAKE_CASE__       =SampleObjecta.from_config(_UpperCamelCase						)

             assert "f" in config._use_default_values
             assert config.f == [1, 3]

         # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
         # **BECAUSE** it is part of `config._use_default_values`
         SCREAMING_SNAKE_CASE__       =SampleObjecta.from_config(config.config						)
         assert new_config.f == [5, 4]

         config.config._use_default_values.pop()
         SCREAMING_SNAKE_CASE__       =SampleObjecta.from_config(config.config						)
         assert new_config_a.f == [1, 3]

         # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
         assert new_config_a.e == [1, 3]












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()

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

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

lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    if spm_diff == list(reversed(__UpperCamelCase     )     ):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(__UpperCamelCase     ) == len(__UpperCamelCase     ) and fast.decode(__UpperCamelCase     ) == fast.decode(__UpperCamelCase     ):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    SCREAMING_SNAKE_CASE__       =slow.encode(slow.decode(__UpperCamelCase     )     )
    SCREAMING_SNAKE_CASE__       =fast.encode(fast.decode(__UpperCamelCase     )     )
    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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =fast.encode_plus(__UpperCamelCase     )[0]
    SCREAMING_SNAKE_CASE__       =enc.offsets
    SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    # 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(__UpperCamelCase,    __UpperCamelCase     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase     ),    reversed(__UpperCamelCase     )     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     ) - i

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[:first]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[last:]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[first:last]     )
    print()
    print(__UpperCamelCase     )
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    global perfect
    global imperfect
    global wrong
    global total

    SCREAMING_SNAKE_CASE__       =slow.encode(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =fast.encode(__UpperCamelCase     )

    SCREAMING_SNAKE_CASE__       =False
    total += 1

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

    if total % 10_000 == 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(__UpperCamelCase     )}\n{fast.tokenize(__UpperCamelCase     )}"""



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    global batch_total
    for i in range(len(__UpperCamelCase     )     ):
        # premise, all languages
        for text in dataset[i]["premise"].values():
            test_string(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )

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


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

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






