




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCamelCase_  						=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Tuple     =  "google/flan-t5-base"
     _A    :   Optional[Any]     =  (
         "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
         "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
     )
     _A    :   Optional[int]     =  "text_qa"
     _A    :   Any     =  AutoTokenizer
     _A    :   int     =  AutoModelForSeqaSeqLM

     _A    :   Optional[int]     =  ["text", "text"]
     _A    :   Optional[int]     =  ["text"]




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =QA_PROMPT.format(text=_UpperCamelCase    ,question=_UpperCamelCase						)
         return self.pre_processor(_UpperCamelCase    ,return_tensors="""pt"""						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.model.generate(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =inputs["""input_ids"""].shape
         SCREAMING_SNAKE_CASE__       =output_ids.shape[0]

         return output_ids.reshape(_UpperCamelCase    ,out_b // in_b    ,*output_ids.shape[1:]						)[0][0]




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


         '''simple docstring'''





         return self.pre_processor.decode(_UpperCamelCase    ,skip_special_tokens=_UpperCamelCase    ,clean_up_tokenization_spaces=_UpperCamelCase						)












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}""")






