




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


lowerCamelCase_  						=       random.Random()



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase=1.0,    __UpperCamelCase=None,    __UpperCamelCase=None     ):
				if rng is None:
								SCREAMING_SNAKE_CASE__       =global_rng

				SCREAMING_SNAKE_CASE__       =[]
				for batch_idx in range(shape[0]     ):
								values.append([]     )
								for _ in range(shape[1]     ):
												values[-1].append(rng.random() * scale     )

				return values
@require_torch
class 				__a (			unittest.TestCase					):


					"""simple docstring"""


					def __init__(     self      :			Any    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Union[str, Any]=7    ,_UpperCamelCase      :			Any=4_0_0    ,_UpperCamelCase      :			Optional[int]=2_0_0_0    ,_UpperCamelCase      :			str=1    ,_UpperCamelCase      :			Optional[Any]=0.0    ,_UpperCamelCase      :			Union[str, Any]=2_4_0_0_0    ,_UpperCamelCase      :			str=True    ,)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =parent
									SCREAMING_SNAKE_CASE__       =batch_size
									SCREAMING_SNAKE_CASE__       =min_seq_length
									SCREAMING_SNAKE_CASE__       =max_seq_length
									SCREAMING_SNAKE_CASE__       =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE__       =feature_size
									SCREAMING_SNAKE_CASE__       =padding_value
									SCREAMING_SNAKE_CASE__       =sampling_rate
									SCREAMING_SNAKE_CASE__       =return_attention_mask




					def   __A    (     self      :			int						)		->       Dict:


									'''simple docstring'''





									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}




					def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			int=False    ,_UpperCamelCase      :			List[Any]=False						)		->       int:


									'''simple docstring'''





									def _flatten(_UpperCamelCase      :			int						):
													return list(itertools.chain(*_UpperCamelCase						)						)

									if equal_length:
													SCREAMING_SNAKE_CASE__       =floats_list((self.batch_size, self.max_seq_length)						)
									else:
													# make sure that inputs increase in size
													SCREAMING_SNAKE_CASE__       =[
													    _flatten(floats_list((x, self.feature_size)						)						)
													    for x in range(self.min_seq_length    ,self.max_seq_length    ,self.seq_length_diff						)
													]

									if numpify:
													SCREAMING_SNAKE_CASE__       =[np.asarray(_UpperCamelCase						) for x in audio_inputs]

									return audio_inputs






@require_torch
class 				__a (			__lowerCamelCase							,					unittest.TestCase					):


					"""simple docstring"""


					_A    :   Optional[Any]     =  EncodecFeatureExtractor




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =EnCodecFeatureExtractionTester(self						)




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE__       =[floats_list((1, x)						)[0] for x in range(8_0_0    ,1_4_0_0    ,2_0_0						)]
									SCREAMING_SNAKE_CASE__       =[np.asarray(_UpperCamelCase						) for audio_input in audio_inputs]

									# Test not batched input
									SCREAMING_SNAKE_CASE__       =feat_extract(audio_inputs[0]    ,return_tensors="""np"""						).input_values
									SCREAMING_SNAKE_CASE__       =feat_extract(np_audio_inputs[0]    ,return_tensors="""np"""						).input_values
									self.assertTrue(np.allclose(_UpperCamelCase    ,_UpperCamelCase    ,atol=1e-3						)						)

									# Test batched
									SCREAMING_SNAKE_CASE__       =feat_extract(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""np"""						).input_values
									SCREAMING_SNAKE_CASE__       =feat_extract(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""np"""						).input_values
									for enc_seq_a, enc_seq_a in zip(_UpperCamelCase    ,_UpperCamelCase						):
													self.assertTrue(np.allclose(_UpperCamelCase    ,_UpperCamelCase    ,atol=1e-3						)						)




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
									SCREAMING_SNAKE_CASE__       =np.random.rand(1_0_0						).astype(np.floataa						)
									SCREAMING_SNAKE_CASE__       =np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
													SCREAMING_SNAKE_CASE__       =feature_extractor.pad([{"""input_values""": inputs}]    ,return_tensors="""np"""						)
													self.assertTrue(np_processed.input_values.dtype == np.floataa						)
													SCREAMING_SNAKE_CASE__       =feature_extractor.pad([{"""input_values""": inputs}]    ,return_tensors="""pt"""						)
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)




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


									'''simple docstring'''





									from datasets import load_dataset

									SCREAMING_SNAKE_CASE__       =load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,"""clean"""    ,split="""validation"""						)
									# automatic decoding with librispeech
									SCREAMING_SNAKE_CASE__       =ds.sort("""id"""						).select(range(_UpperCamelCase						)						)[:num_samples]["""audio"""]

									return [x["array"] for x in audio_samples]




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =torch.tensor(
									    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
									     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
									     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
									     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
									     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
									     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]						)
									# fmt: on
									SCREAMING_SNAKE_CASE__       =self._load_datasamples(1						)
									SCREAMING_SNAKE_CASE__       =EncodecFeatureExtractor()
									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(input_values.shape    ,(1, 1, 9_3_6_8_0)						)
									self.assertTrue(torch.allclose(input_values[0, 0, :3_0]    ,_UpperCamelCase    ,atol=1e-6						)						)




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =torch.tensor(
									    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
									     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
									     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
									     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
									     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
									     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]						)
									# fmt: on
									SCREAMING_SNAKE_CASE__       =self._load_datasamples(1						)
									SCREAMING_SNAKE_CASE__       =[np.tile(input_audio[0][None]    ,reps=(2, 1)						)]
									input_audio[0][1] *= 0.5
									SCREAMING_SNAKE_CASE__       =EncodecFeatureExtractor(feature_size=2						)
									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(input_values.shape    ,(1, 2, 9_3_6_8_0)						)
									self.assertTrue(torch.allclose(input_values[0, 0, :3_0]    ,_UpperCamelCase    ,atol=1e-6						)						)
									self.assertTrue(torch.allclose(input_values[0, 1, :3_0]    ,EXPECTED_INPUT_VALUES * 0.5    ,atol=1e-6						)						)




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self._load_datasamples(2						)
									# would be easier if the stride was like
									SCREAMING_SNAKE_CASE__       =EncodecFeatureExtractor(feature_size=1    ,chunk_length_s=1    ,overlap=0.01						)

									# pad and trunc raise an error ?
									with self.assertRaisesRegex(
									    _UpperCamelCase    ,"""^Both padding and truncation were set. Make sure you only set one.$"""    ,):
													SCREAMING_SNAKE_CASE__       =feature_extractor(
													    _UpperCamelCase    ,padding="""max_length"""    ,truncation=_UpperCamelCase    ,return_tensors="""pt"""						).input_values

									# truncate to chunk
									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,truncation=_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(2, 1, 7_1_5_2_0)						)  # 2 chunks

									# force truncate to max_length
									SCREAMING_SNAKE_CASE__       =feature_extractor(
									    _UpperCamelCase    ,truncation=_UpperCamelCase    ,max_length=4_8_0_0_0    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(2, 1, 4_8_0_0_0)						)

									# pad to chunk
									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(padded_outputs.shape    ,(2, 1, 9_5_2_8_0)						)

									# pad to chunk
									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(2, 1, 9_5_2_8_0)						)

									# force pad to max length
									SCREAMING_SNAKE_CASE__       =feature_extractor(
									    _UpperCamelCase    ,padding="""max_length"""    ,max_length=1_0_0_0_0_0    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(2, 1, 1_0_0_0_0_0)						)

									# force no pad
									with self.assertRaisesRegex(
									    _UpperCamelCase    ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,):
													SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values

									SCREAMING_SNAKE_CASE__       =feature_extractor(input_audio[0]    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(1, 1, 9_3_6_8_0)						)

									# no pad if no chunk_length_s
									SCREAMING_SNAKE_CASE__       =None
									with self.assertRaisesRegex(
									    _UpperCamelCase    ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,):
													SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values

									SCREAMING_SNAKE_CASE__       =feature_extractor(input_audio[0]    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(1, 1, 9_3_6_8_0)						)

									# no pad if no overlap
									SCREAMING_SNAKE_CASE__       =2
									SCREAMING_SNAKE_CASE__       =None
									with self.assertRaisesRegex(
									    _UpperCamelCase    ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,):
													SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values

									SCREAMING_SNAKE_CASE__       =feature_extractor(input_audio[0]    ,padding=_UpperCamelCase    ,return_tensors="""pt"""						).input_values
									self.assertEquals(truncated_outputs.shape    ,(1, 1, 9_3_6_8_0)						)












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}""")






