




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_lowerCamelCase     :		List[Any]								=       get_logger(__name__)


class     UpperCamelCase_   (			enum.Enum					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'all_checks'
    UpperCAmelCase__							  =				'basic_checks'
    UpperCAmelCase__							  =				'no_checks'


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=None						)       ->	List[str]:





       if expected_checksums is None:
              logger.info('''Unable to verify checksums.'''						)
              return
       if len(set(lowercase_						) - set(lowercase_						)						) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(lowercase_						) - set(lowercase_						)						)						)
       if len(set(lowercase_						) - set(lowercase_						)						) > 0:
              raise UnexpectedDownloadedFile(str(set(lowercase_						) - set(lowercase_						)						)						)
       A__							=   [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       A__							=   ''' for ''' + verification_name if verification_name is not None else ''''''
       if len(lowercase_						) > 0:
              raise NonMatchingChecksumError(
                  f"""Checksums didn\'t match{for_verification_name}:\n"""
                  f"""{bad_urls}\n"""
                  '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''						)
       logger.info('''All the checksums matched successfully''' + for_verification_name						)


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass


class     UpperCamelCase_   (			__snake_case					):

    '''simple docstring'''






    pass







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Dict:





       if expected_splits is None:
              logger.info('''Unable to verify splits sizes.'''						)
              return
       if len(set(lowercase_						) - set(lowercase_						)						) > 0:
              raise ExpectedMoreSplits(str(set(lowercase_						) - set(lowercase_						)						)						)
       if len(set(lowercase_						) - set(lowercase_						)						) > 0:
              raise UnexpectedSplits(str(set(lowercase_						) - set(lowercase_						)						)						)
       A__							=   [
           {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(lowercase_						) > 0:
              raise NonMatchingSplitsSizesError(str(lowercase_						)						)
       logger.info('''All the splits matched successfully.'''						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = True						)       ->	dict:





       if record_checksum:
              A__							=   shaaaa()
              with open(lowercase_			,    '''rb'''						) as f:
                     for chunk in iter(lambda: f.read(1 << 20						)			,    b''''''						):
                            m.update(lowercase_						)
                     A__							=   m.hexdigest()
       else:
              A__							=   None
       return {"num_bytes": os.path.getsize(lowercase_						), "checksum": checksum}







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[str]:





       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


