
"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


a					:	Optional[int]							=		['''gpt2''']
a					:	List[str]							=		'''gpt2'''

if is_tf_available():




 class 		a_						( tf.Module	):







     def __init__(			self			:       str     ,					__UpperCamelCase			:       List[str]     )		->Tuple:






         '''simple docstring'''






         super().__init__()
         _UpperCAmelCase          =   tokenizer
         _UpperCAmelCase          =   AutoConfig.from_pretrained(__UpperCamelCase     )
         _UpperCAmelCase          =   TFGPTaLMHeadModel.from_config(__UpperCamelCase     )







     @tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""     ),)     )
     def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[str]     )		->int:






         '''simple docstring'''






         _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     )
         _UpperCAmelCase          =   tokenized["""input_ids"""].to_tensor()

         _UpperCAmelCase          =   tf.cast(input_ids_dense > 0     ,					tf.intaa     )
         # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

         _UpperCAmelCase          =   self.model(input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     )["""logits"""]

         return outputs




@require_tf
@require_keras_nlp
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        super().setUp()

        _UpperCAmelCase          =   [GPTaTokenizer.from_pretrained(__UpperCamelCase     ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
        _UpperCAmelCase          =   [TFGPTaTokenizer.from_pretrained(__UpperCamelCase     ) for checkpoint in TOKENIZER_CHECKPOINTS]
        assert len(self.tokenizers     ) == len(self.tf_tokenizers     )

        _UpperCAmelCase          =   [
            """This is a straightforward English test sentence.""",
            """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
            """Now we're going to add some Chinese: 一 二 三 一二三""",
            """And some much more rare Chinese: 齉 堃 齉堃""",
            """Je vais aussi écrire en français pour tester les accents""",
            """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
        ]
        _UpperCAmelCase          =   list(zip(self.test_sentences     ,					self.test_sentences[::-1]     )     )







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






        '''simple docstring'''






        for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers     ):
            for test_inputs in self.test_sentences:
                _UpperCAmelCase          =   tokenizer([test_inputs]     ,					return_tensors="""tf"""     )
                _UpperCAmelCase          =   tf_tokenizer([test_inputs]     )

                for key in python_outputs.keys():
                    # convert them to numpy to avoid messing with ragged tensors
                    _UpperCAmelCase          =   python_outputs[key].numpy()
                    _UpperCAmelCase          =   tf_outputs[key].numpy()

                    self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape     )     )
                    self.assertTrue(tf.reduce_all(tf.cast(__UpperCamelCase     ,					tf.intaa     ) == tf_outputs_values     )     )







    @slow
    def      _snake_case					(			self			:       int     )		->List[Any]:






        '''simple docstring'''






        for tf_tokenizer in self.tf_tokenizers:
            _UpperCAmelCase          =   tf.function(__UpperCamelCase     )
            for test_inputs in self.test_sentences:
                _UpperCAmelCase          =   tf.constant(__UpperCamelCase     )
                _UpperCAmelCase          =   compiled_tokenizer(__UpperCamelCase     )
                _UpperCAmelCase          =   tf_tokenizer(__UpperCamelCase     )

                for key in eager_outputs.keys():
                    self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]     )     )







    @slow
    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






        for tf_tokenizer in self.tf_tokenizers:
            _UpperCAmelCase          =   ModelToSave(tokenizer=__UpperCamelCase     )
            _UpperCAmelCase          =   tf.convert_to_tensor([self.test_sentences[0]]     )
            _UpperCAmelCase          =   model.serving(__UpperCamelCase     )  # Build model with some sample inputs
            with TemporaryDirectory() as tempdir:
                _UpperCAmelCase          =   Path(__UpperCamelCase     ) / """saved.model"""
                tf.saved_model.save(__UpperCamelCase     ,					__UpperCamelCase     ,					signatures={"""serving_default""": model.serving}     )
                _UpperCAmelCase          =   tf.saved_model.load(__UpperCamelCase     )
            _UpperCAmelCase          =   loaded_model.signatures["""serving_default"""](__UpperCamelCase     )["""output_0"""]
            # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
            self.assertTrue(tf.reduce_all(out == loaded_output     )     )







    @slow
    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






        for tf_tokenizer in self.tf_tokenizers:
            _UpperCAmelCase          =   tf.convert_to_tensor([self.test_sentences[0]]     )
            _UpperCAmelCase          =   tf_tokenizer(__UpperCamelCase     )  # Build model with some sample inputs

            _UpperCAmelCase          =   tf_tokenizer.get_config()
            _UpperCAmelCase          =   TFGPTaTokenizer.from_config(__UpperCamelCase     )
            _UpperCAmelCase          =   model_from_config(__UpperCamelCase     )

            for key in from_config_output.keys():
                self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]     )     )







    @slow
    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        for tf_tokenizer in self.tf_tokenizers:
            # for the test to run
            _UpperCAmelCase          =   12_31_23

            for max_length in [3, 5, 10_24]:
                _UpperCAmelCase          =   tf.convert_to_tensor([self.test_sentences[0]]     )
                _UpperCAmelCase          =   tf_tokenizer(__UpperCamelCase     ,					max_length=__UpperCamelCase     )

                _UpperCAmelCase          =   out["""input_ids"""].numpy().shape[1]

                assert out_length == max_length

"""simple docstring"""



import argparse
import os
import re

import packaging.version


a					:	str							=		'''examples/'''
a					:	List[str]							=		{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a					:	Tuple							=		{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
a					:	List[str]							=		'''README.md'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase    ,_UpperCAmelCase          =   REPLACE_PATTERNS[pattern]
    _UpperCAmelCase          =   replace.replace("""VERSION"""					,      _A      )
    _UpperCAmelCase          =   re_pattern.sub(_A					,      _A      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.write(_A      )


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





    """simple docstring"""

    for folder, directories, fnames in os.walk(_A      ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""      )
        if "legacy" in directories:
            directories.remove("""legacy"""      )
        for fname in fnames:
            if fname.endswith(""".py"""      ):
                update_version_in_file(os.path.join(_A					,      _A      )					,      _A					,      pattern="""examples"""      )


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  int:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_A					,      _A					,      _A      )
    if not patch:
        update_version_in_examples(_A      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """🤗 Transformers currently provides the following architectures"""
    _UpperCAmelCase          =   """1. Want to contribute a new model?"""
    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.readlines()

    # Find the start of the list.
    _UpperCAmelCase          =   0
    while not lines[start_index].startswith(_start_prompt      ):
        start_index += 1
    start_index += 1

    _UpperCAmelCase          =   start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt      ):
        if lines[index].startswith("""1."""      ):
            _UpperCAmelCase          =   lines[index].replace(
                """https://huggingface.co/docs/diffusers/main/model_doc"""					,      """https://huggingface.co/docs/diffusers/model_doc"""					,      )
        index += 1

    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.writelines(_A      )


def  _UpperCamelCase	(						)    ->  Optional[int]:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]					,      """r"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase          =   REPLACE_PATTERNS["""init"""][0].search(_A      ).groups()[0]
    return packaging.version.parse(_A      )


def  _UpperCamelCase	(						_A=False      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
    if default_version.is_devrelease:
        _UpperCAmelCase          =   default_version.base_version
    elif patch:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor + 1}.0"""

    # Now let's ask nicely if that's the right one.
    _UpperCAmelCase          =   input(F"""Which version are you releasing? [{default_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   default_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A					,      patch=_A      )


def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    _UpperCAmelCase          =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _UpperCAmelCase          =   current_version.base_version

    # Check with the user we got that right.
    _UpperCAmelCase          =   input(F"""Which version are we developing now? [{dev_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   dev_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
 parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
 a					:	Tuple							=		parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print('''Nothing to do after a patch :-)''')
 else:
  post_release_work()