

'''simple docstring'''






import argparse
from collections import defaultdict

import yaml


lowerCAmelCase__      		=			'''docs/source/en/_toctree.yml'''


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				defaultdict(A__   )
 __lowercase													=				[]
 __lowercase													=				[]
 for doc in doc_list:
  if "local" in doc:
   counts[doc["local"]] += 1

  if doc["title"].lower() == "overview":
   overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}   )
  else:
   new_doc_list.append(A__   )

 __lowercase													=				new_doc_list
 __lowercase													=				[key for key, value in counts.items() if value > 1]

 __lowercase													=				[]
 for duplicate_key in duplicates:
  __lowercase													=				list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key}   )
  if len(A__   ) > 1:
   raise ValueError(
       F"{duplicate_key} is present several times in the documentation table of content at "
       '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
       '''others.'''   )
  # Only add this once
  new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}   )

 # Add none duplicate-keys
 new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]   )
 __lowercase													=				sorted(A__  ,       key=lambda A__   : s["title"].lower()   )

 # "overview" gets special treatment and is always first
 if len(A__   ) > 1:
  raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''   )

 overview_doc.extend(A__   )

 # Sort
 return overview_doc


def 	_A  (					A__=False   ):






 """simple docstring"""



 with open(A__  ,       encoding='''utf-8'''   ) as f:
  __lowercase													=				yaml.safe_load(f.read()   )

 # Get to the API doc
 __lowercase													=				0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __lowercase													=				content[api_idx]['''sections''']

 # Then to the model doc
 __lowercase													=				0
 while api_doc[scheduler_idx]["title"] != "Schedulers":
  scheduler_idx += 1

 __lowercase													=				api_doc[scheduler_idx]['''sections''']
 __lowercase													=				clean_doc_toc(A__   )

 __lowercase													=				False
 if new_scheduler_doc != scheduler_doc:
  __lowercase													=				True
  if overwrite:
   __lowercase													=				new_scheduler_doc

 if diff:
  if overwrite:
   __lowercase													=				api_doc
   with open(A__  ,       '''w'''  ,       encoding='''utf-8'''   ) as f:
    f.write(yaml.dump(A__  ,       allow_unicode=A__   )   )
  else:
   raise ValueError(
       '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''   )


def 	_A  (					A__=False   ):






 """simple docstring"""



 with open(A__  ,       encoding='''utf-8'''   ) as f:
  __lowercase													=				yaml.safe_load(f.read()   )

 # Get to the API doc
 __lowercase													=				0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __lowercase													=				content[api_idx]['''sections''']

 # Then to the model doc
 __lowercase													=				0
 while api_doc[pipeline_idx]["title"] != "Pipelines":
  pipeline_idx += 1

 __lowercase													=				False
 __lowercase													=				api_doc[pipeline_idx]['''sections''']
 __lowercase													=				[]

 # sort sub pipeline docs
 for pipeline_doc in pipeline_docs:
  if "section" in pipeline_doc:
   __lowercase													=				pipeline_doc['''section''']
   __lowercase													=				clean_doc_toc(A__   )
   if overwrite:
    __lowercase													=				new_sub_pipeline_doc
  new_pipeline_docs.append(A__   )

 # sort overall pipeline doc
 __lowercase													=				clean_doc_toc(A__   )

 if new_pipeline_docs != pipeline_docs:
  __lowercase													=				True
  if overwrite:
   __lowercase													=				new_pipeline_docs

 if diff:
  if overwrite:
   __lowercase													=				api_doc
   with open(A__  ,       '''w'''  ,       encoding='''utf-8'''   ) as f:
    f.write(yaml.dump(A__  ,       allow_unicode=A__   )   )
  else:
   raise ValueError(
       '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''   )


if __name__ == "__main__":
    lowerCAmelCase__      		=			argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCAmelCase__      		=			parser.parse_args()

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)




'''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


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

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

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






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




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

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

   __lowercase													=				[
       '''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''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

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

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

