



import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a_								=   """src/diffusers"""
a_								=   """docs/source/en"""
a_								=   """."""





def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	str	,_UpperCamelCase     :	List[str]							):



			with open(_UpperCamelCase	,'''r'''	,encoding='''utf-8'''	,newline='''\n'''							) as f:
						__lowerCamelCase  							=      f.readlines()
			# Find the start prompt.
			__lowerCamelCase  							=      0
			while not lines[start_index].startswith(_UpperCamelCase							):
						start_index += 1
			start_index += 1

			__lowerCamelCase  							=      start_index
			while not lines[end_index].startswith(_UpperCamelCase							):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]							) <= 1:
						start_index += 1
			while len(lines[end_index]							) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
a_								=   """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a_								=   re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a_								=   re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a_								=   re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
a_								=   importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
a_								=   spec.loader.load_module()





def 						a__     (						_UpperCamelCase     :	Optional[Any]							):
			__lowerCamelCase  							=      re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,_UpperCamelCase							)
			return [m.group(0							) for m in matches]





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	Optional[Any]							):
			__lowerCamelCase  							=      2 if text == '''✅''' or text == '''❌''' else len(_UpperCamelCase							)
			__lowerCamelCase  							=      (width - text_length) // 2
			__lowerCamelCase  							=      width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent





def 						a__     (						):
			__lowerCamelCase  							=      diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__lowerCamelCase  							=      {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			__lowerCamelCase  							=      {name: config.replace('''ConfigMixin'''	,''''''							) for name, config in model_name_to_config.items()}

			# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
			__lowerCamelCase  							=      collections.defaultdict(_UpperCamelCase							)
			__lowerCamelCase  							=      collections.defaultdict(_UpperCamelCase							)
			__lowerCamelCase  							=      collections.defaultdict(_UpperCamelCase							)
			__lowerCamelCase  							=      collections.defaultdict(_UpperCamelCase							)
			__lowerCamelCase  							=      collections.defaultdict(_UpperCamelCase							)

			# Let's lookup through all diffusers object (once).
			for attr_name in dir(_UpperCamelCase							):
						__lowerCamelCase  							=      None
						if attr_name.endswith('''Tokenizer'''							):
									__lowerCamelCase  							=      slow_tokenizers
									__lowerCamelCase  							=      attr_name[:-9]
						elif attr_name.endswith('''TokenizerFast'''							):
									__lowerCamelCase  							=      fast_tokenizers
									__lowerCamelCase  							=      attr_name[:-13]
						elif _re_tf_models.match(_UpperCamelCase							) is not None:
									__lowerCamelCase  							=      tf_models
									__lowerCamelCase  							=      _re_tf_models.match(_UpperCamelCase							).groups()[0]
						elif _re_flax_models.match(_UpperCamelCase							) is not None:
									__lowerCamelCase  							=      flax_models
									__lowerCamelCase  							=      _re_flax_models.match(_UpperCamelCase							).groups()[0]
						elif _re_pt_models.match(_UpperCamelCase							) is not None:
									__lowerCamelCase  							=      pt_models
									__lowerCamelCase  							=      _re_pt_models.match(_UpperCamelCase							).groups()[0]

						if lookup_dict is not None:
									while len(_UpperCamelCase							) > 0:
												if attr_name in model_name_to_prefix.values():
															__lowerCamelCase  							=      True
															break
												# Try again after removing the last word in the name
												__lowerCamelCase  							=      ''''''.join(camel_case_split(_UpperCamelCase							)[:-1]							)

    # Let's build that table!
			__lowerCamelCase  							=      list(model_name_to_config.keys()							)
			model_names.sort(key=str.lower							)
			__lowerCamelCase  							=      ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
			# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
			__lowerCamelCase  							=      [len(_UpperCamelCase							) + 2 for c in columns]
			__lowerCamelCase  							=      max([len(_UpperCamelCase							) for name in model_names]							) + 2

			# Build the table per se
			__lowerCamelCase  							=      '''|''' + '''|'''.join([_center_text(_UpperCamelCase	,_UpperCamelCase							) for c, w in zip(_UpperCamelCase	,_UpperCamelCase							)]							) + '''|\n'''
			# Use ":-----:" format to center-aligned table cell texts
			table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]							) + "|\n"

			__lowerCamelCase  							=      {True: '''✅''', False: '''❌'''}
			for name in model_names:
						__lowerCamelCase  							=      model_name_to_prefix[name]
						__lowerCamelCase  							=      [
						    name,
						    check[slow_tokenizers[prefix]],
						    check[fast_tokenizers[prefix]],
						    check[pt_models[prefix]],
						    check[tf_models[prefix]],
						    check[flax_models[prefix]],
						]
						table += "|" + "|".join([_center_text(_UpperCamelCase	,_UpperCamelCase							) for l, w in zip(_UpperCamelCase	,_UpperCamelCase							)]							) + "|\n"
			return table





def 						a__     (						_UpperCamelCase     :	int=False							):
			__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      _find_text_in_file(
			    filename=os.path.join(_UpperCamelCase	,'''index.mdx'''							)	,start_prompt='''<!--This table is updated automatically from the auto modules'''	,end_prompt='''<!-- End table-->'''	,)
			__lowerCamelCase  							=      get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(_UpperCamelCase	,'''index.mdx'''							)	,'''w'''	,encoding='''utf-8'''	,newline='''\n'''							) as f:
												f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
						else:
									raise ValueError(
									    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''							)


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

		check_model_table(args.fix_and_overwrite)











import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.electra.modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
  )



class 	__lowerCAmelCase       (		unittest.TestCase				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=13		,			__UpperCAmelCase=7		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=99		,			__UpperCAmelCase=24		,			__UpperCAmelCase=32		,			__UpperCAmelCase=5		,			__UpperCAmelCase=4		,			__UpperCAmelCase=37		,			__UpperCAmelCase="gelu"		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=512		,			__UpperCAmelCase=16		,			__UpperCAmelCase=2		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=4		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      seq_length
    __lowerCamelCase  							=      is_training
    __lowerCamelCase  							=      use_attention_mask
    __lowerCamelCase  							=      use_token_type_ids
    __lowerCamelCase  							=      use_labels
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      hidden_size
    __lowerCamelCase  							=      embedding_size
    __lowerCamelCase  							=      num_hidden_layers
    __lowerCamelCase  							=      num_attention_heads
    __lowerCamelCase  							=      intermediate_size
    __lowerCamelCase  							=      hidden_act
    __lowerCamelCase  							=      hidden_dropout_prob
    __lowerCamelCase  							=      attention_probs_dropout_prob
    __lowerCamelCase  							=      max_position_embeddings
    __lowerCamelCase  							=      type_vocab_size
    __lowerCamelCase  							=      type_sequence_label_size
    __lowerCamelCase  							=      initializer_range
    __lowerCamelCase  							=      num_choices




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

    __lowerCamelCase  							=      None
    if self.use_attention_mask:
       __lowerCamelCase  							=      random_attention_mask([self.batch_size, self.seq_length]			)

    __lowerCamelCase  							=      None
    if self.use_token_type_ids:
       __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size			)

    __lowerCamelCase  							=      ElectraConfig(
        vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			embedding_size=self.embedding_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			initializer_range=self.initializer_range		,			)

    return config, input_ids, token_type_ids, attention_mask




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      config_and_inputs
    __lowerCamelCase  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



@require_flax
class 	__lowerCAmelCase       (		lowerCAmelCase__					,		unittest.TestCase				):
 lowerCAmelCase__       =					True

 lowerCAmelCase__       =					(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      FlaxElectraModelTester(self			)




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       __lowerCamelCase  							=      model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__UpperCAmelCase			)






