
'''simple docstring'''


import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)

class 						__lowercase					(	unittest.TestCase     ):

			def __init__(self      , A      , A=1_3      , A=7      , A=True      , A=True      , A=True      , A=True      , A=9_9      , A=3_2      , A=5      , A=4      , A=3_7      , A="gelu"      , A=0.1      , A=0.1      , A=5_1_2      , A=1_6      , A=2      , A=0.02      , A=4      , ):
								lowerCamelCase_       :   Dict  =		parent
								lowerCamelCase_       :   Tuple  =		batch_size
								lowerCamelCase_       :   List[Any]  =		seq_length
								lowerCamelCase_       :   Union[str, Any]  =		is_training
								lowerCamelCase_       :   int  =		use_attention_mask
								lowerCamelCase_       :   Dict  =		use_token_type_ids
								lowerCamelCase_       :   int  =		use_labels
								lowerCamelCase_       :   Dict  =		vocab_size
								lowerCamelCase_       :   List[Any]  =		hidden_size
								lowerCamelCase_       :   Any  =		num_hidden_layers
								lowerCamelCase_       :   Optional[int]  =		num_attention_heads
								lowerCamelCase_       :   int  =		intermediate_size
								lowerCamelCase_       :   str  =		hidden_act
								lowerCamelCase_       :   List[Any]  =		hidden_dropout_prob
								lowerCamelCase_       :   Tuple  =		attention_probs_dropout_prob
								lowerCamelCase_       :   Tuple  =		max_position_embeddings
								lowerCamelCase_       :   List[str]  =		type_vocab_size
								lowerCamelCase_       :   List[str]  =		type_sequence_label_size
								lowerCamelCase_       :   str  =		initializer_range
								lowerCamelCase_       :   Any  =		num_choices

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   List[Any]  =		ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

								lowerCamelCase_       :   Dict  =		None
								if self.use_attention_mask:
													lowerCamelCase_       :   Tuple  =		random_attention_mask([self.batch_size, self.seq_length]   )

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

								lowerCamelCase_       :   Tuple  =		RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size      , hidden_size=self.hidden_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      , is_decoder=lowerCamelCase__      , initializer_range=self.initializer_range      , )

								return config, input_ids, token_type_ids, attention_mask

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   str  =		self.prepare_config_and_inputs()
								lowerCamelCase_       :   str  =		config_and_inputs
								lowerCamelCase_       :   Any  =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict






			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Any  =		self.prepare_config_and_inputs()
								lowerCamelCase_       :   Tuple  =		config_and_inputs

								lowerCamelCase_       :   List[Any]  =		True
								lowerCamelCase_       :   Optional[Any]  =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
								lowerCamelCase_       :   Dict  =		ids_tensor([self.batch_size, self.seq_length]      , vocab_size=2   )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						__lowercase					(	__UpperCAmelCase   ,  unittest.TestCase     ):
			lowerCamelCase      :		Any						   =      True

			lowerCamelCase      :		Dict						   =      (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   List[str]  =		FlaxRobertaPreLayerNormModelTester(self   )






			@slow
			def 							UpperCAmelCase__			(self   ):
								for model_class_name in self.all_model_classes:
													lowerCamelCase_       :   Optional[Any]  =		model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      , from_pt=lowerCamelCase__   )
													lowerCamelCase_       :   List[Any]  =		model(np.ones((1, 1)   )   )
													self.assertIsNotNone(lowerCamelCase__   )

@require_flax
class 						__lowercase					(	unittest.TestCase     ):

			@slow
			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   List[Any]  =		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      , from_pt=lowerCamelCase__   )

								lowerCamelCase_       :   Any  =		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      , dtype=jnp.intaa   )
								lowerCamelCase_       :   List[str]  =		model(lowerCamelCase__   )[0]
								lowerCamelCase_       :   Any  =		[1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape   )      , lowerCamelCase__   )
								# compare the actual values for a slice.
								lowerCamelCase_       :   Dict  =		np.array(
								    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]      , dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]      , lowerCamelCase__      , atol=1E-4   )   )






			@slow
			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Any  =		FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      , from_pt=lowerCamelCase__   )

								lowerCamelCase_       :   Dict  =		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      , dtype=jnp.intaa   )
								lowerCamelCase_       :   str  =		model(lowerCamelCase__   )[0]
								# compare the actual values for a slice.
								lowerCamelCase_       :   Optional[int]  =		np.array(
								    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]      , dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]      , lowerCamelCase__      , atol=1E-4   )   )






"""simple docstring"""


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
__snake_case    					=					'src/transformers'
__snake_case    					=					'docs/source/en'
__snake_case    					=					'.'


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Any  ):
 with open(lowerCamelCase__       ,		"""r"""       ,		encoding="""utf-8"""       ,		newline="""\n"""  ) as f:
  lowercase__       :       List[str]				=						f.readlines()
 # Find the start prompt.
 lowercase__       :       str				=						0
 while not lines[start_index].startswith(lowerCamelCase__  ):
  start_index += 1
 start_index += 1

 lowercase__       :       Optional[Any]				=						start_index
 while not lines[end_index].startswith(lowerCamelCase__  ):
  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 |
__snake_case    					=					'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__snake_case    					=					re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__snake_case    					=					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.
__snake_case    					=					re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__snake_case    					=					direct_transformers_import(TRANSFORMERS_PATH)


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]  ):
 lowercase__       :       Union[str, Any]				=						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""       ,		lowerCamelCase__  )
 return [m.group(0  ) for m in matches]


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							int  ):
 lowercase__       :       Any				=						2 if text == """✅""" or text == """❌""" else len(lowerCamelCase__  )
 lowercase__       :       int				=						(width - text_length) // 2
 lowercase__       :       List[str]				=						width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase__       :       Optional[int]				=						{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase__       :       str				=						{name: config.replace("""Config"""       ,		""""""  ) 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.
 lowercase__       :       Tuple				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[Any]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Dict				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						collections.defaultdict(lowerCamelCase__  )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(lowerCamelCase__  ):
  lowercase__       :       Optional[Any]				=						None
  if attr_name.endswith("""Tokenizer"""  ):
   lowercase__       :       List[Any]				=						slow_tokenizers
   lowercase__       :       List[str]				=						attr_name[:-9]
  elif attr_name.endswith("""TokenizerFast"""  ):
   lowercase__       :       Tuple				=						fast_tokenizers
   lowercase__       :       str				=						attr_name[:-13]
  elif _re_tf_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       Tuple				=						tf_models
   lowercase__       :       List[Any]				=						_re_tf_models.match(lowerCamelCase__  ).groups()[0]
  elif _re_flax_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       List[Any]				=						flax_models
   lowercase__       :       List[Any]				=						_re_flax_models.match(lowerCamelCase__  ).groups()[0]
  elif _re_pt_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       Union[str, Any]				=						pt_models
   lowercase__       :       Optional[int]				=						_re_pt_models.match(lowerCamelCase__  ).groups()[0]

  if lookup_dict is not None:
   while len(lowerCamelCase__  ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowercase__       :       Tuple				=						True
     break
    # Try again after removing the last word in the name
    lowercase__       :       Union[str, Any]				=						"""""".join(camel_case_split(lowerCamelCase__  )[:-1]  )

    # Let's build that table!
 lowercase__       :       Union[str, Any]				=						list(model_name_to_config.keys()  )
 model_names.sort(key=str.lower  )
 lowercase__       :       List[Any]				=						["""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).
 lowercase__       :       str				=						[len(lowerCamelCase__  ) + 2 for c in columns]
 lowercase__       :       Tuple				=						max([len(lowerCamelCase__  ) for name in model_names]  ) + 2

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

 lowercase__       :       List[Any]				=						{True: """✅""", False: """❌"""}
 for name in model_names:
  lowercase__       :       int				=						model_name_to_prefix[name]
  lowercase__       :       List[str]				=						[
      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(lowerCamelCase__       ,		lowerCamelCase__  ) for l, w in zip(lowerCamelCase__       ,		lowerCamelCase__  )]  ) + "|\n"
 return table


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]=False  ):
 lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						_find_text_in_file(
     filename=os.path.join(lowerCamelCase__       ,		"""index.md"""  )       ,		start_prompt="""<!--This table is updated automatically from the auto modules"""       ,		end_prompt="""<!-- End table-->"""       ,		)
 lowercase__       :       Union[str, Any]				=						get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(lowerCamelCase__       ,		"""index.md"""  )       ,		"""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.md` has not been updated. Run `make fix-copies` to fix this."""  )


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

   check_model_table(args.fix_and_overwrite)