




"""simple docstring"""



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
SCREAMING_SNAKE_CASE							=			"src/diffusers"
SCREAMING_SNAKE_CASE							=			"docs/source/en"
SCREAMING_SNAKE_CASE							=			"."







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Optional[int]:
 with open(lowercase_     ,					"r"     ,					encoding="utf-8"     ,					newline="\n"		) as f:
  A__			=							f.readlines()
 # Find the start prompt.
 A__			=							0
 while not lines[start_index].startswith(lowercase_		):
  start_index += 1
 start_index += 1

 A__			=							start_index
 while not lines[end_index].startswith(lowercase_		):
  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 |
SCREAMING_SNAKE_CASE							=			"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE							=			re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_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.
SCREAMING_SNAKE_CASE							=			re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						List[Any]:
 A__			=							re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"     ,					lowercase_		)
 return [m.group(0		) for m in matches]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						Dict:
 A__			=							2 if text == "✅" or text == "❌" else len(lowercase_		)
 A__			=							(width - text_length) // 2
 A__			=							width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent







def     _SCREAMING_SNAKE_CASE  (				)		->						Tuple:
 A__			=							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 A__			=							{
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 A__			=							{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.
 A__			=							collections.defaultdict(lowercase_		)
 A__			=							collections.defaultdict(lowercase_		)
 A__			=							collections.defaultdict(lowercase_		)
 A__			=							collections.defaultdict(lowercase_		)
 A__			=							collections.defaultdict(lowercase_		)

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

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

    # Let's build that table!
 A__			=							list(model_name_to_config.keys()		)
 model_names.sort(key=str.lower		)
 A__			=							["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).
 A__			=							[len(lowercase_		) + 2 for c in columns]
 A__			=							max([len(lowercase_		) for name in model_names]		) + 2

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

 A__			=							{True: "✅", False: "❌"}
 for name in model_names:
  A__			=							model_name_to_prefix[name]
  A__			=							[
      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(lowercase_     ,					lowercase_		) for l, w in zip(lowercase_     ,					lowercase_		)]		) + "|\n"
 return table







def     _SCREAMING_SNAKE_CASE  (				lowercase_=False		)		->						List[str]:
 A__, A__, A__, A__			=							_find_text_in_file(
     filename=os.path.join(lowercase_     ,					"index.mdx"		)     ,					start_prompt="<!--This table is updated automatically from the auto modules"     ,					end_prompt="<!-- End table-->"     ,					)
 A__			=							get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(lowercase_     ,					"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__":
      SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      SCREAMING_SNAKE_CASE							=			parser.parse_args()

      check_model_table(args.fix_and_overwrite)











"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.mpnet.modeling_tf_mpnet import (
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetModel,
      )




class       UpperCAmelCase_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			List[str]      ,					snake_case_		:			List[Any]=13      ,					snake_case_		:			int=7      ,					snake_case_		:			List[Any]=True      ,					snake_case_		:			Tuple=True      ,					snake_case_		:			Tuple=False      ,					snake_case_		:			str=True      ,					snake_case_		:			str=99      ,					snake_case_		:			int=64      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Optional[int]=4      ,					snake_case_		:			List[Any]=64      ,					snake_case_		:			Any="gelu"      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			Dict=512      ,					snake_case_		:			Dict=16      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Tuple=0.02      ,					snake_case_		:			Optional[Any]=3      ,					snake_case_		:			Dict=4      ,					snake_case_		:			List[str]=None      ,					) ->				Any:




     '''simple docstring'''

     A__			=							parent
     A__			=							batch_size
     A__			=							seq_length
     A__			=							is_training
     A__			=							use_input_mask
     A__			=							use_token_type_ids
     A__			=							use_labels
     A__			=							vocab_size
     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							max_position_embeddings
     A__			=							type_vocab_size
     A__			=							type_sequence_label_size
     A__			=							initializer_range
     A__			=							num_labels
     A__			=							num_choices
     A__			=							scope




    def 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

     A__			=							None
     if self.use_input_mask:
      A__			=							random_attention_mask([self.batch_size, self.seq_length] )

     A__			=							None
     A__			=							None
     A__			=							None
     if self.use_labels:
      A__			=							ids_tensor([self.batch_size]      ,					self.type_sequence_label_size )
      A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels )
      A__			=							ids_tensor([self.batch_size]      ,					self.num_choices )

     A__			=							MPNetConfig(
         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      ,					initializer_range=self.initializer_range      ,					)
     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			int      ,					snake_case_		:			int      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			Any      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetModel(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     A__			=							[input_ids, input_mask]
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			str      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetForMaskedLM(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Any      ,					snake_case_		:			str      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__			=							TFMPNetForQuestionAnswering(config=snake_case_ )
     A__			=							{
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.start_logits.shape      ,					(self.batch_size, self.seq_length) )
     self.parent.assertEqual(result.end_logits.shape      ,					(self.batch_size, self.seq_length) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Dict      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int] ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForSequenceClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels) )




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			Tuple      ,					snake_case_		:			int      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			str      ,					snake_case_		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.num_choices
     A__			=							TFMPNetForMultipleChoice(snake_case_ )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							{
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices) )




    def 				__magic_name__				(       self		:			List[str]      ,					snake_case_		:			str      ,					snake_case_		:			Tuple      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			List[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForTokenClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels) )




    def 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     A__			=							self.prepare_config_and_inputs()
     ((A__), (A__), (A__), (A__), (A__), (A__))			=							config_and_inputs
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (
        (
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetModel,
        )
        if is_tf_available()
        else ()
    )
    lowercase__		   = (
        {
            '''feature-extraction''': TFMPNetModel,
            '''fill-mask''': TFMPNetForMaskedLM,
            '''question-answering''': TFMPNetForQuestionAnswering,
            '''text-classification''': TFMPNetForSequenceClassification,
            '''token-classification''': TFMPNetForTokenClassification,
            '''zero-shot''': TFMPNetForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowercase__		   = False
    lowercase__		   = False




    def 				__magic_name__				(       self		:			Any ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModelTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					hidden_size=37 )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Tuple:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case_ )




    def 				__magic_name__				(       self		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case_ )




    def 				__magic_name__				(       self		:			int ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case_ )




    def 				__magic_name__				(       self		:			Tuple ) ->				Any:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case_ )




    @slow
    def 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     for model_name in ["microsoft/mpnet-base"]:
      A__			=							TFMPNetModel.from_pretrained(snake_case_ )
      self.assertIsNotNone(snake_case_ )






@require_tf
class       UpperCAmelCase_    (		unittest.TestCase				):




    @slow
    def 				__magic_name__				(       self		:			str ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModel.from_pretrained("microsoft/mpnet-base" )
     A__			=							tf.constant([[0, 1, 2, 3, 4, 5]] )
     A__			=							model(snake_case_ )[0]

     A__			=							[1, 6, 768]
     self.assertEqual(output.shape      ,					snake_case_ )

     A__			=							tf.constant(
         [
             [
                 [-0.106_7172, 0.0821_6473, 0.002_4543],
                 [-0.0346_5879, 0.835_4118, -0.0325_2288],
                 [-0.0656_9476, -0.1242_4111, -0.049_4436],
             ]
         ] )
     tf.debugging.assert_near(output[:, :3, :3]      ,					snake_case_      ,					atol=1e-4 )





