



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
_lowercase    	=		"src/transformers"
_lowercase    	=		"docs/source/en"
_lowercase    	=		"."



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

    A__					=       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 |
_lowercase    	=		"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase    	=		re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase    	=		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.
_lowercase    	=		re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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



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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str			,							UpperCamelCase_					:							Union[str, Any]						)->	Union[str, Any]:
    A__					=       2 if text == '''✅''' or text == '''❌''' else len(UpperCamelCase_						)
    A__					=       (width - text_length) // 2
    A__					=       width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 		lowerCAmelCase__     (       )->	Optional[int]:
    A__					=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    A__					=       {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    A__					=       {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.
    A__					=       collections.defaultdict(UpperCamelCase_						)
    A__					=       collections.defaultdict(UpperCamelCase_						)
    A__					=       collections.defaultdict(UpperCamelCase_						)
    A__					=       collections.defaultdict(UpperCamelCase_						)
    A__					=       collections.defaultdict(UpperCamelCase_						)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(UpperCamelCase_						):
        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[:-1_3]
        elif _re_tf_models.match(UpperCamelCase_						) is not None:
            A__					=       tf_models
            A__					=       _re_tf_models.match(UpperCamelCase_						).groups()[0]
        elif _re_flax_models.match(UpperCamelCase_						) is not None:
            A__					=       flax_models
            A__					=       _re_flax_models.match(UpperCamelCase_						).groups()[0]
        elif _re_pt_models.match(UpperCamelCase_						) is not None:
            A__					=       pt_models
            A__					=       _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():
                    A__					=       True
                    break
                # Try again after removing the last word in the name
                A__					=       ''''''.join(camel_case_split(UpperCamelCase_						)[:-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(UpperCamelCase_						) + 2 for c in columns]
    A__					=       max([len(UpperCamelCase_						) for name in model_names]						) + 2

    # Build the table per se
    A__					=       '''|''' + '''|'''.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"

    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(UpperCamelCase_			,							UpperCamelCase_						) for l, w in zip(UpperCamelCase_			,							UpperCamelCase_						)]						) + "|\n"
    return table



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]=False						)->	Optional[Any]:
    A__			,						A__			,						A__			,						A__					=       _find_text_in_file(
        filename=os.path.join(UpperCamelCase_			,							'''index.md'''						)			,							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(UpperCamelCase_			,							'''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__":
   _lowercase    	=		argparse.ArgumentParser()
   parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
   _lowercase    	=		parser.parse_args()

   check_model_table(args.fix_and_overwrite)





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 import (
       TFDebertaVaForMaskedLM,
       TFDebertaVaForQuestionAnswering,
       TFDebertaVaForSequenceClassification,
       TFDebertaVaForTokenClassification,
       TFDebertaVaModel,
   )



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=2			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=False			,    a__=True			,    a__="None"			,    a__=3			,    a__=4			,    a__=None			,    ):
         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__					=       relative_attention
         A__					=       position_biased_input
         A__					=       pos_att_type
         A__					=       scope




     def 		snake_case_		(					self):
         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
         if self.use_token_type_ids:
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

         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__					=       DebertaVaConfig(
             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			,    relative_attention=self.relative_attention			,    position_biased_input=self.position_biased_input			,    initializer_range=self.initializer_range			,    return_dict=a__			,    )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaModel(config=a__)
         A__					=       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A__					=       [input_ids, input_mask]
         A__					=       model(a__)

         A__					=       model(a__)

         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForMaskedLM(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForSequenceClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForTokenClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForQuestionAnswering(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         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 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       config_and_inputs
         A__					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict







@require_tf
class      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (
             TFDebertaVaModel,
             TFDebertaVaForMaskedLM,
             TFDebertaVaForQuestionAnswering,
             TFDebertaVaForSequenceClassification,
             TFDebertaVaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		     =					(
         {
             '''feature-extraction''': TFDebertaVaModel,
             '''fill-mask''': TFDebertaVaForMaskedLM,
             '''question-answering''': TFDebertaVaForQuestionAnswering,
             '''text-classification''': TFDebertaVaForSequenceClassification,
             '''token-classification''': TFDebertaVaForTokenClassification,
             '''zero-shot''': TFDebertaVaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    hidden_size=3_7)




     def 		snake_case_		(					self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__)




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         self.assertIsNotNone(a__)







@require_tf
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @unittest.skip(reason='''Model not available yet''')
     def 		snake_case_		(					self):
         pass




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         A__					=       tf.constant([[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]])
         A__					=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         A__					=       model(a__			,    attention_mask=a__)[0]

         A__					=       tf.constant(
             [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,    a__			,    atol=1e-4)
