


'''simple docstring'''






import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_   :      Optional[int]				    =      "src/diffusers"
a_   :      int				    =      "."


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






def 		_A			(lowerCAmelCase__			,  lowerCAmelCase__						)				->					Union[str, Any]:




     '''simple docstring'''



     return line.startswith(lowerCAmelCase__						) or len(lowerCAmelCase__						) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$'			,  lowerCAmelCase__						) is not None






def 		_A			(lowerCAmelCase__						)				->					str:




     '''simple docstring'''



     _a      =							object_name.split('.'						)
     _a      =							0

     # First let's find the module where our object lives.
     _a      =							parts[i]
     while i < len(lowerCAmelCase__						) and not os.path.isfile(os.path.join(lowerCAmelCase__			,  f'{module}.py'						)						):
          i += 1
          if i < len(lowerCAmelCase__						):
               _a      =							os.path.join(lowerCAmelCase__			,  parts[i]						)
     if i >= len(lowerCAmelCase__						):
          raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'						)

     with open(os.path.join(lowerCAmelCase__			,  f'{module}.py'						)			,  'r'			,  encoding='utf-8'			,  newline='\n'						) as f:
          _a      =							f.readlines()

     # Now let's find the class / func in the code!
     _a      =							''
     _a      =							0
     for name in parts[i + 1 :]:
          while (
              line_index < len(lowerCAmelCase__						) and re.search(rf'^{indent}(class|def)\s+{name}(\(|\:)'			,  lines[line_index]						) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(lowerCAmelCase__						):
          raise ValueError(f' {object_name} does not match any function or class in {module}.'						)

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     _a      =							line_index
     while line_index < len(lowerCAmelCase__						) and _should_continue(lines[line_index]			,  lowerCAmelCase__						):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]						) <= 1:
          line_index -= 1

     _a      =							lines[start_index:line_index]
     return "".join(lowerCAmelCase__						)


a_   :      Union[str, Any]				    =      re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_   :      Tuple				    =      re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_   :      Dict				    =      re.compile(R"<FILL\s+[^>]*>")






def 		_A			(lowerCAmelCase__						)				->					int:




     '''simple docstring'''



     _a      =							code.split('\n'						)
     _a      =							0
     while idx < len(lowerCAmelCase__						) and len(lines[idx]						) == 0:
          idx += 1
     if idx < len(lowerCAmelCase__						):
          return re.search(r'^(\s*)\S'			,  lines[idx]						).groups()[0]
     return ""






def 		_A			(lowerCAmelCase__						)				->					Dict:




     '''simple docstring'''



     _a      =							len(get_indent(lowerCAmelCase__						)						) > 0
     if has_indent:
          _a      =							f'class Bla:\n{code}'
     _a      =							black.Mode(target_versions={black.TargetVersion.PYaa}			,  line_length=1_19			,  preview=lowerCAmelCase__						)
     _a      =							black.format_str(lowerCAmelCase__			,  mode=lowerCAmelCase__						)
     _a ,				_a      =							style_docstrings_in_code(lowerCAmelCase__						)
     return result[len('class Bla:\n'						) :] if has_indent else result






def 		_A			(lowerCAmelCase__			,  lowerCAmelCase__=False						)				->					Union[str, Any]:




     '''simple docstring'''



     with open(lowerCAmelCase__			,  'r'			,  encoding='utf-8'			,  newline='\n'						) as f:
          _a      =							f.readlines()
     _a      =							[]
     _a      =							0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(lowerCAmelCase__						):
          _a      =							_re_copy_warning.search(lines[line_index]						)
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          _a ,				_a ,				_a      =							search.groups()
          _a      =							find_code_in_diffusers(lowerCAmelCase__						)
          _a      =							get_indent(lowerCAmelCase__						)

          _a      =							line_index + 1 if indent == theoretical_indent else line_index + 2
          _a      =							theoretical_indent
          _a      =							start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          _a      =							True
          while line_index < len(lowerCAmelCase__						) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase__						):
                    break
               _a      =							lines[line_index]
               _a      =							_should_continue(lowerCAmelCase__			,  lowerCAmelCase__						) and re.search(f'^{indent}# End copy'			,  lowerCAmelCase__						) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]						) <= 1:
               line_index -= 1

          _a      =							lines[start_index:line_index]
          _a      =							''.join(lowerCAmelCase__						)

          # Remove any nested `Copied from` comments to avoid circular copies
          _a      =							[line for line in theoretical_code.split('\n'						) if _re_copy_warning.search(lowerCAmelCase__						) is None]
          _a      =							'\n'.join(lowerCAmelCase__						)

          # Before comparing, use the `replace_pattern` on the original code.
          if len(lowerCAmelCase__						) > 0:
               _a      =							replace_pattern.replace('with'			,  ''						).split(','						)
               _a      =							[_re_replace_pattern.search(lowerCAmelCase__						) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    _a ,				_a ,				_a      =							pattern.groups()
                    _a      =							re.sub(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
                    if option.strip() == "all-casing":
                         _a      =							re.sub(obja.lower()			,  obja.lower()			,  lowerCAmelCase__						)
                         _a      =							re.sub(obja.upper()			,  obja.upper()			,  lowerCAmelCase__						)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               _a      =							blackify(lines[start_index - 1] + theoretical_code						)
               _a      =							theoretical_code[len(lines[start_index - 1]						) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]						)
               if overwrite:
                    _a      =							lines[:start_index] + [theoretical_code] + lines[line_index:]
                    _a      =							start_index + 1

     if overwrite and len(lowerCAmelCase__						) > 0:
          # Warn the user a file has been modified.
          print(f'Detected changes, rewriting {filename}.'						)
          with open(lowerCAmelCase__			,  'w'			,  encoding='utf-8'			,  newline='\n'						) as f:
               f.writelines(lowerCAmelCase__						)
     return diffs






def 		_A			(lowerCAmelCase__ = False						)				->					Any:




     '''simple docstring'''



     _a      =							glob.glob(os.path.join(lowerCAmelCase__			,  '**/*.py'						)			,  recursive=lowerCAmelCase__						)
     _a      =							[]
     for filename in all_files:
          _a      =							is_copy_consistent(lowerCAmelCase__			,  lowerCAmelCase__						)
          diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(lowerCAmelCase__						) > 0:
          _a      =							'\n'.join(lowerCAmelCase__						)
          raise Exception(
              'Found the following copy inconsistencies:\n'
              + diff
              + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'						)


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

 check_copies(args.fix_and_overwrite)





'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=False ,			__magic_name__=True ,			__magic_name__="None" ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							Any:
      _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 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

      _a      =							None
      if self.use_input_mask:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2  )

      _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      =							ids_tensor([self.batch_size] ,			self.num_choices  )

      _a      =							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return DebertaConfig(
          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 ,			initializer_range=self.initializer_range ,			relative_attention=self.relative_attention ,			position_biased_input=self.position_biased_input ,			pos_att_type=self.pos_att_type ,			)
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.get_config()
      _a      =							3_00
      return config
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      self.parent.assertListEqual(list(result.loss.size()  ) ,			[]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__  )[0]

      self.parent.assertListEqual(list(sequence_output.size()  ) ,			[self.batch_size, self.seq_length, self.hidden_size]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaForMaskedLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )

      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							self.num_labels
      _a      =							DebertaForSequenceClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertListEqual(list(result.logits.size()  ) ,			[self.batch_size, self.num_labels]  )
      self.check_loss_output(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							self.num_labels
      _a      =							DebertaForTokenClassification(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							DebertaForQuestionAnswering(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(
          __magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			start_positions=__magic_name__ ,			end_positions=__magic_name__ ,			)
      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 							__UpperCAmelCase						(	self  )   ->							Any:
      _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_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							DebertaModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							DebertaModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )





@require_torch
@require_sentencepiece
@require_tokenizers
class 	a       (       unittest.TestCase    ):
 @unittest.skip(reason='Model not available yet'  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      pass




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							DebertaModel.from_pretrained('microsoft/deberta-base'  )

      _a      =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _a      =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      with torch.no_grad():
           _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )[0]
      # compare the actual values for a slice.
      _a      =							torch.tensor(
          [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]  )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,			__magic_name__ ,			atol=1e-4  ) ,			f'{output[:, 1:4, 1:4]}'  )

