





'''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
lowerCAmelCase :					Tuple 							='''src/diffusers'''
lowerCAmelCase :					Dict 							='''.'''


# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase :					Any 							=importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase :					Optional[Any] 							=spec.loader.load_module()
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any			):
   return line.startswith(__lowerCamelCase			) or len(__lowerCamelCase			) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"     ,__lowerCamelCase			) is not None
def 							UpperCAmelCase_  (						__lowerCamelCase : int			):
   lowercase_      :Any         =			object_name.split("."			)
   lowercase_      :Any         =			0

   # First let's find the module where our object lives.
   lowercase_      :Any         =			parts[i]
   while i < len(__lowerCamelCase			) and not os.path.isfile(os.path.join(__lowerCamelCase     ,F'{module}.py'			)			):
      i += 1
      if i < len(__lowerCamelCase			):
         lowercase_      :Union[str, Any]         =			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:
      lowercase_      :Union[str, Any]         =			f.readlines()

   # Now let's find the class / func in the code!
   lowercase_      :int         =			""
   lowercase_      :int         =			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).
   lowercase_      :List[Any]         =			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

   lowercase_      :int         =			lines[start_index:line_index]
   return "".join(__lowerCamelCase			)


lowerCAmelCase :					Any 							=re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowerCAmelCase :					str 							=re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowerCAmelCase :					Tuple 							=re.compile(r'''<FILL\s+[^>]*>''')
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]			):
   lowercase_      :List[str]         =			code.split("\n"			)
   lowercase_      :Tuple         =			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 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   lowercase_      :Any         =			len(get_indent(__lowerCamelCase			)			) > 0
   if has_indent:
      lowercase_      :Union[str, Any]         =			F'class Bla:\n{code}'
   lowercase_      :int         =			black.Mode(target_versions={black.TargetVersion.PYaa}     ,line_length=1_19     ,preview=__lowerCamelCase			)
   lowercase_      :Optional[Any]         =			black.format_str(__lowerCamelCase     ,mode=__lowerCamelCase			)
   lowercase_      ,							lowercase_      :List[str]         =			style_docstrings_in_code(__lowerCamelCase			)
   return result[len("class Bla:\n"			) :] if has_indent else result
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : str=False			):
   with open(__lowerCamelCase     ,"r"     ,encoding="utf-8"     ,newline="\n"			) as f:
      lowercase_      :Optional[int]         =			f.readlines()
   lowercase_      :List[str]         =			[]
   lowercase_      :List[Any]         =			0
   # Not a for loop cause `lines` is going to change (if `overwrite=True`).
   while line_index < len(__lowerCamelCase			):
      lowercase_      :List[str]         =			_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.
      lowercase_      ,							lowercase_      ,							lowercase_      :str         =			search.groups()
      lowercase_      :Optional[int]         =			find_code_in_diffusers(__lowerCamelCase			)
      lowercase_      :Optional[int]         =			get_indent(__lowerCamelCase			)

      lowercase_      :str         =			line_index + 1 if indent == theoretical_indent else line_index + 2
      lowercase_      :List[str]         =			theoretical_indent
      lowercase_      :List[str]         =			start_index

      # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
      lowercase_      :str         =			True
      while line_index < len(__lowerCamelCase			) and should_continue:
         line_index += 1
         if line_index >= len(__lowerCamelCase			):
            break
         lowercase_      :List[Any]         =			lines[line_index]
         lowercase_      :Dict         =			_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

      lowercase_      :Any         =			lines[start_index:line_index]
      lowercase_      :List[Any]         =			"".join(__lowerCamelCase			)

      # Remove any nested `Copied from` comments to avoid circular copies
      lowercase_      :Tuple         =			[line for line in theoretical_code.split("\n"			) if _re_copy_warning.search(__lowerCamelCase			) is None]
      lowercase_      :Optional[int]         =			"\n".join(__lowerCamelCase			)

      # Before comparing, use the `replace_pattern` on the original code.
      if len(__lowerCamelCase			) > 0:
         lowercase_      :Tuple         =			replace_pattern.replace("with"     ,""			).split(","			)
         lowercase_      :Optional[int]         =			[_re_replace_pattern.search(__lowerCamelCase			) for p in patterns]
         for pattern in patterns:
            if pattern is None:
               continue
            lowercase_      ,							lowercase_      ,							lowercase_      :Tuple         =			pattern.groups()
            lowercase_      :List[str]         =			re.sub(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
            if option.strip() == "all-casing":
               lowercase_      :str         =			re.sub(obja.lower()     ,obja.lower()     ,__lowerCamelCase			)
               lowercase_      :List[Any]         =			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
         lowercase_      :Union[str, Any]         =			blackify(lines[start_index - 1] + theoretical_code			)
         lowercase_      :Union[str, Any]         =			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:
            lowercase_      :str         =			lines[:start_index] + [theoretical_code] + lines[line_index:]
            lowercase_      :Dict         =			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 							UpperCAmelCase_  (						__lowerCamelCase : bool = False			):
   lowercase_      :Any         =			glob.glob(os.path.join(__lowerCamelCase     ,"**/*.py"			)     ,recursive=__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			[]
   for filename in all_files:
      lowercase_      :Tuple         =			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:
      lowercase_      :Tuple         =			"\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__":
 lowerCAmelCase :					Union[str, Any] 							=argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 lowerCAmelCase :					int 							=parser.parse_args()

 check_copies(args.fix_and_overwrite)









'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor
class 				a_       :





     def __init__(			self		: int     ,				lowercase		: Optional[int]     ,				lowercase		: List[Any]=13     ,				lowercase		: Dict=30     ,				lowercase		: Dict=2     ,				lowercase		: Tuple=3     ,				lowercase		: Dict=True     ,				lowercase		: Dict=True     ,				lowercase		: Tuple=32     ,				lowercase		: List[Any]=2     ,				lowercase		: List[str]=4     ,				lowercase		: List[Any]=37     ,				lowercase		: Union[str, Any]="gelu"     ,				lowercase		: List[str]=0.1     ,				lowercase		: List[Any]=0.1     ,				lowercase		: List[str]=10     ,				lowercase		: Any=0.02     ,				lowercase		: Union[str, Any]=3     ,				lowercase		: Tuple=None     ,				lowercase		: List[str]=2     ,				):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			parent
        lowercase_      :Optional[int]         =			batch_size
        lowercase_      :Tuple         =			image_size
        lowercase_      :Any         =			patch_size
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Optional[Any]         =			is_training
        lowercase_      :str         =			use_labels
        lowercase_      :Any         =			hidden_size
        lowercase_      :Optional[int]         =			num_hidden_layers
        lowercase_      :List[Any]         =			num_attention_heads
        lowercase_      :str         =			intermediate_size
        lowercase_      :Optional[int]         =			hidden_act
        lowercase_      :List[Any]         =			hidden_dropout_prob
        lowercase_      :Optional[Any]         =			attention_probs_dropout_prob
        lowercase_      :Any         =			type_sequence_label_size
        lowercase_      :Dict         =			initializer_range
        lowercase_      :List[Any]         =			scope
        lowercase_      :List[str]         =			encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        lowercase_      :str         =			(image_size // patch_size) ** 2
        lowercase_      :Any         =			num_patches + 2





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :List[str]         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        lowercase_      :Optional[Any]         =			None
        if self.use_labels:
           lowercase_      :Optional[Any]         =			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size						)

        lowercase_      :Any         =			self.get_config()

        return config, pixel_values, labels





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        return DeiTConfig(
            image_size=self.image_size     ,				patch_size=self.patch_size     ,				num_channels=self.num_channels     ,				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     ,				is_decoder=lowercase     ,				initializer_range=self.initializer_range     ,				encoder_stride=self.encoder_stride     ,				)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: int     ,				lowercase		: str     ,				lowercase		: Tuple						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			TFDeiTModel(config=lowercase						)
        lowercase_      :str         =			model(lowercase						)
        self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)						)





     def 							lowercase__				(			self		: str     ,				lowercase		: str     ,				lowercase		: Optional[Any]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :List[str]         =			TFDeiTForMaskedImageModeling(config=lowercase						)
        lowercase_      :Optional[int]         =			model(lowercase						)
        self.parent.assertEqual(
            result.reconstruction.shape     ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

        # test greyscale images
        lowercase_      :List[str]         =			1
        lowercase_      :List[str]         =			TFDeiTForMaskedImageModeling(lowercase						)

        lowercase_      :Union[str, Any]         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        lowercase_      :List[str]         =			model(lowercase						)
        self.parent.assertEqual(result.reconstruction.shape     ,				(self.batch_size, 1, self.image_size, self.image_size)						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Any     ,				lowercase		: Union[str, Any]     ,				lowercase		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			self.type_sequence_label_size
        lowercase_      :str         =			TFDeiTForImageClassification(lowercase						)
        lowercase_      :str         =			model(lowercase     ,				labels=lowercase						)
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)						)

        # test greyscale images
        lowercase_      :Optional[int]         =			1
        lowercase_      :Optional[int]         =			TFDeiTForImageClassification(lowercase						)

        lowercase_      :List[Any]         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        lowercase_      :int         =			model(lowercase     ,				labels=lowercase						)
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)						)







     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :Tuple         =			self.prepare_config_and_inputs()
        lowercase_      ,							lowercase_      ,							lowercase_      :Tuple         =			config_and_inputs
        lowercase_      :Tuple         =			{"pixel_values": pixel_values}
        return config, inputs_dict







@require_tf
class 				a_       (    _lowerCAmelCase     ,						_lowerCAmelCase     ,						unittest.TestCase							):
     __A								=      (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     __A								=      (
         {
             "feature-extraction": TFDeiTModel,
             "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     __A								=      False
     __A								=      False
     __A								=      False
     __A								=      False





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			TFDeiTModelTester(self						)
        lowercase_      :List[Any]         =			ConfigTester(self     ,				config_class=lowercase     ,				has_text_modality=lowercase     ,				hidden_size=37						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     @unittest.skip(reason="DeiT does not use inputs_embeds"						)
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        pass





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      ,							lowercase_      :List[str]         =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowercase_      :Optional[Any]         =			model_class(lowercase						)
           self.assertIsInstance(model.get_input_embeddings()     ,				(tf.keras.layers.Layer)						)
           lowercase_      :Tuple         =			model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(lowercase     ,				tf.keras.layers.Dense						)						)





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      ,							lowercase_      :List[str]         =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowercase_      :Tuple         =			model_class(lowercase						)
           lowercase_      :Optional[int]         =			inspect.signature(model.call						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase_      :Tuple         =			[*signature.parameters.keys()]

           lowercase_      :Any         =			["pixel_values"]
           self.assertListEqual(arg_names[:1]     ,				lowercase						)





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase						)





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowercase						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase						)





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Optional[Any]     ,				lowercase		: List[str]     ,				lowercase		: Any=False						):





        """simple docstring"""




        lowercase_      :Dict         =			super()._prepare_for_class(lowercase     ,				lowercase     ,				return_labels=lowercase						)

        if return_labels:
           if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call						).parameters:
              del inputs_dict["labels"]

        return inputs_dict







     @slow
     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_      :List[Any]         =			TFDeiTModel.from_pretrained(lowercase						)
           self.assertIsNotNone(lowercase						)







def 							UpperCAmelCase_  (						):
   lowercase_      :List[str]         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
   return image


@require_tf
@require_vision
class 				a_       (    unittest.TestCase							):





     @cached_property
     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        return (
            DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"						)
            if is_vision_available()
            else None
        )







     @slow
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"						)

        lowercase_      :Optional[int]         =			self.default_image_processor
        lowercase_      :Union[str, Any]         =			prepare_img()
        lowercase_      :Dict         =			image_processor(images=lowercase     ,				return_tensors="tf"						)

        # forward pass
        lowercase_      :Union[str, Any]         =			model(**lowercase						)

        # verify the logits
        lowercase_      :Union[str, Any]         =			tf.TensorShape((1, 1_000)						)
        self.assertEqual(outputs.logits.shape     ,				lowercase						)

        lowercase_      :Dict         =			tf.constant([-1.02_66, 0.19_12, -1.28_61]						)

        self.assertTrue(np.allclose(outputs.logits[0, :3]     ,				lowercase     ,				atol=1e-4						)						)


