
'''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    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_=13 ,       lowercase_=30 ,       lowercase_=2 ,       lowercase_=3 ,       lowercase_=True ,       lowercase_=True ,       lowercase_=32 ,       lowercase_=2 ,       lowercase_=4 ,       lowercase_=37 ,       lowercase_="gelu" ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=10 ,       lowercase_=0.02 ,       lowercase_=3 ,       lowercase_=None ,       lowercase_=2 ,       ):
     snake_case_ :    Tuple     			=						parent
     snake_case_ :    List[str]     			=						batch_size
     snake_case_ :    Tuple     			=						image_size
     snake_case_ :    Dict     			=						patch_size
     snake_case_ :    List[str]     			=						num_channels
     snake_case_ :    Dict     			=						is_training
     snake_case_ :    Optional[int]     			=						use_labels
     snake_case_ :    Optional[int]     			=						hidden_size
     snake_case_ :    Any     			=						num_hidden_layers
     snake_case_ :    Optional[Any]     			=						num_attention_heads
     snake_case_ :    Dict     			=						intermediate_size
     snake_case_ :    Dict     			=						hidden_act
     snake_case_ :    Dict     			=						hidden_dropout_prob
     snake_case_ :    Optional[Any]     			=						attention_probs_dropout_prob
     snake_case_ :    Optional[int]     			=						type_sequence_label_size
     snake_case_ :    Optional[int]     			=						initializer_range
     snake_case_ :    Tuple     			=						scope
     snake_case_ :    Dict     			=						encoder_stride

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





 def        snake_case__		(    self):
     snake_case_ :    int     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     snake_case_ :    Union[str, Any]     			=						self.get_config()

     return config, pixel_values, labels





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Optional[Any]     			=						TFDeiTModel(config=lowercase_)
     snake_case_ :    str     			=						model(lowercase_)
     self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size))





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

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

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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Optional[Any]     			=						self.type_sequence_label_size
     snake_case_ :    Any     			=						TFDeiTForImageClassification(lowercase_)
     snake_case_ :    str     			=						model(lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size))

     # test greyscale images
     snake_case_ :    Tuple     			=						1
     snake_case_ :    Union[str, Any]     			=						TFDeiTForImageClassification(lowercase_)

     snake_case_ :    Dict     			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
     snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size))





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.prepare_config_and_inputs()
     snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						config_and_inputs
     snake_case_ :    Any     			=						{"pixel_values": pixel_values}
     return config, inputs_dict






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

 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						TFDeiTModelTester(self)
     snake_case_ :    int     			=						ConfigTester(self ,       config_class=lowercase_ ,       has_text_modality=lowercase_ ,       hidden_size=37)





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





 @unittest.skip(reason="DeiT does not use inputs_embeds")
 def        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Any     			=						self.model_tester.prepare_config_and_inputs_for_common()

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





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_ :    Dict     			=						["pixel_values"]
         self.assertListEqual(arg_names[:1] ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    int     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_=False):
     snake_case_ :    Optional[Any]     			=						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        snake_case__		(    self):
     for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :    Optional[int]     			=						TFDeiTModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)


def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image






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





 @cached_property
 def        snake_case__		(    self):
     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
         if is_vision_available()
         else None
     )





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")

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

     # forward pass
     snake_case_ :    str     			=						model(**lowercase_)

     # verify the logits
     snake_case_ :    List[str]     			=						tf.TensorShape((1, 10_00))
     self.assertEqual(outputs.logits.shape ,       lowercase_)

     snake_case_ :    Tuple     			=						tf.constant([-1.0_266, 0.1_912, -1.2_861])

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






'''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_											=						"src/diffusers"
a_											=						"."


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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						object_name.split("."       )
    snake_case_ :    Dict     			=						0

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

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

    # Now let's find the class / func in the code!
    snake_case_ :    Any     			=						""
    snake_case_ :    Optional[Any]     			=						0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__SCREAMING_SNAKE_CASE       ) 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(__SCREAMING_SNAKE_CASE       ):
        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).
    snake_case_ :    Union[str, Any]     			=						line_index
    while line_index < len(__SCREAMING_SNAKE_CASE       ) and _should_continue(lines[line_index],		__SCREAMING_SNAKE_CASE       ):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]       ) <= 1:
        line_index -= 1

    snake_case_ :    Dict     			=						lines[start_index:line_index]
    return "".join(__SCREAMING_SNAKE_CASE       )


a_											=						re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_											=						re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_											=						re.compile(R"<FILL\s+[^>]*>")

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						len(get_indent(__SCREAMING_SNAKE_CASE       )       ) > 0
    if has_indent:
        snake_case_ :    Optional[Any]     			=						f'class Bla:\n{code}'
    snake_case_ :    List[Any]     			=						black.Mode(target_versions={black.TargetVersion.PYaa},		line_length=1_1_9,		preview=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						black.format_str(__SCREAMING_SNAKE_CASE,		mode=__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Optional[Any]     			=						style_docstrings_in_code(__SCREAMING_SNAKE_CASE       )
    return result[len("class Bla:\n"       ) :] if has_indent else result

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    List[Any]     			=						f.readlines()
    snake_case_ :    str     			=						[]
    snake_case_ :    Tuple     			=						0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Union[str, Any]     			=						_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.
        snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						search.groups()
        snake_case_ :    int     			=						find_code_in_diffusers(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    List[Any]     			=						get_indent(__SCREAMING_SNAKE_CASE       )

        snake_case_ :    Dict     			=						line_index + 1 if indent == theoretical_indent else line_index + 2
        snake_case_ :    Dict     			=						theoretical_indent
        snake_case_ :    Tuple     			=						start_index

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

        snake_case_ :    Optional[int]     			=						lines[start_index:line_index]
        snake_case_ :    Optional[Any]     			=						"".join(__SCREAMING_SNAKE_CASE       )

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

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

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

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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = False       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						glob.glob(os.path.join(__SCREAMING_SNAKE_CASE,		"**/*.py"       ),		recursive=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						[]
    for filename in all_files:
        snake_case_ :    Tuple     			=						is_copy_consistent(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        snake_case_ :    str     			=						"\n".join(__SCREAMING_SNAKE_CASE       )
        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_											=						argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  a_											=						parser.parse_args()

  check_copies(args.fix_and_overwrite)




