

"""simple docstring"""





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import CvtForImageClassification, CvtModel
    from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class  _lowerCAmelCase  (     __lowercase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						self.config_class(**self.inputs_dict     )
    self.parent.assertTrue(hasattr(_a, "embed_dim"     )     )
    self.parent.assertTrue(hasattr(_a, "num_heads"     )     )







class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Tuple, UpperCAmelCase__							:      str, UpperCAmelCase__							:      Optional[int]=1_3, UpperCAmelCase__							:      Optional[int]=6_4, UpperCAmelCase__							:      Union[str, Any]=3, UpperCAmelCase__							:      List[Any]=[1_6, 4_8, 9_6], UpperCAmelCase__							:      List[Any]=[1, 3, 6], UpperCAmelCase__							:      List[Any]=[1, 2, 1_0], UpperCAmelCase__							:      List[str]=[7, 3, 3], UpperCAmelCase__							:      Optional[Any]=[4, 2, 2], UpperCAmelCase__							:      int=[2, 1, 1], UpperCAmelCase__							:      Tuple=[2, 2, 2], UpperCAmelCase__							:      Dict=[False, False, True], UpperCAmelCase__							:      Tuple=[0.0, 0.0, 0.0], UpperCAmelCase__							:      Optional[int]=0.02, UpperCAmelCase__							:      str=1E-12, UpperCAmelCase__							:      Tuple=True, UpperCAmelCase__							:      Optional[Any]=True, UpperCAmelCase__							:      Optional[Any]=2, ):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_sizes
    __lowercase								=						patch_stride
    __lowercase								=						patch_padding
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						num_labels
    __lowercase								=						num_channels
    __lowercase								=						embed_dim
    __lowercase								=						num_heads
    __lowercase								=						stride_kv
    __lowercase								=						depth
    __lowercase								=						cls_token
    __lowercase								=						attention_drop_rate
    __lowercase								=						initializer_range
    __lowercase								=						layer_norm_eps


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

    __lowercase								=						None
    if self.use_labels:
       __lowercase								=						ids_tensor([self.batch_size], self.num_labels     )

    __lowercase								=						self.get_config()
    return config, pixel_values, labels


 def  _lowercase			(      self							:      Any     ):
    return CvtConfig(
        image_size=self.image_size, num_labels=self.num_labels, num_channels=self.num_channels, embed_dim=self.embed_dim, num_heads=self.num_heads, patch_sizes=self.patch_sizes, patch_padding=self.patch_padding, patch_stride=self.patch_stride, stride_kv=self.stride_kv, depth=self.depth, cls_token=self.cls_token, attention_drop_rate=self.attention_drop_rate, initializer_range=self.initializer_range, )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						CvtModel(config=_a     )
    model.to(_a     )
    model.eval()
    __lowercase								=						model(_a     )
    __lowercase								=						(self.image_size, self.image_size)
    __lowercase		,__lowercase								=						image_size[0], image_size[1]
    for i in range(len(self.depth     )     ):
       __lowercase								=						floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1     )
       __lowercase								=						floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1     )
    self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width)     )


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      int     ):
    __lowercase								=						self.num_labels
    __lowercase								=						CvtForImageClassification(_a     )
    model.to(_a     )
    model.eval()
    __lowercase								=						model(_a, labels=_a     )
    self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)     )



 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						self.prepare_config_and_inputs()
    __lowercase		,__lowercase		,__lowercase								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class  _lowerCAmelCase  (     __lowercase					,__lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     (CvtModel, CvtForImageClassification) if is_torch_available() else ()
 __UpperCAmelCase     : List[Any]       		=     (
     {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
     if is_torch_available()
     else {}
 )

 __UpperCAmelCase     : str       		=     False
 __UpperCAmelCase     : Optional[int]       		=     False
 __UpperCAmelCase     : Tuple       		=     False
 __UpperCAmelCase     : Optional[int]       		=     False
 __UpperCAmelCase     : Any       		=     False


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						CvtModelTester(self     )
    __lowercase								=						ConfigTester(self, config_class=_a, has_text_modality=_a, hidden_size=3_7     )


 def  _lowercase			(      self							:      int     ):
    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()


 def  _lowercase			(      self							:      str     ):
    return


 @unittest.skip(reason="Cvt does not output attentions"     )
 def  _lowercase			(      self							:      Dict     ):
    pass


 @unittest.skip(reason="Cvt does not use inputs_embeds"     )
 def  _lowercase			(      self							:      Any     ):
    pass


 @unittest.skip(reason="Cvt does not support input and output embeddings"     )
 def  _lowercase			(      self							:      Any     ):
    pass


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						model_class(_a     )
       __lowercase								=						inspect.signature(model.forward     )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase								=						[*signature.parameters.keys()]

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1], _a     )


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a     )


 def  _lowercase			(      self							:      Dict     ):
    def check_hidden_states_output(UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Tuple     ):
       __lowercase								=						model_class(_a     )
       model.to(_a     )
       model.eval()

       with torch.no_grad():
          __lowercase								=						model(**self._prepare_for_class(_a, _a     )     )

       __lowercase								=						outputs.hidden_states

       __lowercase								=						len(self.model_tester.depth     )
       self.assertEqual(len(_a     ), _a     )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]     ), [
               self.model_tester.embed_dim[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ], )

    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						True
       check_hidden_states_output(_a, _a, _a     )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowercase								=						True

       check_hidden_states_output(_a, _a, _a     )


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_a     )


 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
 def  _lowercase			(      self							:      Optional[Any]     ):
    pass



 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						CvtModel.from_pretrained(_a     )
       self.assertIsNotNone(_a     )







def   _A  (  )     ->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_torch
@require_vision
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @cached_property
 def  _lowercase			(      self							:      List[str]     ):
    return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )



 @slow
 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(_a     )

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=_a, return_tensors="pt"     ).to(_a     )

    # forward pass
    with torch.no_grad():
       __lowercase								=						model(**_a     )

    # verify the logits
    __lowercase								=						torch.Size((1, 1_0_0_0)     )
    self.assertEqual(outputs.logits.shape, _a     )

    __lowercase								=						torch.tensor([0.9_285, 0.9_015, -0.3_150]     ).to(_a     )

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







"""simple docstring"""





import argparse
import os
import re


_a							=							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a							=							re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_a							=							re.compile(r'\s*\(\s*"(\S[^"]+)"')




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   with open(UpperCamelCase_,      "r",      encoding="utf-8") as f:
      __lowercase								=						f.read()

   __lowercase								=						content.split("\n")
   __lowercase								=						[]
   __lowercase								=						0
   while line_idx < len(UpperCamelCase_):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         __lowercase								=						len(re.search(r"^(\s*)\S",      lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("):
            new_lines.append(lines[line_idx])
            line_idx += 1

         __lowercase								=						[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               __lowercase								=						line_idx
               while not lines[line_idx].startswith(" " * indent + ")"):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         __lowercase								=						sorted(UpperCamelCase_,      key=lambda UpperCamelCase_: _re_identifier.search(UpperCamelCase_).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(UpperCamelCase_,      "w",      encoding="utf-8") as f:
         f.write("\n".join(UpperCamelCase_))
   elif "\n".join(UpperCamelCase_) != content:
      return True




def   _A  (  UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[os.path.join(UpperCamelCase_,      UpperCamelCase_) for f in os.listdir(UpperCamelCase_) if f.endswith(".py")]
   __lowercase								=						[sort_auto_mapping(UpperCamelCase_,      overwrite=UpperCamelCase_) for fname in fnames]

   if not overwrite and any(UpperCamelCase_):
      __lowercase								=						[f for f, d in zip(UpperCamelCase_,      UpperCamelCase_) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_)}. Run `make style` to fix"""
          " this.")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _a							=							parser.parse_args()

   sort_all_auto_mappings(not args.check_only)




