
"""simple docstring"""







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor






class     __a			(					lowerCAmelCase_			):







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.config_class(**self.inputs_dict		)
          self.parent.assertTrue(hasattr(lowerCamelCase_							,"hidden_sizes"		)		)
          self.parent.assertTrue(hasattr(lowerCamelCase_							,"num_attention_heads"		)		)
          self.parent.assertTrue(hasattr(lowerCamelCase_							,"num_encoder_blocks"		)		)






class     __a			:







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=64							,lowerCamelCase_=3							,lowerCamelCase_=4							,lowerCamelCase_=[2, 2, 2, 2]							,lowerCamelCase_=[8, 4, 2, 1]							,lowerCamelCase_=[16, 32, 64, 128]							,lowerCamelCase_=[1, 4, 8, 16]							,lowerCamelCase_=[1, 2, 4, 8]							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_="gelu"							,lowerCamelCase_=0.1							,lowerCamelCase_=0.1							,lowerCamelCase_=0.0_2							,lowerCamelCase_=16							,lowerCamelCase_=3							,lowerCamelCase_=None							,):



          '''simple docstring'''

          _UpperCAmelCase   	=					parent
          _UpperCAmelCase   	=					batch_size
          _UpperCAmelCase   	=					image_size
          _UpperCAmelCase   	=					num_channels
          _UpperCAmelCase   	=					num_encoder_blocks
          _UpperCAmelCase   	=					sr_ratios
          _UpperCAmelCase   	=					depths
          _UpperCAmelCase   	=					hidden_sizes
          _UpperCAmelCase   	=					downsampling_rates
          _UpperCAmelCase   	=					num_attention_heads
          _UpperCAmelCase   	=					is_training
          _UpperCAmelCase   	=					use_labels
          _UpperCAmelCase   	=					hidden_act
          _UpperCAmelCase   	=					hidden_dropout_prob
          _UpperCAmelCase   	=					attention_probs_dropout_prob
          _UpperCAmelCase   	=					initializer_range
          _UpperCAmelCase   	=					decoder_hidden_size
          _UpperCAmelCase   	=					num_labels
          _UpperCAmelCase   	=					scope







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

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







       def    _snake_case			(					self		):



          '''simple docstring'''

          return GLPNConfig(
              image_size=self.image_size							,num_channels=self.num_channels							,num_encoder_blocks=self.num_encoder_blocks							,depths=self.depths							,hidden_sizes=self.hidden_sizes							,num_attention_heads=self.num_attention_heads							,hidden_act=self.hidden_act							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,initializer_range=self.initializer_range							,decoder_hidden_size=self.decoder_hidden_size							,)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					GLPNModel(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_		)
          _UpperCAmelCase   	=					_UpperCAmelCase   	=					self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape							,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.num_labels
          _UpperCAmelCase   	=					GLPNForDepthEstimation(lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_		)
          self.parent.assertEqual(result.predicted_depth.shape							,(self.batch_size, self.image_size, self.image_size)		)
          _UpperCAmelCase   	=					model(lowerCamelCase_							,labels=lowerCamelCase_		)
          self.parent.assertEqual(result.predicted_depth.shape							,(self.batch_size, self.image_size, self.image_size)		)







       def    _snake_case			(					self		):



          '''simple docstring'''

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






@require_torch
class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
       _lowerCamelCase							:							Any       =			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       _lowerCamelCase							:							int       =			(
           {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
       )

       _lowerCamelCase							:							List[Any]       =			False
       _lowerCamelCase							:							Any       =			False
       _lowerCamelCase							:							List[str]       =			False







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					GLPNModelTester(self		)
          _UpperCAmelCase   	=					GLPNConfigTester(self							,config_class=lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          self.config_tester.run_common_tests()







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_		)







       @unittest.skip("GLPN does not use inputs_embeds"		)
       def    _snake_case			(					self		):



          '''simple docstring'''

          pass







       @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"		)
       def    _snake_case			(					self		):



          '''simple docstring'''

          pass







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

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

             _UpperCAmelCase   	=					["pixel_values"]
             self.assertListEqual(arg_names[:1]							,lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase   	=					True

          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					True
             _UpperCAmelCase   	=					False
             _UpperCAmelCase   	=					True
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             model.to(lowerCamelCase_		)
             model.eval()
             with torch.no_grad():
                _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
             _UpperCAmelCase   	=					outputs.attentions

             _UpperCAmelCase   	=					sum(self.model_tester.depths		)
             self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             _UpperCAmelCase   	=					True
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             model.to(lowerCamelCase_		)
             model.eval()
             with torch.no_grad():
                _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
             _UpperCAmelCase   	=					outputs.attentions

             self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)

             # verify the first attentions (first block, first layer)
             _UpperCAmelCase   	=					(self.model_tester.image_size // 4) ** 2
             _UpperCAmelCase   	=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:]		)							,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,)

             # verify the last attentions (last block, last layer)
             _UpperCAmelCase   	=					(self.model_tester.image_size // 32) ** 2
             _UpperCAmelCase   	=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:]		)							,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]							,)
             _UpperCAmelCase   	=					len(lowerCamelCase_		)

             # Check attention is always last and order is fine
             _UpperCAmelCase   	=					True
             _UpperCAmelCase   	=					True
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             model.to(lowerCamelCase_		)
             model.eval()
             with torch.no_grad():
                _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)

             self.assertEqual(out_len + 1							,len(lowerCamelCase_		)		)

             _UpperCAmelCase   	=					outputs.attentions

             self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
             # verify the first attentions (first block, first layer)
             _UpperCAmelCase   	=					(self.model_tester.image_size // 4) ** 2
             _UpperCAmelCase   	=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]		)							,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,)







       def    _snake_case			(					self		):



          '''simple docstring'''

          def check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             model.to(lowerCamelCase_		)
             model.eval()

             with torch.no_grad():
                _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)

             _UpperCAmelCase   	=					outputs.hidden_states

             _UpperCAmelCase   	=					self.model_tester.num_encoder_blocks
             self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)

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

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					True
             check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)

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

             check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          if not self.model_tester.is_training:
             return

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase   	=					True

          for model_class in self.all_model_classes:
             if model_class in get_values(lowerCamelCase_		):
                continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             if model_class.__name__ == "GLPNForDepthEstimation":
                _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					inputs_dict["pixel_values"].shape
                _UpperCAmelCase   	=					torch.zeros(
                    [self.model_tester.batch_size, height, width]							,device=lowerCamelCase_		).long()
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             model.to(lowerCamelCase_		)
             model.train()
             _UpperCAmelCase   	=					self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_							,return_labels=lowerCamelCase_		)
             _UpperCAmelCase   	=					model(**lowerCamelCase_		).loss
             loss.backward()







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase   	=					GLPNModel.from_pretrained(lowerCamelCase_		)
             self.assertIsNotNone(lowerCamelCase_		)



def 			__UpperCAmelCase		(		)     ->       int:
   _UpperCAmelCase   	=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image






@require_torch
@require_vision
@slow
class     __a			(					unittest.TestCase			):







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
          _UpperCAmelCase   	=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowerCamelCase_		)

          _UpperCAmelCase   	=					prepare_img()
          _UpperCAmelCase   	=					image_processor(images=lowerCamelCase_							,return_tensors="pt"		).to(lowerCamelCase_		)

          # forward pass
          with torch.no_grad():
             _UpperCAmelCase   	=					model(**lowerCamelCase_		)

          # verify the predicted depth
          _UpperCAmelCase   	=					torch.Size([1, 480, 640]		)
          self.assertEqual(outputs.predicted_depth.shape							,lowerCamelCase_		)

          _UpperCAmelCase   	=					torch.tensor(
              [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(lowerCamelCase_		)

          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]							,lowerCamelCase_							,atol=1e-4		)		)




"""simple docstring"""







import argparse
import collections
import importlib.util
import os
import re


# 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
SCREAMING_SNAKE_CASE_     				=   'src/diffusers'
SCREAMING_SNAKE_CASE_     				=   'docs/source/en'
SCREAMING_SNAKE_CASE_     				=   '.'
def 			__UpperCAmelCase		(		a_			:		Optional[Any]    ,  a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
   with open(a_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
      _UpperCAmelCase   	=					f.readlines()
   # Find the start prompt.
   _UpperCAmelCase   	=					0
   while not lines[start_index].startswith(a_				):
      start_index += 1
   start_index += 1

   _UpperCAmelCase   	=					start_index
   while not lines[end_index].startswith(a_				):
      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 |
SCREAMING_SNAKE_CASE_     				=   'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE_     				=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE_     				=   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.
SCREAMING_SNAKE_CASE_     				=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_     				=   importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE_     				=   spec.loader.load_module()
def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   _UpperCAmelCase   	=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  a_				)
   return [m.group(0				) for m in matches]
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
   _UpperCAmelCase   	=					2 if text == "✅" or text == "❌" else len(a_				)
   _UpperCAmelCase   	=					(width - text_length) // 2
   _UpperCAmelCase   	=					width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent
def 			__UpperCAmelCase		(		)     ->       List[Any]:
   _UpperCAmelCase   	=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   _UpperCAmelCase   	=					{
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   _UpperCAmelCase   	=					{name: config.replace("ConfigMixin"    ,  ""				) 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.
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(a_				):
      _UpperCAmelCase   	=					None
      if attr_name.endswith("Tokenizer"				):
         _UpperCAmelCase   	=					slow_tokenizers
         _UpperCAmelCase   	=					attr_name[:-9]
      elif attr_name.endswith("TokenizerFast"				):
         _UpperCAmelCase   	=					fast_tokenizers
         _UpperCAmelCase   	=					attr_name[:-13]
      elif _re_tf_models.match(a_				) is not None:
         _UpperCAmelCase   	=					tf_models
         _UpperCAmelCase   	=					_re_tf_models.match(a_				).groups()[0]
      elif _re_flax_models.match(a_				) is not None:
         _UpperCAmelCase   	=					flax_models
         _UpperCAmelCase   	=					_re_flax_models.match(a_				).groups()[0]
      elif _re_pt_models.match(a_				) is not None:
         _UpperCAmelCase   	=					pt_models
         _UpperCAmelCase   	=					_re_pt_models.match(a_				).groups()[0]

      if lookup_dict is not None:
         while len(a_				) > 0:
            if attr_name in model_name_to_prefix.values():
               _UpperCAmelCase   	=					True
               break
            # Try again after removing the last word in the name
            _UpperCAmelCase   	=					"".join(camel_case_split(a_				)[:-1]				)

    # Let's build that table!
   _UpperCAmelCase   	=					list(model_name_to_config.keys()				)
   model_names.sort(key=str.lower				)
   _UpperCAmelCase   	=					["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).
   _UpperCAmelCase   	=					[len(a_				) + 2 for c in columns]
   _UpperCAmelCase   	=					max([len(a_				) for name in model_names]				) + 2

   # Build the table per se
   _UpperCAmelCase   	=					"|" + "|".join([_center_text(a_    ,  a_				) for c, w in zip(a_    ,  a_				)]				) + "|\n"
   # Use ":-----:" format to center-aligned table cell texts
   table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]				) + "|\n"

   _UpperCAmelCase   	=					{True: "✅", False: "❌"}
   for name in model_names:
      _UpperCAmelCase   	=					model_name_to_prefix[name]
      _UpperCAmelCase   	=					[
          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(a_    ,  a_				) for l, w in zip(a_    ,  a_				)]				) + "|\n"
   return table
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]=False				)     ->       int:
   _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					_find_text_in_file(
       filename=os.path.join(a_    ,  "index.mdx"				)    ,  start_prompt="<!--This table is updated automatically from the auto modules"    ,  end_prompt="<!-- End table-->"    ,  )
   _UpperCAmelCase   	=					get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(a_    ,  "index.mdx"				)    ,  "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.mdx` has not been updated. Run `make fix-copies` to fix this."				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()

  check_model_table(args.fix_and_overwrite)

