
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM





@require_torch
class 							UpperCamelCase__						:






       """simple docstring"""


       def __init__(				self      ,			_A      ,			_A=99      ,			_A=13      ,			_A=16      ,			_A=7      ,			_A=True      ,			_A=True      ,			_A=True      ,			_A=False      ,			_A=True      ,			_A=2      ,			_A=32      ,			_A=4      ,			_A=4      ,			_A=30      ,			_A=0      ,			_A=1      ,			_A=2      ,			_A=None      ,			)							->							int:
          SCREAMING_SNAKE_CASE_     =	parent
          SCREAMING_SNAKE_CASE_     =	batch_size
          SCREAMING_SNAKE_CASE_     =	decoder_seq_length
          # For common tests
          SCREAMING_SNAKE_CASE_     =	self.decoder_seq_length
          SCREAMING_SNAKE_CASE_     =	is_training
          SCREAMING_SNAKE_CASE_     =	use_attention_mask
          SCREAMING_SNAKE_CASE_     =	use_labels

          SCREAMING_SNAKE_CASE_     =	vocab_size
          SCREAMING_SNAKE_CASE_     =	d_model
          SCREAMING_SNAKE_CASE_     =	d_model
          SCREAMING_SNAKE_CASE_     =	decoder_layers
          SCREAMING_SNAKE_CASE_     =	decoder_layers
          SCREAMING_SNAKE_CASE_     =	decoder_ffn_dim
          SCREAMING_SNAKE_CASE_     =	decoder_attention_heads
          SCREAMING_SNAKE_CASE_     =	decoder_attention_heads
          SCREAMING_SNAKE_CASE_     =	eos_token_id
          SCREAMING_SNAKE_CASE_     =	bos_token_id
          SCREAMING_SNAKE_CASE_     =	pad_token_id
          SCREAMING_SNAKE_CASE_     =	decoder_start_token_id
          SCREAMING_SNAKE_CASE_     =	use_cache
          SCREAMING_SNAKE_CASE_     =	max_position_embeddings

          SCREAMING_SNAKE_CASE_     =	None
          SCREAMING_SNAKE_CASE_     =	decoder_seq_length
          SCREAMING_SNAKE_CASE_     =	2
          SCREAMING_SNAKE_CASE_     =	1



       def   _UpperCamelCase     (				self     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size, self.decoder_seq_length]      ,			self.vocab_size     )

          SCREAMING_SNAKE_CASE_     =	None
          if self.use_attention_mask:
             SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size, self.decoder_seq_length]      ,			vocab_size=2     )

          SCREAMING_SNAKE_CASE_     =	None
          if self.use_labels:
             SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size, self.decoder_seq_length]      ,			self.vocab_size     )

          SCREAMING_SNAKE_CASE_     =	TrOCRConfig(
              vocab_size=self.vocab_size      ,			d_model=self.d_model      ,			decoder_layers=self.decoder_layers      ,			decoder_ffn_dim=self.decoder_ffn_dim      ,			decoder_attention_heads=self.decoder_attention_heads      ,			eos_token_id=self.eos_token_id      ,			bos_token_id=self.bos_token_id      ,			use_cache=self.use_cache      ,			pad_token_id=self.pad_token_id      ,			decoder_start_token_id=self.decoder_start_token_id      ,			max_position_embeddings=self.max_position_embeddings      ,			)

          return (config, input_ids, attention_mask, lm_labels)



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			_A      ,			)							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	True
          SCREAMING_SNAKE_CASE_     =	TrOCRDecoder(config=_A     ).to(_A     ).eval()
          SCREAMING_SNAKE_CASE_     =	input_ids[:2]

          input_ids[input_ids == 0] += 1
          # first forward pass
          SCREAMING_SNAKE_CASE_     =	model(_A      ,			use_cache=_A     )
          SCREAMING_SNAKE_CASE_     =	model(_A     )
          SCREAMING_SNAKE_CASE_     =	model(_A      ,			use_cache=_A     )

          self.parent.assertTrue(len(_A     ) == len(_A     )     )
          self.parent.assertTrue(len(_A     ) == len(_A     ) + 1     )

          SCREAMING_SNAKE_CASE_     =	outputs["""past_key_values"""]

          # create hypothetical next token and extent to next_input_ids
          SCREAMING_SNAKE_CASE_     =	ids_tensor((2, 1)      ,			config.vocab_size - 1     ) + 1

          # append to next input_ids and
          SCREAMING_SNAKE_CASE_     =	torch.cat([input_ids, next_tokens]      ,			dim=-1     )

          SCREAMING_SNAKE_CASE_     =	model(_A     )["""last_hidden_state"""]
          SCREAMING_SNAKE_CASE_     =	model(_A      ,			past_key_values=_A     )["""last_hidden_state"""]

          # select random slice
          SCREAMING_SNAKE_CASE_     =	ids_tensor((1,)      ,			output_from_past.shape[-1]     ).item()
          SCREAMING_SNAKE_CASE_     =	output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
          SCREAMING_SNAKE_CASE_     =	output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          assert torch.allclose(_A      ,			_A      ,			atol=1E-3     )




       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_     =	config_and_inputs

          SCREAMING_SNAKE_CASE_     =	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict







@require_torch
class 							UpperCamelCase__						(     __lowerCamelCase    ,  __lowerCamelCase    ,  __lowerCamelCase    ,  unittest.TestCase     ):






       """simple docstring"""


       UpperCAmelCase_							    =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
       UpperCAmelCase_							    =(TrOCRForCausalLM,) if is_torch_available() else ()
       UpperCAmelCase_							    ={"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
       UpperCAmelCase_							    =True
       UpperCAmelCase_							    =False



       def   _UpperCamelCase     (				self     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	TrOCRStandaloneDecoderModelTester(self      ,			is_training=_A     )
          SCREAMING_SNAKE_CASE_     =	ConfigTester(self      ,			config_class=_A     )



       def   _UpperCamelCase     (				self     )							->							int:
          pass



       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          pass



       def   _UpperCamelCase     (				self     )							->							int:
          pass



       def   _UpperCamelCase     (				self     )							->							Tuple:
          self.config_tester.run_common_tests()



       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past(*_A     )



       def   _UpperCamelCase     (				self     )							->							Any:
          return




       @unittest.skip('''The model doesn\'t support left padding'''     )  # and it's not used enough to be worth fixing :)
       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          pass









import argparse
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_dummies.py
__UpperCAmelCase       :   Any				       =							"src/diffusers"

# Matches is_xxx_available()
__UpperCAmelCase       :   List[str]				       =							re.compile(R"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
__UpperCAmelCase       :   Dict				       =							re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


__UpperCAmelCase       :   int				       =							"\n{0} = None\n"

__UpperCAmelCase       :   List[str]				       =							"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


__UpperCAmelCase       :   Tuple				       =							"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						int   =						_re_backend.findall(SCREAMING_SNAKE_CASE__)
  if len(SCREAMING_SNAKE_CASE__) == 0:
    return None

  return "_and_".join(SCREAMING_SNAKE_CASE__)







def 			A__							(    )   ->    Optional[int]:
  with open(os.path.join(SCREAMING_SNAKE_CASE__					,					"""__init__.py""")					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
    __snake_case:						Optional[Any]   =						f.readlines()

  # Get to the point we do the actual imports for type checking
  __snake_case:						Tuple   =						0
  __snake_case:						Any   =						{}
  # Go through the end of the file
  while line_index < len(SCREAMING_SNAKE_CASE__):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    __snake_case:						List[Any]   =						find_backend(lines[line_index])
    if backend is not None:
      while not lines[line_index].startswith("""else:"""):
        line_index += 1
      line_index += 1
      __snake_case:						Any   =						[]
      # Until we unindent, add backend objects to the list
      while line_index < len(SCREAMING_SNAKE_CASE__) and len(lines[line_index]) > 1:
        __snake_case:						List[Any]   =						lines[line_index]
        __snake_case:						str   =						_re_single_line_import.search(SCREAMING_SNAKE_CASE__)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """))
        elif line.startswith(""" """ * 8):
          objects.append(line[8:-2])
        line_index += 1

      if len(SCREAMING_SNAKE_CASE__) > 0:
        __snake_case:						Optional[Any]   =						objects
    else:
      line_index += 1

  return backend_specific_objects







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if name.isupper():
    return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__)
  elif name.islower():
    return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  else:
    return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)







def 			A__							(    SCREAMING_SNAKE_CASE__=None)   ->    Optional[int]:
  if backend_specific_objects is None:
    __snake_case:						Union[str, Any]   =						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __snake_case:						Union[str, Any]   =						{}

  for backend, objects in backend_specific_objects.items():
    __snake_case:						List[Any]   =						"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_""")) + """]"""
    __snake_case:						Dict   =						"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__) for o in objects])
    __snake_case:						List[str]   =						dummy_file

  return dummy_files







def 			A__							(    SCREAMING_SNAKE_CASE__=False)   ->    int:
  __snake_case:						List[str]   =						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __snake_case:						Dict   =						{"""torch""": """pt"""}

  # Locate actual dummy modules and read their content.
  __snake_case:						Optional[Any]   =						os.path.join(SCREAMING_SNAKE_CASE__					,					"""utils""")
  __snake_case:						List[Any]   =						{
      backend: os.path.join(SCREAMING_SNAKE_CASE__					,					F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py''')
      for backend in dummy_files.keys()
  }

  __snake_case:						int   =						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(SCREAMING_SNAKE_CASE__):
      with open(SCREAMING_SNAKE_CASE__					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
        __snake_case:						Any   =						f.read()
    else:
      __snake_case:						Tuple   =						""""""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py as the main '''
            """__init__ has new objects.""")
        with open(dummy_file_paths[backend]					,					"""w"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
          f.write(dummy_files[backend])
      else:
        raise ValueError(
            """The main __init__ has objects that are not present in """
            F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py. Run `make fix-copies` '''
            """to fix this.""")


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

      check_dummies(args.fix_and_overwrite)






