
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
__lowerCAmelCase					:			List[Any]            =		"src/diffusers"

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


__lowerCAmelCase					:			Optional[int]            =		"\n{0} = None\n"

__lowerCAmelCase					:			Optional[Any]            =		"\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"


__lowerCAmelCase					:			Optional[int]            =		"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[str]:
    __lowercase		:			Optional[int]	    =       _re_backend.findall(lowerCamelCase_	)
    if len(lowerCamelCase_	) == 0:
        return None

    return "_and_".join(lowerCamelCase_	)


def     UpperCAmelCase_  (						) ->						Any:



    with open(os.path.join(lowerCamelCase_	,					'''__init__.py'''	)	,					'''r'''	,					encoding='''utf-8'''	,					newline='''\n'''	) as f:
        __lowercase		:			str	    =       f.readlines()

    # Get to the point we do the actual imports for type checking
    __lowercase		:			Optional[Any]	    =       0
    __lowercase		:			Tuple	    =       {}
    # Go through the end of the file
    while line_index < len(lowerCamelCase_	):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        __lowercase		:			Dict	    =       find_backend(lines[line_index]	)
        if backend is not None:
            while not lines[line_index].startswith('''else:'''	):
                line_index += 1
            line_index += 1
            __lowercase		:			Dict	    =       []
            # Until we unindent, add backend objects to the list
            while line_index < len(lowerCamelCase_	) and len(lines[line_index]	) > 1:
                __lowercase		:			List[str]	    =       lines[line_index]
                __lowercase		:			Optional[Any]	    =       _re_single_line_import.search(lowerCamelCase_	)
                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(lowerCamelCase_	) > 0:
                __lowercase		:			int	    =       objects
        else:
            line_index += 1

    return backend_specific_objects


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Dict:



    if name.isupper():
        return DUMMY_CONSTANT.format(lowerCamelCase_	)
    elif name.islower():
        return DUMMY_FUNCTION.format(lowerCamelCase_	,					lowerCamelCase_	)
    else:
        return DUMMY_CLASS.format(lowerCamelCase_	,					lowerCamelCase_	)


def     UpperCAmelCase_  (						__lowerCAmelCase=None	) ->						Optional[int]:



    if backend_specific_objects is None:
        __lowercase		:			str	    =       read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    __lowercase		:			Dict	    =       {}

    for backend, objects in backend_specific_objects.items():
        __lowercase		:			Union[str, Any]	    =       """[""" + """, """.join(F'"{b}"' for b in backend.split('''_and_'''	)	) + """]"""
        __lowercase		:			List[str]	    =       """# 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(lowerCamelCase_	,					lowerCamelCase_	) for o in objects]	)
        __lowercase		:			str	    =       dummy_file

    return dummy_files


def     UpperCAmelCase_  (						__lowerCAmelCase=False	) ->						Optional[int]:
    __lowercase		:			str	    =       create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    __lowercase		:			str	    =       {"""torch""": """pt"""}

    # Locate actual dummy modules and read their content.
    __lowercase		:			int	    =       os.path.join(lowerCamelCase_	,					'''utils'''	)
    __lowercase		:			Tuple	    =       {
        backend: os.path.join(lowerCamelCase_	,					F'dummy_{short_names.get(lowerCamelCase_	,					lowerCamelCase_	)}_objects.py'	)
        for backend in dummy_files.keys()
    }

    __lowercase		:			Dict	    =       {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(lowerCamelCase_	):
            with open(lowerCamelCase_	,					'''r'''	,					encoding='''utf-8'''	,					newline='''\n'''	) as f:
                __lowercase		:			Tuple	    =       f.read()
        else:
            __lowercase		:			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(lowerCamelCase_	,					lowerCamelCase_	)}_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(lowerCamelCase_	,					lowerCamelCase_	)}_objects.py. Run `make fix-copies` '
                    '''to fix this.'''	)


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

 check_dummies(args.fix_and_overwrite)




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers import AutoModelForCausalLM


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Optional[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[int]	    =       tokenizer.decode(greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			List[Any]	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Tuple	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(greedy_ids[0]				)

          __lowercase		:			int	    =       TextIteratorStreamer(_snake_case				)
          __lowercase		:			int	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[Any]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()
          __lowercase		:			Optional[int]	    =       ''''''
          for new_text in streamer:
              streamer_text += new_text

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			str	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			List[Any]	    =       greedy_ids[:, input_ids.shape[1] :]
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(new_greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			int	    =       TextStreamer(_snake_case				,       skip_prompt=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			int	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Any				):
          # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
          # with actual models -- the dummy models' tokenizers are not aligned with their models, and
          # `skip_special_tokens=True` has no effect on them
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_snake_case				)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(_snake_case				):
              __lowercase		:			List[Any]	    =       ''''''
              for new_text in streamer:
                  streamer_text += new_text


