





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class   UpperCAmelCase      :
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Optional[int]		,			lowercase_ :Dict		,			lowercase_ :Tuple				)->				Optional[int]:
     return None






class   UpperCAmelCase      :
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict		,			lowercase_ :Dict		,			lowercase_ :Dict		,			lowercase_ :Dict				)->				Optional[Any]:
     return None






class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							[
     # (model_name, model_kwargs)
     ("""bert-base-cased""", {}),
     ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
 ]
 @require_tf
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Optional[Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_		,			"tf"		,			12		,			**lowercase_				)
 @require_torch
 @slow
 def 	UpperCAmelCase_				(							self :Tuple				)->				str:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_		,			"pt"		,			12		,			**lowercase_				)
 @require_torch
 @slow
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     from transformers import BertModel

     A__							=     ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
     with NamedTemporaryFile(mode="w+t"				) as vocab_file:
         vocab_file.write("\n".join(lowercase_				)				)
         vocab_file.flush()
         A__							=     BertTokenizerFast(vocab_file.name				)

     with TemporaryDirectory() as bert_save_dir:
         A__							=     BertModel(BertConfig(vocab_size=len(lowercase_				)				)				)
         model.save_pretrained(lowercase_				)
         self._test_export(lowercase_		,			"pt"		,			12		,			lowercase_				)
 @require_tf
 @slow
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Tuple:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         A__							=     self._test_export(lowercase_		,			"tf"		,			12		,			**lowercase_				)
         A__							=     quantize(Path(lowercase_				)				)

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowercase_				).stat().st_size:
             self.fail("Quantized model is bigger than initial ONNX model"				)
 @require_torch
 @slow
 def 	UpperCAmelCase_				(							self :Any				)->				Tuple:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         A__							=     self._test_export(lowercase_		,			"pt"		,			12		,			**lowercase_				)
         A__							=     quantize(lowercase_				)

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowercase_				).stat().st_size:
             self.fail("Quantized model is bigger than initial ONNX model"				)
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Optional[int]		,			lowercase_ :str		,			lowercase_ :List[Any]		,			lowercase_ :Tuple=None		,			**lowercase_ :Optional[Any]				)->				int:
     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             A__							=     Path(lowercase_				).joinpath("model.onnx"				)

         # Remove folder if exists
         if path.parent.exists():
             path.parent.rmdir()

         # Export
         convert(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				)

         return path
     except Exception as e:
         self.fail(lowercase_				)
 @require_torch
 @require_tokenizers
 @slow
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Tuple:
     from transformers import BertModel

     A__							=     BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
     A__							=     BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
     self._test_infer_dynamic_axis(lowercase_		,			lowercase_		,			"pt"				)
 @require_tf
 @require_tokenizers
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Any:
     from transformers import TFBertModel

     A__							=     TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
     A__							=     BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
     self._test_infer_dynamic_axis(lowercase_		,			lowercase_		,			"tf"				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Dict		,			lowercase_ :Union[str, Any]		,			lowercase_ :str				)->				Dict:
     A__							=     FeatureExtractionPipeline(lowercase_		,			lowercase_				)

     A__							=     ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
     A__,      A__,      A__,      A__							=     infer_shapes(lowercase_		,			lowercase_				)

     # Assert all variables are present
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     self.assertTrue(all(var_name in shapes for var_name in variable_names				)				)
     self.assertSequenceEqual(variable_names[:3]		,			lowercase_				)
     self.assertSequenceEqual(variable_names[3:]		,			lowercase_				)

     # Assert inputs are {0: batch, 1: sequence}
     for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
         self.assertDictEqual(shapes[var_name]		,			{0: "batch", 1: "sequence"}				)

     # Assert outputs are {0: batch, 1: sequence} and {0: batch}
     self.assertDictEqual(shapes["output_0"]		,			{0: "batch", 1: "sequence"}				)
     self.assertDictEqual(shapes["output_1"]		,			{0: "batch"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     A__							=     ["input_ids", "attention_mask", "token_type_ids"]
     A__							=     {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
     A__,      A__							=     ensure_valid_input(FuncContiguousArgs()		,			lowercase_		,			lowercase_				)

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(lowercase_				)		,			3				)

     # Should have exactly the same input names
     self.assertEqual(set(lowercase_				)		,			set(lowercase_				)				)

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(lowercase_		,			(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])				)

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     A__,      A__							=     ensure_valid_input(FuncNonContiguousArgs()		,			lowercase_		,			lowercase_				)

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(lowercase_				)		,			1				)
     self.assertEqual(len(lowercase_				)		,			1				)

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0]		,			tokens["input_ids"]				)
     self.assertEqual(ordered_input_names[0]		,			"input_ids"				)



 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				str:
     A__							=     generate_identified_filename(Path("/home/something/my_fake_model.onnx"				)		,			"-test"				)
     self.assertEqual("/home/something/my_fake_model-test.onnx"		,			generated.as_posix()				)










'''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
__lowerCAmelCase				:      Optional[Any]						      ="src/diffusers"
__lowerCAmelCase				:      Dict						      ="."


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





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Optional[Any]			):
    return line.startswith(_lowerCamelCase			) or len(_lowerCamelCase			) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"		,			_lowerCamelCase			) is not None





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     object_name.split("."			)
    A__							=     0

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

    with open(os.path.join(_lowerCamelCase		,			F"{module}.py"			)		,			"r"		,			encoding="utf-8"		,			newline="\n"			) as f:
        A__							=     f.readlines()

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

    A__							=     lines[start_index:line_index]
    return "".join(_lowerCamelCase			)


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





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
    A__							=     code.split("\n"			)
    A__							=     0
    while idx < len(_lowerCamelCase			) and len(lines[idx]			) == 0:
        idx += 1
    if idx < len(_lowerCamelCase			):
        return re.search(r"^(\s*)\S"		,			lines[idx]			).groups()[0]
    return ""





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple			):
    A__							=     len(get_indent(_lowerCamelCase			)			) > 0
    if has_indent:
        A__							=     F"class Bla:\n{code}"
    A__							=     black.Mode(target_versions={black.TargetVersion.PYaa}		,			line_length=1_19		,			preview=_lowerCamelCase			)
    A__							=     black.format_str(_lowerCamelCase		,			mode=_lowerCamelCase			)
    A__,      A__							=     style_docstrings_in_code(_lowerCamelCase			)
    return result[len("class Bla:\n"			) :] if has_indent else result





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

        A__							=     line_index + 1 if indent == theoretical_indent else line_index + 2
        A__							=     theoretical_indent
        A__							=     start_index

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

        A__							=     lines[start_index:line_index]
        A__							=     "".join(_lowerCamelCase			)

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

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

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

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





def 		UpperCamelCase							(				_lowerCamelCase   :   bool = False			):
    A__							=     glob.glob(os.path.join(_lowerCamelCase		,			"**/*.py"			)		,			recursive=_lowerCamelCase			)
    A__							=     []
    for filename in all_files:
        A__							=     is_copy_consistent(_lowerCamelCase		,			_lowerCamelCase			)
        diffs += [F"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
    if not overwrite and len(_lowerCamelCase			) > 0:
        A__							=     "\n".join(_lowerCamelCase			)
        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__":
  __lowerCAmelCase				:      Union[str, Any]						      =argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  __lowerCAmelCase				:      int						      =parser.parse_args()

  check_copies(args.fix_and_overwrite)



