




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFTransfoXLForSequenceClassification,
         TFTransfoXLLMHeadModel,
         TFTransfoXLModel,
     )





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     )				->	Union[str, Any]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			13
         lowerCAmelCase__          =			7
         lowerCAmelCase__          =			30
         lowerCAmelCase__          =			self.seq_length + self.mem_len
         lowerCAmelCase__          =			15
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			99
         lowerCAmelCase__          =			[10, 50, 80]
         lowerCAmelCase__          =			32
         lowerCAmelCase__          =			32
         lowerCAmelCase__          =			4
         lowerCAmelCase__          =			8
         lowerCAmelCase__          =			128
         lowerCAmelCase__          =			2
         lowerCAmelCase__          =			2
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			1
         lowerCAmelCase__          =			0
         lowerCAmelCase__          =			3
         lowerCAmelCase__          =			self.vocab_size - 1
         lowerCAmelCase__          =			0.01


    def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_labels:
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			TransfoXLConfig(
             vocab_size=self.vocab_size       ,     mem_len=self.mem_len       ,     clamp_len=self.clamp_len       ,     cutoffs=self.cutoffs       ,     d_model=self.hidden_size       ,     d_embed=self.d_embed       ,     n_head=self.num_attention_heads       ,     d_head=self.d_head       ,     d_inner=self.d_inner       ,     div_val=self.div_val       ,     n_layer=self.num_hidden_layers       ,     eos_token_id=self.eos_token_id       ,     pad_token_id=self.vocab_size - 1       ,     init_range=self.init_range       ,     num_labels=self.num_labels       ,     )

         return (config, input_ids_a, input_ids_a, lm_labels)


    def        a				(	self					:					Tuple       )				->	List[str]:
         random.seed(self.seed       )
         tf.random.set_seed(self.seed       )


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Any:
         lowerCAmelCase__          =			TFTransfoXLModel(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

         lowerCAmelCase__          =			{"input_ids": input_ids_a, "mems": mems_a}

         lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(hidden_states_a.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]       ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,     )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]       ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,     )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	str:
         lowerCAmelCase__          =			TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

         lowerCAmelCase__          =			{"input_ids": input_ids_a, "labels": lm_labels}
         lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

         lowerCAmelCase__     ,		lowerCAmelCase__          =			model([input_ids_a, mems_a]       ).to_tuple()

         lowerCAmelCase__          =			{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

         lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]       ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,     )

         self.parent.assertEqual(lm_logits_a.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]       ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,     )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Tuple:
         lowerCAmelCase__          =			TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )


    def        a				(	self					:					Union[str, Any]       )				->	List[Any]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         ((lowerCAmelCase__)     ,		(lowerCAmelCase__)     ,		(lowerCAmelCase__)     ,		(lowerCAmelCase__))          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids_a}
         return config, inputs_dict






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




    """simple docstring"""

    snake_case__ =				(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    snake_case__ =				() if is_tf_available() else ()
    snake_case__ =				(
        {
            "feature-extraction": TFTransfoXLModel,
            "text-classification": TFTransfoXLForSequenceClassification,
            "text-generation": TFTransfoXLLMHeadModel,
            "zero-shot": TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Tuple:
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
              # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
              # tokenizer.
              return True

         return False


    def        a				(	self					:					Dict       )				->	List[str]:
         lowerCAmelCase__          =			TFTransfoXLModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     d_embed=37       )


    def        a				(	self					:					Union[str, Any]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Any       )				->	Dict:
         self.model_tester.set_seed()
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         self.model_tester.set_seed()
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Any       )				->	Tuple:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__          =			[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              assert isinstance(model.get_input_embeddings()       ,     tf.keras.layers.Layer       )
              if model_class in list_other_models_with_output_ebd:
                   lowerCAmelCase__          =			model.get_output_embeddings()
                   assert isinstance(SCREAMING_SNAKE_CASE__       ,     tf.keras.layers.Layer       )
                   lowerCAmelCase__          =			model.get_bias()
                   assert name is None
              else:
                   lowerCAmelCase__          =			model.get_output_embeddings()
                   assert x is None
                   lowerCAmelCase__          =			model.get_bias()
                   assert name is None


    def        a				(	self					:					Any       )				->	Tuple:
         # TODO JP: Make TransfoXL XLA compliant
         pass


    @slow
    def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    @unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."       )
    def        a				(	self					:					Union[str, Any]       )				->	int:
         pass






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @unittest.skip("Skip test until #12651 is resolved."       )
    @slow
    def        a				(	self					:					Union[str, Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"       )
         # fmt: off
         lowerCAmelCase__          =			tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]       ,     dtype=tf.intaa       )  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         lowerCAmelCase__          =			[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         lowerCAmelCase__          =			model.generate(SCREAMING_SNAKE_CASE__       ,     max_length=200       ,     do_sample=SCREAMING_SNAKE_CASE__       )
         self.assertListEqual(output_ids[0].numpy().tolist()       ,     SCREAMING_SNAKE_CASE__       )






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
UpperCamelCase								=					'src/diffusers'
UpperCamelCase								=					'.'


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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     return line.startswith(lowerCAmelCase_      ) or len(lowerCAmelCase_      ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"       ,    lowerCAmelCase_      ) is not None



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			object_name.split("."      )
     lowerCAmelCase__          =			0

     # First let's find the module where our object lives.
     lowerCAmelCase__          =			parts[i]
     while i < len(lowerCAmelCase_      ) and not os.path.isfile(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )      ):
          i += 1
          if i < len(lowerCAmelCase_      ):
               lowerCAmelCase__          =			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:
          lowerCAmelCase__          =			f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase__          =			""
     lowerCAmelCase__          =			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).
     lowerCAmelCase__          =			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

     lowerCAmelCase__          =			lines[start_index:line_index]
     return "".join(lowerCAmelCase_      )


UpperCamelCase								=					re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase								=					re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase								=					re.compile(R'<FILL\s+[^>]*>')



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			code.split("\n"      )
     lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(get_indent(lowerCAmelCase_      )      ) > 0
     if has_indent:
          lowerCAmelCase__          =			F'class Bla:\n{code}'
     lowerCAmelCase__          =			black.Mode(target_versions={black.TargetVersion.PYaa}       ,    line_length=119       ,    preview=lowerCAmelCase_      )
     lowerCAmelCase__          =			black.format_str(lowerCAmelCase_       ,    mode=lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			style_docstrings_in_code(lowerCAmelCase_      )
     return result[len("class Bla:\n"      ) :] if has_indent else result



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

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

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

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase__          =			True
          while line_index < len(lowerCAmelCase_      ) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase_      ):
                    break
               lowerCAmelCase__          =			lines[line_index]
               lowerCAmelCase__          =			_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

          lowerCAmelCase__          =			lines[start_index:line_index]
          lowerCAmelCase__          =			"".join(lowerCAmelCase_      )

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

          # Before comparing, use the `replace_pattern` on the original code.
          if len(lowerCAmelCase_      ) > 0:
               lowerCAmelCase__          =			replace_pattern.replace("with"       ,    ""      ).split(","      )
               lowerCAmelCase__          =			[_re_replace_pattern.search(lowerCAmelCase_      ) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			pattern.groups()
                    lowerCAmelCase__          =			re.sub(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
                    if option.strip() == "all-casing":
                         lowerCAmelCase__          =			re.sub(obja.lower()       ,    obja.lower()       ,    lowerCAmelCase_      )
                         lowerCAmelCase__          =			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
               lowerCAmelCase__          =			blackify(lines[start_index - 1] + theoretical_code      )
               lowerCAmelCase__          =			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:
                    lowerCAmelCase__          =			lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			glob.glob(os.path.join(lowerCAmelCase_       ,    "**/*.py"      )       ,    recursive=lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     for filename in all_files:
          lowerCAmelCase__          =			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:
          lowerCAmelCase__          =			"\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__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     UpperCamelCase								=					parser.parse_args()

     check_copies(args.fix_and_overwrite)
