






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1_4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=7  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : List[str]=False  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=9_9  ,							SCREAMING_SNAKE_CASE__       : List[Any]=3_2  ,							SCREAMING_SNAKE_CASE__       : Any=4  ,							SCREAMING_SNAKE_CASE__       : Tuple=4  ,							SCREAMING_SNAKE_CASE__       : List[Any]=4  ,							SCREAMING_SNAKE_CASE__       : Tuple=3_7  ,							SCREAMING_SNAKE_CASE__       : Tuple="gelu"  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Dict=0.02  ,							)	->    Dict:
             a_      :	Tuple							=						parent
             a_      :	Any							=						batch_size
             a_      :	Optional[Any]							=						seq_length
             a_      :	Optional[int]							=						is_training
             a_      :	Dict							=						use_input_mask
             a_      :	List[Any]							=						use_token_type_ids
             a_      :	List[Any]							=						use_labels
             a_      :	Optional[Any]							=						vocab_size
             a_      :	str							=						hidden_size
             a_      :	List[Any]							=						rotary_dim
             a_      :	int							=						num_hidden_layers
             a_      :	str							=						num_attention_heads
             a_      :	Any							=						intermediate_size
             a_      :	Optional[int]							=						hidden_act
             a_      :	List[Any]							=						hidden_dropout_prob
             a_      :	Optional[Any]							=						attention_probs_dropout_prob
             a_      :	int							=						max_position_embeddings
             a_      :	Tuple							=						initializer_range
             a_      :	List[str]							=						None
             a_      :	Union[str, Any]							=						vocab_size - 1
             a_      :	Tuple							=						vocab_size - 1
             a_      :	Optional[Any]							=						vocab_size - 1
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             a_      :	List[str]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

             a_      :	int							=						None
             if self.use_input_mask:
                    a_      :	Optional[Any]							=						random_attention_mask([self.batch_size, self.seq_length]  )

             a_      :	List[str]							=						GPTJConfig(
                 vocab_size=self.vocab_size  ,							n_embd=self.hidden_size  ,							n_layer=self.num_hidden_layers  ,							n_head=self.num_attention_heads  ,							n_positions=self.max_position_embeddings  ,							use_cache=SCREAMING_SNAKE_CASE__  ,							bos_token_id=self.bos_token_id  ,							eos_token_id=self.eos_token_id  ,							pad_token_id=self.pad_token_id  ,							rotary_dim=self.rotary_dim  ,							)

             return (config, input_ids, input_mask)
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             a_      :	Union[str, Any]							=						self.prepare_config_and_inputs()
             a_      :	Optional[Any]							=						config_and_inputs
             a_      :	int							=						{'input_ids': input_ids, 'attention_mask': attention_mask}
             return config, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    List[Any]:
             a_      :	str							=						2_0
             a_      :	Any							=						model_class_name(SCREAMING_SNAKE_CASE__  )

             a_      :	str							=						model.init_cache(input_ids.shape[0]  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						jnp.ones((input_ids.shape[0], max_decoder_length)  ,							dtype='i4'  )

             a_      :	Union[str, Any]							=						jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1  )[None, :]  ,							(input_ids.shape[0], input_ids.shape[-1] - 1)  )
             a_      :	Any							=						model(
                 input_ids[:, :-1]  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)

             a_      :	List[str]							=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,							dtype='i4'  )
             a_      :	Union[str, Any]							=						model(
                 input_ids[:, -1:]  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=outputs_cache.past_key_values  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)

             a_      :	Union[str, Any]							=						model(SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
             self.parent.assertTrue(diff < 1E-3  ,							msg=F"""Max diff is {diff}"""  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    str:
             a_      :	Optional[Any]							=						2_0
             a_      :	int							=						model_class_name(SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						jnp.concatenate(
                 [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]  ,							axis=-1  ,							)

             a_      :	str							=						model.init_cache(input_ids.shape[0]  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Union[str, Any]							=						jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1  )[None, :]  ,							(input_ids.shape[0], input_ids.shape[-1] - 1)  )

             a_      :	Tuple							=						model(
                 input_ids[:, :-1]  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	Tuple							=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,							dtype='i4'  )
             a_      :	List[Any]							=						model(
                 input_ids[:, -1:]  ,							past_key_values=outputs_cache.past_key_values  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)

             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
             self.parent.assertTrue(diff < 1E-3  ,							msg=F"""Max diff is {diff}"""  )
@require_flax
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  int       =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      snake_case__       :  Any       =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             a_      :	int							=						FlaxGPTJModelTester(self  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             for model_class_name in self.all_model_classes:
                    a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             for model_class_name in self.all_model_classes:
                    a_      :	Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      @tooslow
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[str]:
             a_      :	str							=						GPTaTokenizer.from_pretrained('gpt2'  ,							pad_token='<|endoftext|>'  ,							padding_side='left'  )
             a_      :	Union[str, Any]							=						tokenizer(['Hello this is a long string', 'Hey']  ,							return_tensors='np'  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  )

             a_      :	List[Any]							=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  )
             a_      :	List[str]							=						False
             a_      :	Optional[int]							=						model.config.eos_token_id

             a_      :	Optional[int]							=						jax.jit(model.generate  )

             a_      :	Optional[int]							=						jit_generate(
                 inputs['input_ids']  ,							attention_mask=inputs['attention_mask']  ,							pad_token_id=tokenizer.pad_token_id  ).sequences

             a_      :	List[str]							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						[
                 'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
                 'Hey, I\'m a little late to the party. I\'m going to',
             ]

             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      @is_pt_flax_cross_test
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             a_      :	Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__  ):
                           # prepare inputs
                           a_      :	Any							=						self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	Any							=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           a_      :	Dict							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           a_      :	Optional[Any]							=						getattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                           a_      :	Tuple							=						pt_inputs['input_ids'].shape
                           a_      :	int							=						np.random.randint(0  ,							seq_length - 1  ,							size=(batch_size,)  )
                           for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__  ):
                                  a_      :	Union[str, Any]							=						0
                                  a_      :	Any							=						1
                                  a_      :	int							=						0
                                  a_      :	List[str]							=						1
                           a_      :	Optional[Any]							=						pt_model_class(SCREAMING_SNAKE_CASE__  ).eval()
                           a_      :	str							=						model_class(SCREAMING_SNAKE_CASE__  ,							dtype=jnp.floataa  )

                           a_      :	Any							=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	Optional[int]							=						fx_state

                           with torch.no_grad():
                                  a_      :	Tuple							=						pt_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()

                           a_      :	Optional[int]							=						fx_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()
                           self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,							len(SCREAMING_SNAKE_CASE__  )  ,							'Output lengths differ between Flax and PyTorch'  )
                           for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                                  self.assert_almost_equals(fx_output[:, -1]  ,							pt_output[:, -1].numpy()  ,							4E-2  )

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  pt_model.save_pretrained(SCREAMING_SNAKE_CASE__  )
                                  a_      :	Optional[int]							=						model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							from_pt=SCREAMING_SNAKE_CASE__  )

                           a_      :	Dict							=						fx_model_loaded(**SCREAMING_SNAKE_CASE__  ).to_tuple()
                           self.assertEqual(
                               len(SCREAMING_SNAKE_CASE__  )  ,							len(SCREAMING_SNAKE_CASE__  )  ,							'Output lengths differ between Flax and PyTorch'  )
                           for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                                  self.assert_almost_equals(fx_output_loaded[:, -1]  ,							pt_output[:, -1].numpy()  ,							4E-2  )
      @is_pt_flax_cross_test
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__  ):
                           # prepare inputs
                           a_      :	Dict							=						self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	List[str]							=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           a_      :	str							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           a_      :	Union[str, Any]							=						getattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                           a_      :	int							=						pt_model_class(SCREAMING_SNAKE_CASE__  ).eval()
                           a_      :	List[str]							=						model_class(SCREAMING_SNAKE_CASE__  ,							dtype=jnp.floataa  )

                           a_      :	Tuple							=						load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__  ,							fx_model.params  )
                           a_      :	Any							=						pt_inputs['input_ids'].shape
                           a_      :	List[Any]							=						np.random.randint(0  ,							seq_length - 1  ,							size=(batch_size,)  )
                           for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__  ):
                                  a_      :	Dict							=						0
                                  a_      :	List[Any]							=						1
                                  a_      :	str							=						0
                                  a_      :	Union[str, Any]							=						1

                           # make sure weights are tied in PyTorch
                           pt_model.tie_weights()

                           with torch.no_grad():
                                  a_      :	List[Any]							=						pt_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()

                           a_      :	List[str]							=						fx_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()
                           self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,							len(SCREAMING_SNAKE_CASE__  )  ,							'Output lengths differ between Flax and PyTorch'  )
                           for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                                  self.assert_almost_equals(fx_output[:, -1]  ,							pt_output[:, -1].numpy()  ,							4E-2  )

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  fx_model.save_pretrained(SCREAMING_SNAKE_CASE__  )
                                  a_      :	Optional[int]							=						pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							from_flax=SCREAMING_SNAKE_CASE__  )

                           with torch.no_grad():
                                  a_      :	int							=						pt_model_loaded(**SCREAMING_SNAKE_CASE__  ).to_tuple()

                           self.assertEqual(
                               len(SCREAMING_SNAKE_CASE__  )  ,							len(SCREAMING_SNAKE_CASE__  )  ,							'Output lengths differ between Flax and PyTorch'  )
                           for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                                  self.assert_almost_equals(fx_output[:, -1]  ,							pt_output[:, -1].numpy()  ,							4E-2  )

      @tooslow
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             for model_class_name in self.all_model_classes:
                    a_      :	Union[str, Any]							=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'  )
                    a_      :	Tuple							=						model(np.ones((1, 1)  )  )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






import argparse
import os
import re


UpperCAmelCase_   :				List[Any]						      = 'src/transformers'

# Pattern that looks at the indentation in a line.
UpperCAmelCase_   :				Any						      = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_   :				str						      = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_   :				Dict						      = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_   :				int						      = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_   :				List[str]						      = re.compile(R'\[([^\]]+)\]')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						_re_indent.search(__A       )
       return "" if search is None else search.groups()[0]







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]=""						,					__A				:    Dict=None						,					__A				:    Dict=None       )			->	int:

       """simple docstring"""

       a_      :	Tuple							=						0
       a_      :	Dict							=						code.split('\n'       )
       if start_prompt is not None:
              while not lines[index].startswith(__A       ):
                     index += 1
              a_      :	List[Any]							=						['\n'.join(lines[:index]       )]
       else:
              a_      :	List[str]							=						[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_      :	Tuple							=						[lines[index]]
       index += 1
       while index < len(__A       ) and (end_prompt is None or not lines[index].startswith(__A       )):
              if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
                     if len(__A       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ' '       ):
                            current_block.append(lines[index]       )
                            blocks.append('\n'.join(__A       )       )
                            if index < len(__A       ) - 1:
                                   a_      :	Dict							=						[lines[index + 1]]
                                   index += 1
                            else:
                                   a_      :	Dict							=						[]
                     else:
                            blocks.append('\n'.join(__A       )       )
                            a_      :	int							=						[lines[index]]
              else:
                     current_block.append(lines[index]       )
              index += 1

       # Adds current block if it's nonempty.
       if len(__A       ) > 0:
              blocks.append('\n'.join(__A       )       )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(__A       ):
              blocks.append('\n'.join(lines[index:]       )       )

       return blocks







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	Any:

       """simple docstring"""

       def _inner(__A				:    Tuple       ):
              return key(__A       ).lower().replace('_'						,					''       )

       return _inner







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    List[Any]=None       )			->	List[str]:

       """simple docstring"""

       def noop(__A				:    Tuple       ):
              return x

       if key is None:
              a_      :	Optional[Any]							=						noop
       # Constants are all uppercase, they go first.
       a_      :	List[Any]							=						[obj for obj in objects if key(__A       ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_      :	Dict							=						[obj for obj in objects if key(__A       )[0].isupper() and not key(__A       ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_      :	Optional[int]							=						[obj for obj in objects if not key(__A       )[0].isupper()]

       a_      :	Optional[Any]							=						ignore_underscore(__A       )
       return sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Optional[int]:

       """simple docstring"""

       def _replace(__A				:    List[Any]       ):
              a_      :	Tuple							=						match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_      :	int							=						[part.strip().replace('"'						,					''       ) for part in imports.split(','       )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]       ) == 0:
                     a_      :	str							=						keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__A       )]       ) + "]"

       a_      :	Optional[int]							=						import_statement.split('\n'       )
       if len(__A       ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_      :	int							=						2 if lines[1].strip() == '[' else 1
              a_      :	int							=						[(i, _re_strip_line.search(__A       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
              a_      :	str							=						sort_objects(__A						,					key=lambda __A       : x[1]       )
              a_      :	int							=						[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
       elif len(__A       ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]       ) is not None:
                     a_      :	str							=						_re_bracket_content.sub(_replace						,					lines[1]       )
              else:
                     a_      :	List[str]							=						[part.strip().replace('"'						,					''       ) for part in lines[1].split(','       )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]       ) == 0:
                            a_      :	Optional[int]							=						keys[:-1]
                     a_      :	Any							=						get_indent(lines[1]       ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__A       )]       )
              return "\n".join(__A       )
       else:
              # Finally we have to deal with imports fitting on one line
              a_      :	Union[str, Any]							=						_re_bracket_content.sub(_replace						,					__A       )
              return import_statement







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any=True       )			->	Union[str, Any]:

       """simple docstring"""

       with open(__A						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_      :	Optional[Any]							=						split_code_in_indented_blocks(
           __A						,					start_prompt='_import_structure = {'						,					end_prompt='if TYPE_CHECKING:'       )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1						,					len(__A       ) - 1       ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_      :	Any							=						main_blocks[block_idx]
              a_      :	Any							=						block.split('\n'       )

              # Get to the start of the imports.
              a_      :	Any							=						0
              while line_idx < len(__A       ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_      :	Tuple							=						len(__A       )
                     else:
                            line_idx += 1
              if line_idx >= len(__A       ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_      :	List[str]							=						'\n'.join(block_lines[line_idx:-1]       )
              a_      :	List[Any]							=						get_indent(block_lines[1]       )
              # Slit the internal block into blocks of indent level 1.
              a_      :	Optional[int]							=						split_code_in_indented_blocks(__A						,					indent_level=__A       )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_      :	Tuple							=						_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_      :	Union[str, Any]							=						[(pattern.search(__A       ).groups()[0] if pattern.search(__A       ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_      :	str							=						[(i, key) for i, key in enumerate(__A       ) if key is not None]
              a_      :	Optional[int]							=						[x[0] for x in sorted(__A						,					key=lambda __A       : x[1]       )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_      :	int							=						0
              a_      :	int							=						[]
              for i in range(len(__A       )       ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]       )
                     else:
                            a_      :	List[str]							=						sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
                            reorderded_blocks.append(__A       )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_      :	Any							=						'\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

       if code != "\n".join(__A       ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""       )
                     with open(__A						,					'w'						,					encoding='utf-8'       ) as f:
                            f.write('\n'.join(__A       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for root, _, files in os.walk(__A       ):
              if "__init__.py" in files:
                     a_      :	Dict							=						sort_imports(os.path.join(__A						,					'__init__.py'       )						,					check_only=__A       )
                     if result:
                            a_      :	Tuple							=						[os.path.join(__A						,					'__init__.py'       )]
       if len(__A       ) > 0:
              raise ValueError(F"""Would overwrite {len(__A       )} files, run `make style`."""       )


if __name__ == "__main__":
   UpperCAmelCase_   :				int						      = argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   UpperCAmelCase_   :				Tuple						      = parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)


