





"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=7    ,  a__=True    ,  a__=True    ,  a__=False    ,  a__=True    ,  a__=99    ,  a__=32    ,  a__=5    ,  a__=4    ,  a__=37    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=16    ,  a__=2    ,  a__=0.02    ,  a__=3    ,  a__=4    ,  a__=None    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     seq_length
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     use_input_mask
              _UpperCAmelCase     =     use_token_type_ids
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     type_sequence_label_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     num_labels
              _UpperCAmelCase     =     num_choices
              _UpperCAmelCase     =     scope






       def 							__A (   self     ):
              _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size     )

              _UpperCAmelCase     =     None
              if self.use_input_mask:
                     _UpperCAmelCase     =     random_attention_mask([self.batch_size, self.seq_length]     )

              _UpperCAmelCase     =     None
              if self.use_token_type_ids:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size     )

              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              if self.use_labels:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.type_sequence_label_size     )
                     _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels     )
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.num_choices     )

              _UpperCAmelCase     =     self.get_config()

              return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def 							__A (   self     ):
              return LlamaConfig(
                  vocab_size=self.vocab_size    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  max_position_embeddings=self.max_position_embeddings    ,  type_vocab_size=self.type_vocab_size    ,  is_decoder=a__    ,  initializer_range=self.initializer_range    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     LlamaModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     LlamaModel(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  )
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  )
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     LlamaForCausalLM(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     LlamaForCausalLM(config=a__     )
              model.to(a__     )
              model.eval()

              # first forward pass
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  use_cache=a__    ,  )
              _UpperCAmelCase     =     outputs.past_key_values

              # create hypothetical multiple next token and extent to next_input_ids
              _UpperCAmelCase     =     ids_tensor((self.batch_size, 3)    ,  config.vocab_size     )
              _UpperCAmelCase     =     ids_tensor((self.batch_size, 3)    ,  vocab_size=2     )

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

              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  output_hidden_states=a__    ,  )['hidden_states'][0]
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  past_key_values=a__    ,  output_hidden_states=a__    ,  )['hidden_states'][0]

              # select random slice
              _UpperCAmelCase     =     ids_tensor((1,)    ,  output_from_past.shape[-1]     ).item()
              _UpperCAmelCase     =     output_from_no_past[:, -3:, random_slice_idx].detach()
              _UpperCAmelCase     =     output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(a__    ,  a__    ,  atol=1E-3     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              (
                  (
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,														
              )     =     config_and_inputs
              _UpperCAmelCase     =     {'input_ids': input_ids, 'attention_mask': input_mask}
              return config, inputs_dict



@require_torch
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
       lowerCAmelCase__						 =  (LlamaForCausalLM,) if is_torch_available() else ()
       lowerCAmelCase__						 =  (
           {
               """feature-extraction""": LlamaModel,
               """text-classification""": LlamaForSequenceClassification,
               """text-generation""": LlamaForCausalLM,
               """zero-shot""": LlamaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     LlamaModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  hidden_size=37     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     _UpperCAmelCase     =     type
                     self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor([self.model_tester.batch_size]    ,  self.model_tester.type_sequence_label_size     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     'single_label_classification'
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor([self.model_tester.batch_size]    ,  self.model_tester.type_sequence_label_size     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     'multi_label_classification'
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor(
                  [self.model_tester.batch_size, config.num_labels]    ,  self.model_tester.type_sequence_label_size     ).to(torch.float     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'     )
       def 							__A (   self     ):
              pass






       @parameterized.expand([('linear',), ('dynamic',)]     )
       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     ids_tensor([1, 10]    ,  config.vocab_size     )
              _UpperCAmelCase     =     ids_tensor([1, int(config.max_position_embeddings * 1.5     )]    ,  config.vocab_size     )

              set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
              _UpperCAmelCase     =     LlamaModel(a__     )
              original_model.to(a__     )
              original_model.eval()
              _UpperCAmelCase     =     original_model(a__     ).last_hidden_state
              _UpperCAmelCase     =     original_model(a__     ).last_hidden_state

              set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
              _UpperCAmelCase     =     {'type': scaling_type, 'factor': 10.0}
              _UpperCAmelCase     =     LlamaModel(a__     )
              scaled_model.to(a__     )
              scaled_model.eval()
              _UpperCAmelCase     =     scaled_model(a__     ).last_hidden_state
              _UpperCAmelCase     =     scaled_model(a__     ).last_hidden_state

              # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
              # maximum sequence length, so the outputs for the short input should match.
              if scaling_type == "dynamic":
                     self.assertTrue(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )
              else:
                     self.assertFalse(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )

              # The output should be different for long inputs
              self.assertFalse(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )



@require_torch
class 	lowerCAmelCase					(      unittest.TestCase							):






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor([input_ids]     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]     )
              # fmt: on
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )






       @unittest.skip(
           'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )

              _UpperCAmelCase     =     torch.tensor(
                  [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]    ,  dtype=torch.floataa     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Model is curently gated'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
              _UpperCAmelCase     =     'Simply put, the theory of relativity states that '
              _UpperCAmelCase     =     LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'     )
              _UpperCAmelCase     =     tokenizer.encode(a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained(
                  'meta-llama/Llama-2-13b-chat-hf'    ,  device_map='sequential'    ,  use_safetensors=a__     )

              # greedy generation outputs
              _UpperCAmelCase     =     model.generate(a__    ,  max_new_tokens=64    ,  top_p=a__    ,  temperature=1    ,  do_sample=a__     )
              _UpperCAmelCase     =     tokenizer.decode(generated_ids[0]    ,  skip_special_tokens=a__     )
              self.assertEqual(a__    ,  a__     )













"""simple docstring"""






import argparse
import os
import re


lowerCAmelCase_			=							'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_			=							re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
lowerCAmelCase_			=							re.compile(r'''\s*\(\s*"(\S[^"]+)"''')

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = False							)		->       Optional[Any]:






       """simple docstring"""




       with open(SCREAMING_SNAKE_CASE,'r',encoding='utf-8'							) as f:
              _UpperCAmelCase     =     f.read()

       _UpperCAmelCase     =     content.split('\n'							)
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     0
       while line_idx < len(SCREAMING_SNAKE_CASE							):
              if _re_intro_mapping.search(lines[line_idx]							) is not None:
                     _UpperCAmelCase     =     len(re.search(R'^(\s*)\S',lines[line_idx]							).groups()[0]							) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(' ' * indent + '('							):
                            new_lines.append(lines[line_idx]							)
                            line_idx += 1

                     _UpperCAmelCase     =     []
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   _UpperCAmelCase     =     line_idx
                                   while not lines[line_idx].startswith(' ' * indent + ')'							):
                                          line_idx += 1
                                   blocks.append('\n'.join(lines[start_idx : line_idx + 1]							)							)
                            else:
                                   blocks.append(lines[line_idx]							)
                            line_idx += 1

                     # Sort blocks by their identifiers
                     _UpperCAmelCase     =     sorted(SCREAMING_SNAKE_CASE,key=lambda SCREAMING_SNAKE_CASE							: _re_identifier.search(SCREAMING_SNAKE_CASE							).groups()[0]							)
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]							)
                     line_idx += 1

       if overwrite:
              with open(SCREAMING_SNAKE_CASE,'w',encoding='utf-8'							) as f:
                     f.write('\n'.join(SCREAMING_SNAKE_CASE							)							)
       elif "\n".join(SCREAMING_SNAKE_CASE							) != content:
              return True

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = False							)		->       Union[str, Any]:






       """simple docstring"""




       _UpperCAmelCase     =     [os.path.join(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							) for f in os.listdir(SCREAMING_SNAKE_CASE							) if f.endswith('.py'							)]
       _UpperCAmelCase     =     [sort_auto_mapping(SCREAMING_SNAKE_CASE,overwrite=SCREAMING_SNAKE_CASE							) for fname in fnames]

       if not overwrite and any(SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     [f for f, d in zip(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							) if d]
              raise ValueError(
                  F"""The following files have auto mappings that need sorting: {", ".join(SCREAMING_SNAKE_CASE							)}. Run `make style` to fix"""
                  ' this.'							)


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

    sort_all_auto_mappings(not args.check_only)






