


import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
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 (
       FalconForCausalLM,
       FalconForQuestionAnswering,
       FalconForSequenceClassification,
       FalconForTokenClassification,
       FalconModel,
   )







class lowerCAmelCase_		:

 '''simple docstring'''

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size      )

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

       snake_case_ =   None

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

       snake_case_ =   self.get_config()

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



 def 		UpperCamelCase__  (			self      ):
       return FalconConfig(
           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=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   pad_token_id=1	,   new_decoder_architecture=_UpperCAmelCase	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   FalconModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )



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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   FalconForCausalLM(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   True
       snake_case_ =   True
       snake_case_ =   FalconForCausalLM(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

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

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

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

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

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





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







@require_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (
         FalconModel,
         FalconForCausalLM,
         FalconForSequenceClassification,
         FalconForTokenClassification,
         FalconForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __snake_case				=						(FalconForCausalLM,) if is_torch_available() else ()
 __snake_case				=						(
     {
         "feature-extraction": FalconModel,
         "text-classification": FalconForSequenceClassification,
         "text-generation": FalconForCausalLM,
         "question-answering": FalconForQuestionAnswering,
         "token-classification": FalconForTokenClassification,
         "zero-shot": FalconForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __snake_case				=						False
 __snake_case				=						False



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FalconModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   hidden_size=37      )



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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					*snake_case_ =   self.model_tester.prepare_config_and_inputs()
       for alibi in [True, False]:
             snake_case_ =   alibi
             self.model_tester.create_and_check_model(_UpperCAmelCase	,   *_UpperCAmelCase      )



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



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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_ =   input_dict['''input_ids''']
       snake_case_ =   FalconForCausalLM(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   use_cache=_UpperCAmelCase      )
       snake_case_ =   input_ids.shape[0]
       snake_case_ =   model._convert_to_rw_cache(result.past_key_values      )
       snake_case_ =   model._convert_cache_to_standard_format(_UpperCAmelCase	,   _UpperCAmelCase      )
       for layer in range(len(_UpperCAmelCase      )      ):
             for tensor_idx in range(2      ):
                   self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3      )
                   self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4      )
                   self.assertTrue(
                       torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]      )      )



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





 def 		UpperCamelCase__  (			self      ):
       # Falcon can have different numbers of KV-heads than the number of query heads, so we need
       # to override this test to use the right head counts.
       for model_class in self.all_generative_model_classes:
             snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

             # If it doesn't support cache, pass the test
             if not hasattr(_UpperCAmelCase	,   '''use_cache'''      ):
                   return

             snake_case_ =   model_class(_UpperCAmelCase      ).to(_UpperCAmelCase      )
             if "use_cache" not in inputs:
                   snake_case_ =   True
             snake_case_ =   model(**_UpperCAmelCase      )

             # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
             if "past_key_values" not in outputs:
                   return

             snake_case_ =   (
                 getattr(_UpperCAmelCase	,   '''decoder_layers'''	,   _UpperCAmelCase      )
                 or getattr(_UpperCAmelCase	,   '''num_decoder_layers'''	,   _UpperCAmelCase      )
                 or config.num_hidden_layers
             )
             snake_case_ =   getattr(_UpperCAmelCase	,   '''num_kv_heads'''	,   config.num_attention_heads      )
             snake_case_ =   getattr(_UpperCAmelCase	,   '''d_model'''	,   config.hidden_size      )
             snake_case_ =   embed_dim // num_attention_heads

             snake_case_ =   outputs['''past_key_values''']
             self.assertEqual(len(_UpperCAmelCase      )	,   _UpperCAmelCase      )

             snake_case_ ,					snake_case_ =   inputs['''input_ids'''].shape
             for i in range(_UpperCAmelCase      ):
                   if config.new_decoder_architecture:
                         snake_case_ =   config.num_attention_heads
                   elif config.multi_query:
                         snake_case_ =   1
                   self.assertEqual(len(past_kv[0]      )	,   2      )  # K V for the decoder = 2
                   self.assertEqual(
                       past_kv[i][0].shape	,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )
                   self.assertEqual(
                       past_kv[i][1].shape	,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )







@require_torch
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''      )
       snake_case_ =   FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''      )
       model.eval()
       model.to(_UpperCAmelCase      )
       snake_case_ =   tokenizer('''My favorite food is'''	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

       snake_case_ =   (
           '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
       )

       snake_case_ =   model.generate(**_UpperCAmelCase	,   do_sample=_UpperCAmelCase	,   max_new_tokens=19      )
       snake_case_ =   tokenizer.batch_decode(_UpperCAmelCase      )[0]

       self.assertEqual(_UpperCAmelCase	,   _UpperCAmelCase      )



 @slow
 def 		UpperCamelCase__  (			self      ):
       # The big models are way too big for the CI, so we use tiny random models that resemble their
       # architectures but with much smaller and fewer layers
       for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
             snake_case_ =   FalconForCausalLM.from_pretrained(_UpperCAmelCase      )
             model.eval()
             model.to(_UpperCAmelCase      )
             snake_case_ =   tokenizer('''My favorite food is'''	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

             # We just test that these run without errors - the models are randomly initialized
             # and so the actual text outputs will be garbage
             model.generate(**_UpperCAmelCase	,   do_sample=_UpperCAmelCase	,   max_new_tokens=4      )
             model.generate(**_UpperCAmelCase	,   do_sample=_UpperCAmelCase	,   max_new_tokens=4      )
             model.generate(**_UpperCAmelCase	,   num_beams=2	,   max_new_tokens=4      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       # The big models are way too big for the CI, so we use tiny random models that resemble their
       # architectures but with much smaller and fewer layers
       with torch.no_grad():
             for repo in [
                 "Rocketknight1/falcon-rw-1b",
                 "Rocketknight1/tiny-random-falcon-7b",
                 "Rocketknight1/tiny-random-falcon-40b",
             ]:
                   snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
                   snake_case_ =   FalconForCausalLM.from_pretrained(_UpperCAmelCase      )
                   model.eval()
                   model.to(device=_UpperCAmelCase      )
                   snake_case_ =   tokenizer('''My favorite food is'''	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

                   # Test results are the same with and without cache
                   snake_case_ =   model.generate(**_UpperCAmelCase	,   do_sample=_UpperCAmelCase	,   max_new_tokens=20	,   use_cache=_UpperCAmelCase      )
                   snake_case_ =   model.generate(**_UpperCAmelCase	,   do_sample=_UpperCAmelCase	,   max_new_tokens=20	,   use_cache=_UpperCAmelCase      )
                   self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )



import copy
import re







class lowerCAmelCase_		:

 '''simple docstring'''

 __snake_case				=						"hp"
 __snake_case				=						{}
 __snake_case				=						None



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   prefix
       snake_case_ =   defaults
       cls.build_naming_info()



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       if len(_UpperCAmelCase      ) == 0:
             return ""
       snake_case_ =   None
       if any(char.isdigit() for char in word      ):
             raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number'''      )
       if word in info["short_word"]:
             return info["short_word"][word]
       for prefix_len in range(1	,   len(_UpperCAmelCase      ) + 1      ):
             snake_case_ =   word[:prefix_len]
             if prefix in info["reverse_short_word"]:
                   continue
             else:
                   snake_case_ =   prefix
                   break

       if short_word is None:
             # Paranoid fallback
             def int_to_alphabetic(_UpperCAmelCase      ):
                   snake_case_ =   ''''''
                   while integer != 0:
                         snake_case_ =   chr(ord('''A'''      ) + integer % 10      ) + s
                         integer //= 10
                   return s

             snake_case_ =   0
             while True:
                   snake_case_ =   word + '''#''' + int_to_alphabetic(_UpperCAmelCase      )
                   if sword in info["reverse_short_word"]:
                         continue
                   else:
                         snake_case_ =   sword
                         break

       snake_case_ =   short_word
       snake_case_ =   word
       return short_word



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   param_name.split('''_'''      )

       snake_case_ =   [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,   _UpperCAmelCase      ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       snake_case_ =   ['''''', '''_''']

       for separator in separators:
             snake_case_ =   separator.join(_UpperCAmelCase      )
             if shortname not in info["reverse_short_param"]:
                   snake_case_ =   shortname
                   snake_case_ =   param_name
                   return shortname

       return param_name



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   TrialShortNamer.shortname_for_key(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   short_name
       snake_case_ =   param_name



 @classmethod
 def 		UpperCamelCase__  (			cls      ):
       if cls.NAMING_INFO is not None:
             return

       snake_case_ =   {
           '''short_word''': {},
           '''reverse_short_word''': {},
           '''short_param''': {},
           '''reverse_short_param''': {},
       }

       snake_case_ =   list(cls.DEFAULTS.keys()      )

       for k in field_keys:
             cls.add_new_param_name(_UpperCAmelCase	,   _UpperCAmelCase      )

       snake_case_ =   info



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase      ):
       cls.build_naming_info()
       assert cls.PREFIX is not None
       snake_case_ =   [copy.copy(cls.PREFIX      )]

       for k, v in params.items():
             if k not in cls.DEFAULTS:
                   raise Exception(F'''You should provide a default value for the param name {k} with value {v}'''      )
             if v == cls.DEFAULTS[k]:
                   # The default value is not added to the name
                   continue

             snake_case_ =   cls.NAMING_INFO['''short_param'''][k]

             if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   snake_case_ =   1 if v else 0

             snake_case_ =   '''''' if isinstance(_UpperCAmelCase	,   (int, float)      ) else '''-'''
             snake_case_ =   F'''{key}{sep}{v}'''
             name.append(_UpperCAmelCase      )

       return "_".join(_UpperCAmelCase      )





 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase      ):
       snake_case_ =   repr[len(cls.PREFIX      ) + 1 :]
       if repr == "":
             snake_case_ =   []
       else:
             snake_case_ =   repr.split('''_'''      )

       snake_case_ =   {}

       for value in values:
             if "-" in value:
                   snake_case_ ,					snake_case_ =   value.split('''-'''      )
             else:
                   snake_case_ =   re.sub('''[0-9.]'''	,   ''''''	,   _UpperCAmelCase      )
                   snake_case_ =   float(re.sub('''[^0-9.]'''	,   ''''''	,   _UpperCAmelCase      )      )

             snake_case_ =   cls.NAMING_INFO['''reverse_short_param'''][p_k]

             snake_case_ =   p_v

       for k in cls.DEFAULTS:
             if k not in parameters:
                   snake_case_ =   cls.DEFAULTS[k]

       return parameters