


"""simple docstring"""







import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, 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 (
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
           GPTNeoXModel,
       )






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=13			,   __A=7			,   __A=True			,   __A=True			,   __A=True			,   __A=True			,   __A=99			,   __A=64			,   __A=5			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=16			,   __A=2			,   __A=0.02			,   __A=3			,   __A=4			,   __A=None			,   ) ->					Union[str, Any]:
     a					=parent
     a					=batch_size
     a					=seq_length
     a					=is_training
     a					=use_input_mask
     a					=use_token_type_ids
     a					=use_labels
     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=type_sequence_label_size
     a					=initializer_range
     a					=num_labels
     a					=num_choices
     a					=scope
     a					=vocab_size - 1


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

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

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

     a					=self.get_config()

     return config, input_ids, input_mask, token_labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     return GPTNeoXConfig(
         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			,   pad_token_id=self.pad_token_id			,   )


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a		,				a		,				a		,				a					=self.prepare_config_and_inputs()

     a					=True

     return config, input_ids, input_mask, token_labels


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Any:
     a					=GPTNeoXModel(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   attention_mask=__A						)
     a					=model(__A						)
     self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=True
     a					=GPTNeoXModel(__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   attention_mask=__A						)
     self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     a					=GPTNeoXForCausalLM(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   attention_mask=__A			,   labels=__A						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					Any:
     a					=self.num_labels
     a					=GPTNeoXForQuestionAnswering(__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   attention_mask=__A						)
     self.parent.assertEqual(result.start_logits.shape			,   (self.batch_size, self.seq_length)						)
     self.parent.assertEqual(result.end_logits.shape			,   (self.batch_size, self.seq_length)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					int:
     a					=self.num_labels
     a					=GPTNeoXForSequenceClassification(__A						)
     model.to(__A						)
     model.eval()
     a					=ids_tensor([self.batch_size]			,   self.type_sequence_label_size						)
     a					=model(__A			,   attention_mask=__A			,   labels=__A						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     a					=self.num_labels
     a					=GPTNeoXForTokenClassification(__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   attention_mask=__A			,   labels=__A						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length, self.num_labels)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=True
     a					=GPTNeoXForCausalLM(config=__A						)
     model.to(__A						)
     model.eval()

     # first forward pass
     a					=model(__A			,   attention_mask=__A			,   use_cache=__A						)
     a					=outputs.past_key_values

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

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

     a					=model(__A			,   attention_mask=__A			,   output_hidden_states=__A						)
     a					=output_from_no_past['''hidden_states'''][0]
     a					=model(
         __A			,   attention_mask=__A			,   past_key_values=__A			,   output_hidden_states=__A			,   )['''hidden_states'''][0]

     # select random slice
     a					=ids_tensor((1,)			,   output_from_past.shape[-1]						).item()
     a					=output_from_no_past[:, -3:, random_slice_idx].detach()
     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.prepare_config_and_inputs()
     a		,				a		,				a		,				a					=config_and_inputs
     a					={'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_torch
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  (
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase			 =  (GPTNeoXForCausalLM,) if is_torch_available() else ()
 __lowerCAmelCase			 =  (
     {
         "feature-extraction": GPTNeoXModel,
         "question-answering": GPTNeoXForQuestionAnswering,
         "text-classification": GPTNeoXForSequenceClassification,
         "text-generation": GPTNeoXForCausalLM,
         "token-classification": GPTNeoXForTokenClassification,
         "zero-shot": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=GPTNeoXModelTester(self						)
     a					=ConfigTester(self			,   config_class=__A			,   hidden_size=64			,   num_attention_heads=8						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self.config_tester.run_common_tests()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a		,				a		,				a		,				a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a		,				a		,				a		,				a					=self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     # This regression test was failing with PyTorch < 1.3
     a		,				a		,				a		,				a					=self.model_tester.prepare_config_and_inputs_for_decoder()

     a					=None

     self.model_tester.create_and_check_model_as_decoder(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a		,				a		,				a		,				a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__A						)


 @unittest.skip(reason='''Feed forward chunking is not implemented'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     pass





 @parameterized.expand([('''linear''',), ('''dynamic''',)]						)
 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Optional[Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()
     a					=ids_tensor([1, 10]			,   config.vocab_size						)
     a					=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
     a					=GPTNeoXModel(__A						)
     original_model.to(__A						)
     original_model.eval()
     a					=original_model(__A						).last_hidden_state
     a					=original_model(__A						).last_hidden_state

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






 """simple docstring"""




 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)
     for checkpointing in [True, False]:
         a					=GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)

         if checkpointing:
             model.gradient_checkpointing_enable()
         else:
             model.gradient_checkpointing_disable()
         model.to(__A						)

         a					=tokenizer('''My favorite food is'''			,   return_tensors='''pt'''						).to(__A						)
         # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
         # See: https://github.com/huggingface/transformers/pull/24193
         a					='''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

         a					=model.generate(**__A			,   do_sample=__A			,   max_new_tokens=20						)
         a					=tokenizer.batch_decode(__A						)[0]

         self.assertEqual(__A			,   __A						)



"""simple docstring"""







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  RoFormerTokenizer
 __lowerCAmelCase			 =  RoFormerTokenizerFast
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  True


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     super().setUp()


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[int]:
     return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					List[Any]:
     return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					='''永和服装饰品有限公司,今天天气非常好'''
     a					='''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.get_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)

     self.assertListEqual(__A			,   output_text.split()						)

     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.get_rust_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   output_text.split()						)
     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     pass