




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 						UpperCamelCase__      :

    '''simple docstring'''



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







    def        _lowercase	(       self	)	-> str:
           lowerCamelCase  : List[str]     					=   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

           lowerCamelCase  : List[str]     					=   None
           if self.use_input_mask:
                  lowerCamelCase  : Tuple     					=   random_attention_mask([self.batch_size, self.seq_length]	)

           lowerCamelCase  : Any     					=   None
           if self.use_labels:
                  lowerCamelCase  : List[str]     					=   ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels	)

           lowerCamelCase  : int     					=   self.get_config()

           return config, input_ids, input_mask, token_labels







    def        _lowercase	(       self	)	-> str:
           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=__lowerCAmelCase	,    initializer_range=self.initializer_range	,    pad_token_id=self.pad_token_id	,    )







    def        _lowercase	(       self	)	-> Optional[Any]:
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : Tuple     					=   self.prepare_config_and_inputs()

           lowerCamelCase  : Any     					=   True

           return config, input_ids, input_mask, token_labels







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Tuple:
           lowerCamelCase  : Tuple     					=   GPTNeoXModel(config=__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : Optional[int]     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	)
           lowerCamelCase  : List[Any]     					=   model(__lowerCAmelCase	)
           self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> List[str]:
           lowerCamelCase  : List[str]     					=   True
           lowerCamelCase  : Tuple     					=   GPTNeoXModel(__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : str     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	)
           self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> List[Any]:
           lowerCamelCase  : str     					=   GPTNeoXForCausalLM(config=__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : Optional[int]     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    labels=__lowerCAmelCase	)
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> List[Any]:
           lowerCamelCase  : Tuple     					=   self.num_labels
           lowerCamelCase  : Optional[Any]     					=   GPTNeoXForQuestionAnswering(__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : Dict     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	)
           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        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Optional[Any]:
           lowerCamelCase  : List[str]     					=   self.num_labels
           lowerCamelCase  : str     					=   GPTNeoXForSequenceClassification(__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : int     					=   ids_tensor([self.batch_size]	,    self.type_sequence_label_size	)
           lowerCamelCase  : str     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    labels=__lowerCAmelCase	)
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> List[str]:
           lowerCamelCase  : str     					=   self.num_labels
           lowerCamelCase  : List[Any]     					=   GPTNeoXForTokenClassification(__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()
           lowerCamelCase  : Any     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    labels=__lowerCAmelCase	)
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Dict:
           lowerCamelCase  : Tuple     					=   True
           lowerCamelCase  : Tuple     					=   GPTNeoXForCausalLM(config=__lowerCAmelCase	)
           model.to(__lowerCAmelCase	)
           model.eval()

           # first forward pass
           lowerCamelCase  : List[Any]     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    use_cache=__lowerCAmelCase	)
           lowerCamelCase  : Dict     					=   outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           lowerCamelCase  : Optional[int]     					=   ids_tensor((self.batch_size, 3)	,    config.vocab_size	)
           lowerCamelCase  : List[Any]     					=   ids_tensor((self.batch_size, 3)	,    vocab_size=2	)

           # append to next input_ids and
           lowerCamelCase  : Dict     					=   torch.cat([input_ids, next_tokens]	,    dim=-1	)
           lowerCamelCase  : Optional[int]     					=   torch.cat([input_mask, next_mask]	,    dim=-1	)

           lowerCamelCase  : int     					=   model(__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    output_hidden_states=__lowerCAmelCase	)
           lowerCamelCase  : Dict     					=   output_from_no_past["hidden_states"][0]
           lowerCamelCase  : Tuple     					=   model(
               __lowerCAmelCase	,    attention_mask=__lowerCAmelCase	,    past_key_values=__lowerCAmelCase	,    output_hidden_states=__lowerCAmelCase	,    )["hidden_states"][0]

           # select random slice
           lowerCamelCase  : Tuple     					=   ids_tensor((1,)	,    output_from_past.shape[-1]	).item()
           lowerCamelCase  : int     					=   output_from_no_past[:, -3:, random_slice_idx].detach()
           lowerCamelCase  : List[str]     					=   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(__lowerCAmelCase	,    __lowerCAmelCase	,    atol=1e-3	)	)







    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : Tuple     					=   self.prepare_config_and_inputs()
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : Any     					=   config_and_inputs
           lowerCamelCase  : Union[str, Any]     					=   {"input_ids": input_ids, "attention_mask": input_mask}
           return config, inputs_dict




@require_torch
class 						UpperCamelCase__      (_a       ,							_a       ,							_a       ,							unittest.TestCase	):

    '''simple docstring'''



    lowerCamelCase_ :      Union[str, Any]      	= (
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase_ :      int      	= (GPTNeoXForCausalLM,) if is_torch_available() else ()
    lowerCamelCase_ :      Any      	= (
        {
            """feature-extraction""": GPTNeoXModel,
            """question-answering""": GPTNeoXForQuestionAnswering,
            """text-classification""": GPTNeoXForSequenceClassification,
            """text-generation""": GPTNeoXForCausalLM,
            """token-classification""": GPTNeoXForTokenClassification,
            """zero-shot""": GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase_ :      List[Any]      	= False
    lowerCamelCase_ :      str      	= False
    lowerCamelCase_ :      Any      	= False
    lowerCamelCase_ :      Union[str, Any]      	= False







    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : List[Any]     					=   GPTNeoXModelTester(self	)
           lowerCamelCase  : Optional[int]     					=   ConfigTester(self	,    config_class=__lowerCAmelCase	,    hidden_size=64	,    num_attention_heads=8	)







    def        _lowercase	(       self	)	-> int:
           self.config_tester.run_common_tests()







    def        _lowercase	(       self	)	-> Any:
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : List[Any]     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(__lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase	)







    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : Optional[Any]     					=   self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase	)







    def        _lowercase	(       self	)	-> Any:
           # This regression test was failing with PyTorch < 1.3
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : List[str]     					=   self.model_tester.prepare_config_and_inputs_for_decoder()

           lowerCamelCase  : Optional[Any]     					=   None

           self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase	)







    def        _lowercase	(       self	)	-> List[str]:
           lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : List[str]     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase	)







    def        _lowercase	(       self	)	-> Any:
           lowerCamelCase  : Dict     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase	)







    def        _lowercase	(       self	)	-> int:
           lowerCamelCase  : Any     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase	)







    def        _lowercase	(       self	)	-> List[str]:
           lowerCamelCase  : Optional[int]     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase	)







    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : Optional[Any]     					=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase	)







    @unittest.skip(reason="Feed forward chunking is not implemented"	)
    def        _lowercase	(       self	)	-> int:
           pass







    @parameterized.expand([("linear",), ("dynamic",)]	)
    def        _lowercase	(       self	,    UpperCamelCase__	)	-> Union[str, Any]:
           lowerCamelCase					, lowerCamelCase  : Optional[int]     					=   self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase  : str     					=   ids_tensor([1, 10]	,    config.vocab_size	)
           lowerCamelCase  : Union[str, Any]     					=   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
           lowerCamelCase  : Optional[Any]     					=   GPTNeoXModel(__lowerCAmelCase	)
           original_model.to(__lowerCAmelCase	)
           original_model.eval()
           lowerCamelCase  : List[str]     					=   original_model(__lowerCAmelCase	).last_hidden_state
           lowerCamelCase  : Any     					=   original_model(__lowerCAmelCase	).last_hidden_state

           set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
           lowerCamelCase  : Dict     					=   {"type": scaling_type, "factor": 10.0}
           lowerCamelCase  : Union[str, Any]     					=   GPTNeoXModel(__lowerCAmelCase	)
           scaled_model.to(__lowerCAmelCase	)
           scaled_model.eval()
           lowerCamelCase  : List[Any]     					=   scaled_model(__lowerCAmelCase	).last_hidden_state
           lowerCamelCase  : Optional[Any]     					=   scaled_model(__lowerCAmelCase	).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(__lowerCAmelCase	,    __lowerCAmelCase	,    atol=1e-5	)	)
           else:
                  self.assertFalse(torch.allclose(__lowerCAmelCase	,    __lowerCAmelCase	,    atol=1e-5	)	)

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




@require_torch
class 						UpperCamelCase__      (unittest.TestCase	):

    '''simple docstring'''



    @slow
    def        _lowercase	(       self	)	-> str:
           lowerCamelCase  : Optional[Any]     					=   AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"	)
           for checkpointing in [True, False]:
                  lowerCamelCase  : Tuple     					=   GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"	)

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

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

                  lowerCamelCase  : Optional[int]     					=   model.generate(**__lowerCAmelCase	,    do_sample=__lowerCAmelCase	,    max_new_tokens=20	)
                  lowerCamelCase  : Dict     					=   tokenizer.batch_decode(__lowerCAmelCase	)[0]

                  self.assertEqual(__lowerCAmelCase	,    __lowerCAmelCase	)













import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase__           =			"\nimport os\n"

UpperCamelCase__           =			"\ndef foo():\n    import os\n    return False\n"

UpperCamelCase__           =			"\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n"

UpperCamelCase__           =			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]





@pytest.mark.parametrize("""case""" ,      a__							)
def      _UpperCamelCase  (a__ :Any ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      os.path.join(a__ ,      """test_file.py"""							)
							with open(a__ ,      """w"""							) as _tmp_file:
														_tmp_file.write(a__							)

							UpperCamelCase__					  =      get_imports(a__							)
							assert parsed_imports == ["os"]




