


from __future__ import annotations

import os
from typing import Any

import requests

_SCREAMING_SNAKE_CASE      :			List[str]      				=   '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_SCREAMING_SNAKE_CASE      :			Tuple      				=   BASE_URL + '''/user'''

# https://github.com/settings/tokens
_SCREAMING_SNAKE_CASE      :			Tuple      				=   os.environ.get("USER_TOKEN", "")
def       UpperCAmelCase__						(UpperCamelCase_    ):



					"""simple docstring"""






					snake_case					=    {
					    "Authorization": F'''token {auth_token}''',
					    "Accept": "application/vnd.github.v3+json",
					}
					return requests.get(_lowerCAmelCase							,headers=_lowerCAmelCase    ).json()


if __name__ == "__main__":  # pragma: no cover
	if USER_TOKEN:
		for key, value in fetch_github_info(USER_TOKEN).items():
			print(f'''{key}: {value}''')
	else:
		raise ValueError("\'USER_TOKEN\' field cannot be empty.")








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      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=6_4      , __snake_case=5      , __snake_case=4      , __snake_case=3_7      , __snake_case="gelu"      , __snake_case=0.1      , __snake_case=0.1      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=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
         snake_case					=    vocab_size - 1
    def 		a_      (	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
         if self.use_labels:
              snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.num_labels   )

         snake_case					=    self.get_config()

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self   ):
         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=__snake_case      , initializer_range=self.initializer_range      , pad_token_id=self.pad_token_id      , )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()

         snake_case					=    True

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXModel(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    self.num_labels
         snake_case					=    GPTNeoXForQuestionAnswering(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    self.num_labels
         snake_case					=    GPTNeoXForSequenceClassification(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    ids_tensor([self.batch_size]      , self.type_sequence_label_size   )
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    self.num_labels
         snake_case					=    GPTNeoXForTokenClassification(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         # first forward pass
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , use_cache=__snake_case   )
         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(__snake_case      , attention_mask=__snake_case      , output_hidden_states=__snake_case   )
         snake_case					=    output_from_no_past['''hidden_states'''][0]
         snake_case					=    model(
             __snake_case      , attention_mask=__snake_case      , past_key_values=__snake_case      , output_hidden_states=__snake_case      , )['''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(__snake_case      , __snake_case      , atol=1E-3   )   )




    def 		a_      (	self   ):
         snake_case					=    self.prepare_config_and_inputs()
         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   A__ (   snake_case__	, snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __magic_name__        =		(GPTNeoXForCausalLM,) if is_torch_available() else ()
    __magic_name__        =		(
        {
            'feature-extraction': GPTNeoXModel,
            'question-answering': GPTNeoXForQuestionAnswering,
            'text-classification': GPTNeoXForSequenceClassification,
            'text-generation': GPTNeoXForCausalLM,
            'token-classification': GPTNeoXForTokenClassification,
            'zero-shot': GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    GPTNeoXModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , hidden_size=6_4      , num_attention_heads=8   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         # This regression test was failing with PyTorch < 1.3
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()

         snake_case					=    None

         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__snake_case   )
    @unittest.skip(reason='''Feed forward chunking is not implemented'''   )
    def 		a_      (	self   ):
         pass




    @parameterized.expand([('''linear''',), ('''dynamic''',)]   )
    def 		a_      (	self      , __snake_case   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         snake_case					=    ids_tensor([1, 1_0]      , config.vocab_size   )
         snake_case					=    ids_tensor([1, int(config.max_position_embeddings * 1.5   )]      , config.vocab_size   )

         set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
         snake_case					=    GPTNeoXModel(__snake_case   )
         original_model.to(__snake_case   )
         original_model.eval()
         snake_case					=    original_model(__snake_case   ).last_hidden_state
         snake_case					=    original_model(__snake_case   ).last_hidden_state

         set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
         snake_case					=    {'''type''': scaling_type, '''factor''': 10.0}
         snake_case					=    GPTNeoXModel(__snake_case   )
         scaled_model.to(__snake_case   )
         scaled_model.eval()
         snake_case					=    scaled_model(__snake_case   ).last_hidden_state
         snake_case					=    scaled_model(__snake_case   ).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(__snake_case      , __snake_case      , atol=1E-5   )   )
         else:
              self.assertFalse(torch.allclose(__snake_case      , __snake_case      , atol=1E-5   )   )

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






@require_torch
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

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

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

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

              snake_case					=    model.generate(**__snake_case      , do_sample=__snake_case      , max_new_tokens=2_0   )
              snake_case					=    tokenizer.batch_decode(__snake_case   )[0]

              self.assertEqual(__snake_case      , __snake_case   )




