
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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=99	,      SCREAMING_SNAKE_CASE_=64	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=16	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=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
       UpperCamelCase__				=    vocab_size - 1







  def 	UpperCAmelCase_ (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_labels:
            UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels						)

       UpperCamelCase__				=    self.get_config()

       return config, input_ids, input_mask, token_labels







  def 	UpperCAmelCase_ (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=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      pad_token_id=self.pad_token_id	,      )







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.prepare_config_and_inputs()

       UpperCamelCase__				=    True

       return config, input_ids, input_mask, token_labels







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    GPTNeoXModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    True
       UpperCamelCase__				=    GPTNeoXModel(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    ids_tensor([self.batch_size]	,      self.type_sequence_label_size						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    True
       UpperCamelCase__				=    GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

       # first forward pass
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      use_cache=SCREAMING_SNAKE_CASE_						)
       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(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      output_hidden_states=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    output_from_no_past["""hidden_states"""][0]
       UpperCamelCase__				=    model(
           SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      past_key_values=SCREAMING_SNAKE_CASE_	,      output_hidden_states=SCREAMING_SNAKE_CASE_	,      )["""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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      atol=1E-3						)						)







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






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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GPTNeoXModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      hidden_size=64	,      num_attention_heads=8						)







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # This regression test was failing with PyTorch < 1.3
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase__				=    None

       self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_						)







  @unittest.skip(reason="""Feed forward chunking is not implemented"""						)
  def 	UpperCAmelCase_ (self						):
       pass







  @parameterized.expand([("""linear""",), ("""dynamic""",)]						)
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       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__				=    GPTNeoXModel(SCREAMING_SNAKE_CASE_						)
       original_model.to(SCREAMING_SNAKE_CASE_						)
       original_model.eval()
       UpperCamelCase__				=    original_model(SCREAMING_SNAKE_CASE_						).last_hidden_state
       UpperCamelCase__				=    original_model(SCREAMING_SNAKE_CASE_						).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__				=    GPTNeoXModel(SCREAMING_SNAKE_CASE_						)
       scaled_model.to(SCREAMING_SNAKE_CASE_						)
       scaled_model.eval()
       UpperCamelCase__				=    scaled_model(SCREAMING_SNAKE_CASE_						).last_hidden_state
       UpperCamelCase__				=    scaled_model(SCREAMING_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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      atol=1E-5						)						)
       else:
            self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      atol=1E-5						)						)

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






@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







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

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

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

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

            self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







from __future__ import annotations

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

       for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                 return dest
       return ""






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(__a	,		__a	,		__a						)

     UpperCamelCase__				=    Counter(graph.get_nodes()						)
     UpperCamelCase__				=    start

     for _ in range(__a						):
          UpperCamelCase__				=    graph.transition(__a						)
          visited[node] += 1

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





