





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__     :





      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 ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     parent
       UpperCamelCase			:      Any 			=     batch_size
       UpperCamelCase			:      List[str] 			=     seq_length
       UpperCamelCase			:      Union[str, Any] 			=     is_training
       UpperCamelCase			:      int 			=     use_input_mask
       UpperCamelCase			:      int 			=     use_token_type_ids
       UpperCamelCase			:      Any 			=     use_labels
       UpperCamelCase			:      Optional[Any] 			=     vocab_size
       UpperCamelCase			:      List[Any] 			=     hidden_size
       UpperCamelCase			:      Optional[int] 			=     num_hidden_layers
       UpperCamelCase			:      Optional[int] 			=     num_attention_heads
       UpperCamelCase			:      List[str] 			=     intermediate_size
       UpperCamelCase			:      List[str] 			=     hidden_act
       UpperCamelCase			:      str 			=     hidden_dropout_prob
       UpperCamelCase			:      Optional[int] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      int 			=     max_position_embeddings
       UpperCamelCase			:      Dict 			=     type_vocab_size
       UpperCamelCase			:      Optional[Any] 			=     type_sequence_label_size
       UpperCamelCase			:      List[str] 			=     initializer_range
       UpperCamelCase			:      Optional[int] 			=     num_labels
       UpperCamelCase			:      List[str] 			=     num_choices
       UpperCamelCase			:      List[Any] 			=     scope
       UpperCamelCase			:      Optional[Any] 			=     vocab_size - 1





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size     )

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

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

       UpperCamelCase			:      Tuple 			=     self.get_config()

       return config, input_ids, input_mask, token_labels





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[str] 			=     self.prepare_config_and_inputs()

       UpperCamelCase			:      Dict 			=     True

       return config, input_ids, input_mask, token_labels





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     GPTNeoXModel(config=A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Any 			=     model(A_ ,							attention_mask=A_     )
       UpperCamelCase			:      Dict 			=     model(A_     )
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     True
       UpperCamelCase			:      Optional[Any] 			=     GPTNeoXModel(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      List[Any] 			=     model(A_ ,							attention_mask=A_     )
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     GPTNeoXForCausalLM(config=A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      int 			=     model(A_ ,							attention_mask=A_ ,							labels=A_     )
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.num_labels
       UpperCamelCase			:      Union[str, Any] 			=     GPTNeoXForQuestionAnswering(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      int 			=     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  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.num_labels
       UpperCamelCase			:      List[str] 			=     GPTNeoXForSequenceClassification(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      str 			=     ids_tensor([self.batch_size] ,							self.type_sequence_label_size     )
       UpperCamelCase			:      List[str] 			=     model(A_ ,							attention_mask=A_ ,							labels=A_     )
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.num_labels
       UpperCamelCase			:      Optional[int] 			=     GPTNeoXForTokenClassification(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Union[str, Any] 			=     model(A_ ,							attention_mask=A_ ,							labels=A_     )
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     True
       UpperCamelCase			:      Tuple 			=     GPTNeoXForCausalLM(config=A_     )
       model.to(A_     )
       model.eval()

       # first forward pass
       UpperCamelCase			:      str 			=     model(A_ ,							attention_mask=A_ ,							use_cache=A_     )
       UpperCamelCase			:      List[Any] 			=     outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCamelCase			:      Dict 			=     ids_tensor((self.batch_size, 3) ,							config.vocab_size     )
       UpperCamelCase			:      List[str] 			=     ids_tensor((self.batch_size, 3) ,							vocab_size=2     )

       # append to next input_ids and
       UpperCamelCase			:      Union[str, Any] 			=     torch.cat([input_ids, next_tokens] ,							dim=-1     )
       UpperCamelCase			:      Dict 			=     torch.cat([input_mask, next_mask] ,							dim=-1     )

       UpperCamelCase			:      Optional[Any] 			=     model(A_ ,							attention_mask=A_ ,							output_hidden_states=A_     )
       UpperCamelCase			:      Any 			=     output_from_no_past["hidden_states"][0]
       UpperCamelCase			:      List[str] 			=     model(
           A_ ,							attention_mask=A_ ,							past_key_values=A_ ,							output_hidden_states=A_ ,							)["hidden_states"][0]

       # select random slice
       UpperCamelCase			:      Union[str, Any] 			=     ids_tensor((1,) ,							output_from_past.shape[-1]     ).item()
       UpperCamelCase			:      Tuple 			=     output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase			:      Optional[Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.prepare_config_and_inputs()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     config_and_inputs
       UpperCamelCase			:      List[str] 			=     {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_torch
class       A__     ( __snake_case ,     __snake_case ,     __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     (
          (
              GPTNeoXModel,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase			:str							=     (GPTNeoXForCausalLM,) if is_torch_available() else ()
      _UpperCAmelCase			:Optional[Any]							=     (
          {
              'feature-extraction': GPTNeoXModel,
              'question-answering': GPTNeoXForQuestionAnswering,
              'text-classification': GPTNeoXForSequenceClassification,
              'text-generation': GPTNeoXForCausalLM,
              'token-classification': GPTNeoXForTokenClassification,
              'zero-shot': GPTNeoXForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase			:Any							=     False
      _UpperCAmelCase			:List[str]							=     False
      _UpperCAmelCase			:Union[str, Any]							=     False
      _UpperCAmelCase			:Dict							=     False





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     GPTNeoXModelTester(self     )
       UpperCamelCase			:      Any 			=     ConfigTester(self ,							config_class=A_ ,							hidden_size=64 ,							num_attention_heads=8     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      str 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(A_ ,							A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(A_ ,							A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Dict 			=     self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase			:      Optional[int] 			=     None

       self.model_tester.create_and_check_model_as_decoder(A_ ,							A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      int 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(A_ ,							A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      @parameterized.expand([("linear",), ("dynamic",)]     )
      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase			:      Tuple 			=     ids_tensor([1, 10] ,							config.vocab_size     )
       UpperCamelCase			:      List[str] 			=     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			:      Dict 			=     GPTNeoXModel(A_     )
       original_model.to(A_     )
       original_model.eval()
       UpperCamelCase			:      List[Any] 			=     original_model(A_     ).last_hidden_state
       UpperCamelCase			:      Optional[Any] 			=     original_model(A_     ).last_hidden_state

       set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
       UpperCamelCase			:      Union[str, Any] 			=     {"type": scaling_type, "factor": 10.0}
       UpperCamelCase			:      List[Any] 			=     GPTNeoXModel(A_     )
       scaled_model.to(A_     )
       scaled_model.eval()
       UpperCamelCase			:      Dict 			=     scaled_model(A_     ).last_hidden_state
       UpperCamelCase			:      Any 			=     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    ):





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"     )
       for checkpointing in [True, False]:
        UpperCamelCase			:      str 			=     GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"     )

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

        UpperCamelCase			:      Optional[Any] 			=     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
        UpperCamelCase			:      Optional[int] 			=     "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

        UpperCamelCase			:      List[Any] 			=     model.generate(**A_ ,							do_sample=A_ ,							max_new_tokens=20     )
        UpperCamelCase			:      int 			=     tokenizer.batch_decode(A_     )[0]

        self.assertEqual(A_ ,							A_     )







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
