"""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      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=7		,			a=True		,			a=True		,			a=True		,			a=True		,			a=9_9		,			a=6_4		,			a=5		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=5_1_2		,			a=1_6		,			a=2		,			a=0.02		,			a=3		,			a=4		,			a=None		,			)       ->					Dict:
             lowercase__			:		List[str]       =	parent
             lowercase__			:		Any       =	batch_size
             lowercase__			:		Optional[int]       =	seq_length
             lowercase__			:		List[str]       =	is_training
             lowercase__			:		List[Any]       =	use_input_mask
             lowercase__			:		Dict       =	use_token_type_ids
             lowercase__			:		Optional[Any]       =	use_labels
             lowercase__			:		Any       =	vocab_size
             lowercase__			:		Optional[int]       =	hidden_size
             lowercase__			:		int       =	num_hidden_layers
             lowercase__			:		Dict       =	num_attention_heads
             lowercase__			:		List[Any]       =	intermediate_size
             lowercase__			:		List[Any]       =	hidden_act
             lowercase__			:		str       =	hidden_dropout_prob
             lowercase__			:		Any       =	attention_probs_dropout_prob
             lowercase__			:		Optional[Any]       =	max_position_embeddings
             lowercase__			:		Optional[Any]       =	type_vocab_size
             lowercase__			:		int       =	type_sequence_label_size
             lowercase__			:		Union[str, Any]       =	initializer_range
             lowercase__			:		List[str]       =	num_labels
             lowercase__			:		str       =	num_choices
             lowercase__			:		Optional[Any]       =	scope
             lowercase__			:		List[Any]       =	vocab_size - 1


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Optional[Any]       =	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size							)

             lowercase__			:		Dict       =	None
             if self.use_input_mask:
                    lowercase__			:		int       =	random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__			:		Optional[Any]       =	None
             if self.use_labels:
                    lowercase__			:		Tuple       =	ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels							)

             lowercase__			:		Dict       =	self.get_config()

             return config, input_ids, input_mask, token_labels


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             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 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		List[Any]       =	self.prepare_config_and_inputs()

             lowercase__			:		int       =	True

             return config, input_ids, input_mask, token_labels


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[int]:
             lowercase__			:		Union[str, Any]       =	GPTNeoXModel(config=lowerCamelCase__							)
             model.to(lowerCamelCase__							)
             model.eval()
             lowercase__			:		Optional[int]       =	model(lowerCamelCase__		,			attention_mask=lowerCamelCase__							)
             lowercase__			:		Optional[Any]       =	model(lowerCamelCase__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


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


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


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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a							)       ->					Any:
             lowercase__			:		List[str]       =	self.num_labels
             lowercase__			:		Optional[Any]       =	GPTNeoXForTokenClassification(lowerCamelCase__							)
             model.to(lowerCamelCase__							)
             model.eval()
             lowercase__			:		int       =	model(lowerCamelCase__		,			attention_mask=lowerCamelCase__		,			labels=lowerCamelCase__							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Dict:
             lowercase__			:		List[str]       =	True
             lowercase__			:		str       =	GPTNeoXForCausalLM(config=lowerCamelCase__							)
             model.to(lowerCamelCase__							)
             model.eval()

             # first forward pass
             lowercase__			:		Tuple       =	model(lowerCamelCase__		,			attention_mask=lowerCamelCase__		,			use_cache=lowerCamelCase__							)
             lowercase__			:		str       =	outputs.past_key_values

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

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

             lowercase__			:		Any       =	model(lowerCamelCase__		,			attention_mask=lowerCamelCase__		,			output_hidden_states=lowerCamelCase__							)
             lowercase__			:		int       =	output_from_no_past['hidden_states'][0]
             lowercase__			:		Dict       =	model(
                 lowerCamelCase__		,			attention_mask=lowerCamelCase__		,			past_key_values=lowerCamelCase__		,			output_hidden_states=lowerCamelCase__		,			)['hidden_states'][0]

             # select random slice
             lowercase__			:		Union[str, Any]       =	ids_tensor((1,)		,			output_from_past.shape[-1]							).item()
             lowercase__			:		Dict       =	output_from_no_past[:, -3:, random_slice_idx].detach()
             lowercase__			:		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 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		str       =	self.prepare_config_and_inputs()
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		str       =	config_and_inputs
             lowercase__			:		Any       =	{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_torch
class      UpperCAmelCase_	(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							unittest.TestCase):
      lowerCamelCase__		:    Tuple   =  (
          (
              GPTNeoXModel,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__		:    Optional[int]   =  (GPTNeoXForCausalLM,) if is_torch_available() else ()
      lowerCamelCase__		:    Tuple   =  (
          {
              "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[str]   =  False
      lowerCamelCase__		:    Optional[int]   =  False
      lowerCamelCase__		:    Dict   =  False
      lowerCamelCase__		:    Union[str, Any]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	GPTNeoXModelTester(self							)
             lowercase__			:		Optional[Any]       =	ConfigTester(self		,			config_class=lowerCamelCase__		,			hidden_size=6_4		,			num_attention_heads=8							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             self.config_tester.run_common_tests()


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(lowerCamelCase__		,			lowerCamelCase__		,			lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(lowerCamelCase__		,			lowerCamelCase__		,			lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_decoder()

             lowercase__			:		List[Any]       =	None

             self.model_tester.create_and_check_model_as_decoder(lowerCamelCase__		,			lowerCamelCase__		,			lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase__		,			lowerCamelCase__		,			lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		Any       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	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 		_UpperCAmelCase       (   self							)       ->					Any:
             pass


      @parameterized.expand([('linear',), ('dynamic',)]							)
      def 		_UpperCAmelCase       (   self		,			a							)       ->					int:
             lowercase__    ,      lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Tuple       =	ids_tensor([1, 1_0]		,			config.vocab_size							)
             lowercase__			:		Union[str, Any]       =	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
             lowercase__			:		List[str]       =	GPTNeoXModel(lowerCamelCase__							)
             original_model.to(lowerCamelCase__							)
             original_model.eval()
             lowercase__			:		Tuple       =	original_model(lowerCamelCase__							).last_hidden_state
             lowercase__			:		str       =	original_model(lowerCamelCase__							).last_hidden_state

             set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
             lowercase__			:		Any       =	{'type': scaling_type, 'factor': 10.0}
             lowercase__			:		Optional[Any]       =	GPTNeoXModel(lowerCamelCase__							)
             scaled_model.to(lowerCamelCase__							)
             scaled_model.eval()
             lowercase__			:		str       =	scaled_model(lowerCamelCase__							).last_hidden_state
             lowercase__			:		str       =	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):


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

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

                    lowercase__			:		str       =	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
                    lowercase__			:		Union[str, Any]       =	'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

                    lowercase__			:		Union[str, Any]       =	model.generate(**lowerCamelCase__		,			do_sample=lowerCamelCase__		,			max_new_tokens=2_0							)
                    lowercase__			:		List[Any]       =	tokenizer.batch_decode(lowerCamelCase__							)[0]

                    self.assertEqual(lowerCamelCase__		,			lowerCamelCase__							)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


