




import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     YosoForMaskedLM,
     YosoForMultipleChoice,
     YosoForQuestionAnswering,
     YosoForSequenceClassification,
     YosoForTokenClassification,
     YosoModel,
 )
 from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       Dict       ,				snake_case    :       Dict=13       ,				snake_case    :       Union[str, Any]=7       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       List[str]=True       ,				snake_case    :       Dict=True       ,				snake_case    :       str=True       ,				snake_case    :       Optional[Any]=99       ,				snake_case    :       Any=32       ,				snake_case    :       List[str]=5       ,				snake_case    :       Optional[int]=4       ,				snake_case    :       int=37       ,				snake_case    :       Optional[int]="gelu"       ,				snake_case    :       Optional[int]=0.1       ,				snake_case    :       Tuple=0.1       ,				snake_case    :       Any=512       ,				snake_case    :       List[str]=16       ,				snake_case    :       Any=2       ,				snake_case    :       Optional[Any]=0.02       ,				snake_case    :       int=3       ,				snake_case    :       Union[str, Any]=4       ,				snake_case    :       List[Any]=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







      def 					snake_case		(   self    :       Any ):
          __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_token_type_ids:
              __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size )

          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  None
          if self.use_labels:
              __UpperCamelCase     						=  ids_tensor([self.batch_size]       ,				self.type_sequence_label_size )
              __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels )
              __UpperCamelCase     						=  ids_tensor([self.batch_size]       ,				self.num_choices )

          __UpperCamelCase     						=  self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







      def 					snake_case		(   self    :       int ):
          return YosoConfig(
              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       ,				)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.get_config()
          __UpperCamelCase     						=  300
          return config







      def 					snake_case		(   self    :       Optional[Any] ):
          (
              (
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					
          )     						=  self.prepare_config_and_inputs()

          __UpperCamelCase     						=  True
          __UpperCamelCase     						=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				vocab_size=2 )

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )







      def 					snake_case		(   self    :       str       ,				snake_case    :       str       ,				snake_case    :       str       ,				snake_case    :       str       ,				snake_case    :       int       ,				snake_case    :       Tuple       ,				snake_case    :       int       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  YosoModel(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case )
          __UpperCamelCase     						=  model(snake_case       ,				token_type_ids=snake_case )
          __UpperCamelCase     						=  model(snake_case )
          self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[Any]       ,				snake_case    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Optional[int]       ,				):
          __UpperCamelCase     						=  True
          __UpperCamelCase     						=  YosoModel(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				encoder_hidden_states=snake_case       ,				encoder_attention_mask=snake_case       ,				)
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				encoder_hidden_states=snake_case       ,				)
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case )
          self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )







      def 					snake_case		(   self    :       Any       ,				snake_case    :       str       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       str       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Dict       ,				snake_case    :       str ):
          __UpperCamelCase     						=  YosoForMaskedLM(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size) )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Any       ,				snake_case    :       Tuple       ,				snake_case    :       Dict       ,				snake_case    :       List[str]       ,				snake_case    :       Dict       ,				snake_case    :       List[str]       ,				snake_case    :       Dict ):
          __UpperCamelCase     						=  YosoForQuestionAnswering(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				start_positions=snake_case       ,				end_positions=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 					snake_case		(   self    :       int       ,				snake_case    :       Optional[Any]       ,				snake_case    :       List[Any]       ,				snake_case    :       str       ,				snake_case    :       Any       ,				snake_case    :       str       ,				snake_case    :       Optional[int]       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  self.num_labels
          __UpperCamelCase     						=  YosoForSequenceClassification(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels) )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       str       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       List[Any] ):
          __UpperCamelCase     						=  self.num_labels
          __UpperCamelCase     						=  YosoForTokenClassification(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels) )







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[int]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       int       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  self.num_choices
          __UpperCamelCase     						=  YosoForMultipleChoice(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  input_ids.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  token_type_ids.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  input_mask.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case       ,				)
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices) )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					
          )     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_torch
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    (
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__	:       Union[str, Any]			=    False
      lowerCAmelCase__	:       Optional[Any]			=    False
      lowerCAmelCase__	:       List[Any]			=    False

      lowerCAmelCase__	:       str			=    ()
      lowerCAmelCase__	:       str			=    (
          {
              "feature-extraction": YosoModel,
              "fill-mask": YosoForMaskedLM,
              "question-answering": YosoForQuestionAnswering,
              "text-classification": YosoForSequenceClassification,
              "token-classification": YosoForTokenClassification,
              "zero-shot": YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  YosoModelTester(self )
          __UpperCamelCase     						=  ConfigTester(self       ,				config_class=snake_case       ,				hidden_size=37 )







      def 					snake_case		(   self    :       int ):
          self.config_tester.run_common_tests()







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase     						=  type
              self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case )







      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case )







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case )







      @slow
      def 					snake_case		(   self    :       Dict ):
          for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase     						=  YosoModel.from_pretrained(snake_case )
              self.assertIsNotNone(snake_case )







      def 					snake_case		(   self    :       Tuple ):
          return







@require_torch
class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      @slow
      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  YosoModel.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  torch.Size((1, 6, 768) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-0.06_11, 0.12_42, 0.08_40], [0.02_80, -0.00_48, 0.11_25], [0.01_06, 0.02_26, 0.07_51]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )







      @slow
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  50265

          __UpperCamelCase     						=  torch.Size((1, 6, vocab_size) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-2.13_13, -3.72_85, -2.24_07], [-2.70_47, -3.33_14, -2.64_08], [0.06_29, -2.51_66, -0.33_56]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )







      @slow
      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.arange(4096 ).unsqueeze(0 )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  50265

          __UpperCamelCase     						=  torch.Size((1, 4096, vocab_size) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-2.39_14, -4.37_42, -5.09_56], [-4.09_88, -4.23_84, -7.04_06], [-3.14_27, -3.71_92, -6.68_00]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )












import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __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     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, Any] ):
          __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     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=self.max_position_embeddings       ,				use_cache=snake_case       ,				bos_token_id=self.bos_token_id       ,				eos_token_id=self.eos_token_id       ,				pad_token_id=self.pad_token_id       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.ones((input_ids.shape[0], max_decoder_length)       ,				dtype='''i4''' )

          __UpperCamelCase     						=  jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]       ,				(input_ids.shape[0], input_ids.shape[-1] - 1) )
          __UpperCamelCase     						=  model(
              input_ids[:, :-1]       ,				attention_mask=snake_case       ,				past_key_values=snake_case       ,				position_ids=snake_case       ,				)

          __UpperCamelCase     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,				dtype='''i4''' )
          __UpperCamelCase     						=  model(
              input_ids[:, -1:]       ,				attention_mask=snake_case       ,				past_key_values=outputs_cache.past_key_values       ,				position_ids=snake_case       ,				)

          __UpperCamelCase     						=  model(snake_case )

          __UpperCamelCase     						=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3       ,				msg=F"Max diff is {diff}" )







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

          __UpperCamelCase     						=  jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]       ,				axis=-1       ,				)

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]       ,				(input_ids.shape[0], input_ids.shape[-1] - 1) )

          __UpperCamelCase     						=  model(
              input_ids[:, :-1]       ,				attention_mask=snake_case       ,				past_key_values=snake_case       ,				position_ids=snake_case       ,				)
          __UpperCamelCase     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,				dtype='''i4''' )
          __UpperCamelCase     						=  model(
              input_ids[:, -1:]       ,				past_key_values=outputs_cache.past_key_values       ,				attention_mask=snake_case       ,				position_ids=snake_case       ,				)

          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case )

          __UpperCamelCase     						=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3       ,				msg=F"Max diff is {diff}" )







@require_flax
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      @slow
      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  # prepare inputs
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  __UpperCamelCase     						=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  __UpperCamelCase     						=  getattr(snake_case       ,				snake_case )

                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,				snake_case )
                  __UpperCamelCase     						=  fx_state

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

                  __UpperCamelCase     						=  fx_model(**snake_case ).to_tuple()
                  self.assertEqual(len(snake_case )       ,				len(snake_case )       ,				'''Output lengths differ between Flax and PyTorch''' )
                  for fx_output, pt_output in zip(snake_case       ,				snake_case ):
                      self.assert_almost_equals(fx_output[:, -1]       ,				pt_output[:, -1].numpy()       ,				4E-2 )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(snake_case )
                      __UpperCamelCase     						=  model_class.from_pretrained(snake_case       ,				from_pt=snake_case )

                  __UpperCamelCase     						=  fx_model_loaded(**snake_case ).to_tuple()
                  self.assertEqual(
                      len(snake_case )       ,				len(snake_case )       ,				'''Output lengths differ between Flax and PyTorch''' )
                  for fx_output_loaded, pt_output in zip(snake_case       ,				snake_case ):
                      self.assert_almost_equals(fx_output_loaded[:, -1]       ,				pt_output[:, -1].numpy()       ,				4E-2 )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  # prepare inputs
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  __UpperCamelCase     						=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  __UpperCamelCase     						=  getattr(snake_case       ,				snake_case )

                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  load_flax_weights_in_pytorch_model(snake_case       ,				fx_model.params )
                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

                  __UpperCamelCase     						=  fx_model(**snake_case ).to_tuple()
                  self.assertEqual(len(snake_case )       ,				len(snake_case )       ,				'''Output lengths differ between Flax and PyTorch''' )
                  for fx_output, pt_output in zip(snake_case       ,				snake_case ):
                      self.assert_almost_equals(fx_output[:, -1]       ,				pt_output[:, -1].numpy()       ,				4E-2 )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(snake_case )
                      __UpperCamelCase     						=  pt_model_class.from_pretrained(snake_case       ,				from_flax=snake_case )

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model_loaded(**snake_case ).to_tuple()

                  self.assertEqual(
                      len(snake_case )       ,				len(snake_case )       ,				'''Output lengths differ between Flax and PyTorch''' )
                  for fx_output, pt_output in zip(snake_case       ,				snake_case ):
                      self.assert_almost_equals(fx_output[:, -1]       ,				pt_output[:, -1].numpy()       ,				4E-2 )







      @slow
      def 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )






