






"""simple docstring"""






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Dict		,			lowercase_  :						str		,			lowercase_  :						Dict=13		,			lowercase_  :						Any=[30, 30]		,			lowercase_  :						List[Any]=2		,			lowercase_  :						List[Any]=3		,			lowercase_  :						int=True		,			lowercase_  :						Tuple=True		,			lowercase_  :						str=32		,			lowercase_  :						Dict=5		,			lowercase_  :						Dict=4		,			lowercase_  :						Optional[Any]=37		,			lowercase_  :						List[str]="gelu"		,			lowercase_  :						Optional[Any]=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						List[str]=10		,			lowercase_  :						Union[str, Any]=0.02		,			lowercase_  :						Optional[Any]=3		,			lowercase_  :						Tuple=None		,			lowercase_  :						Union[str, Any]=8		,			lowercase_  :						Any=10		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	parent
          SCREAMING_SNAKE_CASE_   :						int							=	batch_size
          SCREAMING_SNAKE_CASE_   :						int							=	image_size
          SCREAMING_SNAKE_CASE_   :						Dict							=	patch_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	num_channels
          SCREAMING_SNAKE_CASE_   :						Any							=	is_training
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	use_labels
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Dict							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						str							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						List[str]							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						List[str]							=	type_sequence_label_size
          SCREAMING_SNAKE_CASE_   :						str							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[str]							=	num_labels
          SCREAMING_SNAKE_CASE_   :						str							=	scope
          SCREAMING_SNAKE_CASE_   :						List[str]							=	n_targets
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          SCREAMING_SNAKE_CASE_   :						Tuple							=	(image_size[1] // patch_size) * (image_size[0] // patch_size)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	num_patches + 1 + self.num_detection_tokens
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

          SCREAMING_SNAKE_CASE_   :						List[str]							=	None
          if self.use_labels:
              # labels is a list of Dict (each Dict being the labels for a given example in the batch)
              SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
              for i in range(self.batch_size):
                  SCREAMING_SNAKE_CASE_   :						Dict							=	{}
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	torch.randint(
                      high=self.num_labels		,			size=(self.n_targets,)		,			device=lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.rand(self.n_targets		,			4		,			device=lowercase_)
                  labels.append(lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.get_config()

          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          return YolosConfig(
              image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			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		,			is_decoder=lowercase_		,			initializer_range=self.initializer_range		,			num_detection_tokens=self.num_detection_tokens		,			num_labels=self.num_labels		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						List[Any]		,			lowercase_  :						Optional[int]		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	YolosModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,			(self.batch_size, self.expected_seq_len, self.hidden_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict		,			lowercase_  :						List[Any]		,			lowercase_  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	YolosForObjectDetection(lowercase_)
          model.to(lowercase_)
          model.eval()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(pixel_values=lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_)

          self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,			(self.batch_size, self.num_detection_tokens, 4))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	model(pixel_values=lowercase_		,			labels=lowercase_)

          self.parent.assertEqual(result.loss.shape		,			())
          self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,			(self.batch_size, self.num_detection_tokens, 4))



      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						int							=	{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      __UpperCamelCase							=						(
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Dict		,			lowercase_  :						int		,			lowercase_  :						int=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	super()._prepare_for_class(lowercase_		,			lowercase_		,			return_labels=lowercase_)

          if return_labels:
              if model_class.__name__ == "YolosForObjectDetection":
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[]
                  for i in range(self.model_tester.batch_size):
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
                      SCREAMING_SNAKE_CASE_   :						str							=	torch.ones(
                          size=(self.model_tester.n_targets,)		,			device=lowercase_		,			dtype=torch.long)
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.ones(
                          self.model_tester.n_targets		,			4		,			device=lowercase_		,			dtype=torch.float)
                      labels.append(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	labels

          return inputs_dict
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	YolosModelTester(self)
          SCREAMING_SNAKE_CASE_   :						Any							=	ConfigTester(self		,			config_class=lowercase_		,			has_text_modality=lowercase_		,			hidden_size=37)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          self.config_tester.run_common_tests()
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class(lowercase_)
              self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module))
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase_		,			nn.Linear))
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Any							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	model_class(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_   :						str							=	[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''pixel_values''']
              self.assertListEqual(arg_names[:1]		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Any							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	True

          # in YOLOS, the seq_len is different
          SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	True
              SCREAMING_SNAKE_CASE_   :						Tuple							=	False
              SCREAMING_SNAKE_CASE_   :						int							=	True
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model_class(lowercase_)
              model.to(lowercase_)
              model.eval()
              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model(**self._prepare_for_class(lowercase_		,			lowercase_))
              SCREAMING_SNAKE_CASE_   :						List[str]							=	outputs.attentions
              self.assertEqual(len(lowercase_)		,			self.model_tester.num_hidden_layers)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              SCREAMING_SNAKE_CASE_   :						Dict							=	True
              SCREAMING_SNAKE_CASE_   :						Tuple							=	model_class(lowercase_)
              model.to(lowercase_)
              model.eval()
              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_   :						Any							=	model(**self._prepare_for_class(lowercase_		,			lowercase_))
              SCREAMING_SNAKE_CASE_   :						List[str]							=	outputs.attentions
              self.assertEqual(len(lowercase_)		,			self.model_tester.num_hidden_layers)

              self.assertListEqual(
                  list(attentions[0].shape[-3:])		,			[self.model_tester.num_attention_heads, seq_len, seq_len]		,			)
              SCREAMING_SNAKE_CASE_   :						List[str]							=	len(lowercase_)

              # Check attention is always last and order is fine
              SCREAMING_SNAKE_CASE_   :						int							=	True
              SCREAMING_SNAKE_CASE_   :						Dict							=	True
              SCREAMING_SNAKE_CASE_   :						Tuple							=	model_class(lowercase_)
              model.to(lowercase_)
              model.eval()
              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	model(**self._prepare_for_class(lowercase_		,			lowercase_))

              SCREAMING_SNAKE_CASE_   :						Any							=	1
              self.assertEqual(out_len + added_hidden_states		,			len(lowercase_))

              SCREAMING_SNAKE_CASE_   :						Tuple							=	outputs.attentions

              self.assertEqual(len(lowercase_)		,			self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:])		,			[self.model_tester.num_attention_heads, seq_len, seq_len]		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_  :						int		,			lowercase_  :						str		,			lowercase_  :						Dict):
              SCREAMING_SNAKE_CASE_   :						Dict							=	model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	model(**self._prepare_for_class(lowercase_		,			lowercase_))

              SCREAMING_SNAKE_CASE_   :						Any							=	outputs.hidden_states

              SCREAMING_SNAKE_CASE_   :						str							=	getattr(
                  self.model_tester		,			'''expected_num_hidden_layers'''		,			self.model_tester.num_hidden_layers + 1)
              self.assertEqual(len(lowercase_)		,			lowercase_)

              # YOLOS has a different seq_length
              SCREAMING_SNAKE_CASE_   :						str							=	self.model_tester.expected_seq_len

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])		,			[seq_length, self.model_tester.hidden_size]		,			)

          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	True
              check_hidden_states_output(lowercase_		,			lowercase_		,			lowercase_)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	True

              check_hidden_states_output(lowercase_		,			lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	YolosModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def       _A    ()				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image





@require_torch
@require_vision
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''') if is_vision_available() else None



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''').to(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.default_image_processor
          SCREAMING_SNAKE_CASE_   :						List[str]							=	prepare_img()
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	image_processor(images=lowercase_		,			return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	model(inputs.pixel_values)

          # verify outputs
          SCREAMING_SNAKE_CASE_   :						int							=	torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape		,			lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.tensor(
              [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]		,			device=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Any							=	torch.tensor(
              [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]		,			device=lowercase_)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,			lowercase_		,			atol=1e-4))
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,			lowercase_		,			atol=1e-4))

          # verify postprocessing
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	image_processor.post_process_object_detection(
              lowercase_		,			threshold=0.3		,			target_sizes=[image.size[::-1]])[0]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	[75, 75, 17, 63, 17]
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]).to(lowercase_)

          self.assertEqual(len(results['''scores'''])		,			5)
          self.assertTrue(torch.allclose(results['''scores''']		,			lowercase_		,			atol=1e-4))
          self.assertSequenceEqual(results['''labels'''].tolist()		,			lowercase_)
          self.assertTrue(torch.allclose(results['''boxes'''][0, :]		,			lowercase_))










"""simple docstring"""






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						List[Any]		,			lowercase_  :						int=14		,			lowercase_  :						Tuple=7		,			lowercase_  :						int=True		,			lowercase_  :						int=True		,			lowercase_  :						int=False		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=99		,			lowercase_  :						Optional[int]=32		,			lowercase_  :						List[Any]=4		,			lowercase_  :						Optional[int]=4		,			lowercase_  :						List[str]=4		,			lowercase_  :						Tuple=37		,			lowercase_  :						Any="gelu"		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=0.1		,			lowercase_  :						Optional[int]=512		,			lowercase_  :						Dict=0.02		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						Any							=	batch_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	seq_length
          SCREAMING_SNAKE_CASE_   :						Any							=	is_training
          SCREAMING_SNAKE_CASE_   :						int							=	use_input_mask
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_token_type_ids
          SCREAMING_SNAKE_CASE_   :						Tuple							=	use_labels
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	rotary_dim
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						int							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						str							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	GPTJConfig(
              vocab_size=self.vocab_size		,			n_embd=self.hidden_size		,			n_layer=self.num_hidden_layers		,			n_head=self.num_attention_heads		,			n_positions=self.max_position_embeddings		,			use_cache=lowercase_		,			bos_token_id=self.bos_token_id		,			eos_token_id=self.eos_token_id		,			pad_token_id=self.pad_token_id		,			rotary_dim=self.rotary_dim		,			)

          return (config, input_ids, input_mask)
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						int							=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	20
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.ones((input_ids.shape[0], max_decoder_length)		,			dtype='''i4''')

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

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

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	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 _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	20
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]		,			axis=-1		,			)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1)[None, :]		,			(input_ids.shape[0], input_ids.shape[-1] - 1))

          SCREAMING_SNAKE_CASE_   :						Dict							=	model(
              input_ids[:, :-1]		,			attention_mask=lowercase_		,			past_key_values=lowercase_		,			position_ids=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='''i4''')
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(
              input_ids[:, -1:]		,			past_key_values=outputs_cache.past_key_values		,			attention_mask=lowercase_		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	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__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __UpperCamelCase							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJModelTester(self)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	GPTaTokenizer.from_pretrained('''gpt2'''		,			pad_token='''<|endoftext|>'''		,			padding_side='''left''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''Hello this is a long string''', '''Hey''']		,			return_tensors='''np'''		,			padding=lowercase_		,			truncation=lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	False
          SCREAMING_SNAKE_CASE_   :						str							=	model.config.eos_token_id

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jax.jit(model.generate)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	jit_generate(
              inputs['''input_ids''']		,			attention_mask=inputs['''attention_mask''']		,			pad_token_id=tokenizer.pad_token_id).sequences

          SCREAMING_SNAKE_CASE_   :						int							=	tokenizer.batch_decode(lowercase_		,			skip_special_tokens=lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	[
              '''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(lowercase_		,			lowercase_)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Any							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	getattr(lowercase_		,			lowercase_)

                  SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	pt_inputs['''input_ids'''].shape
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	np.random.randint(0		,			seq_length - 1		,			size=(batch_size,))
                  for batch_idx, start_index in enumerate(lowercase_):
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	0
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	1
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	0
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	1
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	pt_model_class(lowercase_).eval()
                  SCREAMING_SNAKE_CASE_   :						Any							=	model_class(lowercase_		,			dtype=jnp.floataa)

                  SCREAMING_SNAKE_CASE_   :						List[str]							=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	fx_state

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Dict							=	pt_model(**lowercase_).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						int							=	model_class.from_pretrained(lowercase_		,			from_pt=lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	fx_model_loaded(**lowercase_).to_tuple()
                  self.assertEqual(
                      len(lowercase_)		,			len(lowercase_)		,			'''Output lengths differ between Flax and PyTorch''')
                  for fx_output_loaded, pt_output in zip(lowercase_		,			lowercase_):
                      self.assert_almost_equals(fx_output_loaded[:, -1]		,			pt_output[:, -1].numpy()		,			4e-2)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[str]							=	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
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  SCREAMING_SNAKE_CASE_   :						Dict							=	getattr(lowercase_		,			lowercase_)

                  SCREAMING_SNAKE_CASE_   :						int							=	pt_model_class(lowercase_).eval()
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_class(lowercase_		,			dtype=jnp.floataa)

                  SCREAMING_SNAKE_CASE_   :						int							=	load_flax_weights_in_pytorch_model(lowercase_		,			fx_model.params)
                  SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	pt_inputs['''input_ids'''].shape
                  SCREAMING_SNAKE_CASE_   :						Any							=	np.random.randint(0		,			seq_length - 1		,			size=(batch_size,))
                  for batch_idx, start_index in enumerate(lowercase_):
                      SCREAMING_SNAKE_CASE_   :						int							=	0
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1
                      SCREAMING_SNAKE_CASE_   :						str							=	0
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	1

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

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	pt_model(**lowercase_).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	pt_model_class.from_pretrained(lowercase_		,			from_flax=lowercase_)

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	pt_model_loaded(**lowercase_).to_tuple()

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



      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Any							=	model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model(np.ones((1, 1)))
              self.assertIsNotNone(lowercase_)


