




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     UpperCamelCase_   :

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   seq_length
           A__							=   is_training
           A__							=   use_input_mask
           A__							=   use_token_type_ids
           A__							=   use_labels
           A__							=   vocab_size
           A__							=   hidden_size
           A__							=   rotary_dim
           A__							=   num_hidden_layers
           A__							=   num_attention_heads
           A__							=   intermediate_size
           A__							=   hidden_act
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   initializer_range
           A__							=   None
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

           A__							=   ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size)

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

           A__							=   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=UpperCAmelCase__ ,			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)						->Optional[int]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

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

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

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

           A__							=   model(UpperCAmelCase__)

           A__							=   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 ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

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

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

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

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)

           A__							=   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     UpperCamelCase_   (			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])						->List[Any]:





           '''simple docstring'''

           A__							=   FlaxGPTJModelTester(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

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

           A__							=   FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
           A__							=   False
           A__							=   model.config.eos_token_id

           A__							=   jax.jit(model.generate)

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

           A__							=   tokenizer.batch_decode(UpperCAmelCase__ ,			skip_special_tokens=UpperCAmelCase__)

           A__							=   [
               '''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(UpperCAmelCase__ ,			UpperCAmelCase__)


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                         A__							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			UpperCAmelCase__)
                         A__							=   fx_state

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

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


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

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

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

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

                         with torch.no_grad():
                                A__							=   pt_model_loaded(**UpperCAmelCase__).to_tuple()

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





    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__							=   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
                  A__							=   model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)








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     UpperCamelCase_   :

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   seq_length
           A__							=   is_training
           A__							=   use_input_mask
           A__							=   use_token_type_ids
           A__							=   use_labels
           A__							=   vocab_size
           A__							=   hidden_size
           A__							=   rotary_dim
           A__							=   num_hidden_layers
           A__							=   num_attention_heads
           A__							=   intermediate_size
           A__							=   hidden_act
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   initializer_range
           A__							=   None
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

           A__							=   ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size)

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

           A__							=   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=UpperCAmelCase__ ,			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)						->Optional[int]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

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

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

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

           A__							=   model(UpperCAmelCase__)

           A__							=   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 ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

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

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

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

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)

           A__							=   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     UpperCamelCase_   (			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])						->List[Any]:





           '''simple docstring'''

           A__							=   FlaxGPTJModelTester(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

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

           A__							=   FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
           A__							=   False
           A__							=   model.config.eos_token_id

           A__							=   jax.jit(model.generate)

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

           A__							=   tokenizer.batch_decode(UpperCAmelCase__ ,			skip_special_tokens=UpperCAmelCase__)

           A__							=   [
               '''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(UpperCAmelCase__ ,			UpperCAmelCase__)


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                         A__							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			UpperCAmelCase__)
                         A__							=   fx_state

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

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


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

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

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

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

                         with torch.no_grad():
                                A__							=   pt_model_loaded(**UpperCAmelCase__).to_tuple()

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





    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__							=   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
                  A__							=   model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)


