





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 							__lowercase			:






      def __init__(		self			,							lowercase_			,							lowercase_=1_4			,							lowercase_=7			,							lowercase_=True			,							lowercase_=True			,							lowercase_=False			,							lowercase_=True			,							lowercase_=9_9			,							lowercase_=3_2			,							lowercase_=4			,							lowercase_=4			,							lowercase_=4			,							lowercase_=3_7			,							lowercase_="gelu"			,							lowercase_=0.1			,							lowercase_=0.1			,							lowercase_=5_1_2			,							lowercase_=0.02			,							)     ->       Any:
       __snake_case         =			parent
       __snake_case         =			batch_size
       __snake_case         =			seq_length
       __snake_case         =			is_training
       __snake_case         =			use_input_mask
       __snake_case         =			use_token_type_ids
       __snake_case         =			use_labels
       __snake_case         =			vocab_size
       __snake_case         =			hidden_size
       __snake_case         =			rotary_dim
       __snake_case         =			num_hidden_layers
       __snake_case         =			num_attention_heads
       __snake_case         =			intermediate_size
       __snake_case         =			hidden_act
       __snake_case         =			hidden_dropout_prob
       __snake_case         =			attention_probs_dropout_prob
       __snake_case         =			max_position_embeddings
       __snake_case         =			initializer_range
       __snake_case         =			None
       __snake_case         =			vocab_size - 1
       __snake_case         =			vocab_size - 1
       __snake_case         =			vocab_size - 1






      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size)

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

       __snake_case         =			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 			_a						(		self)     ->       List[Any]:
       __snake_case         =			self.prepare_config_and_inputs()
       __snake_case         =			config_and_inputs
       __snake_case         =			{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       str:
       __snake_case         =			2_0
       __snake_case         =			model_class_name(lowercase__)

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

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

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

       __snake_case         =			model(lowercase__)

       __snake_case         =			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 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Union[str, Any]:
       __snake_case         =			2_0
       __snake_case         =			model_class_name(lowercase__)

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

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

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

       __snake_case         =			model(lowercase__			,							attention_mask=lowercase__)

       __snake_case         =			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 							__lowercase			(     snake_case_   ,							snake_case_   ,							unittest.TestCase       ):
      __UpperCAmelCase		      =						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __UpperCAmelCase		      =						(FlaxGPTJForCausalLM,) if is_flax_available() else ()






      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			FlaxGPTJModelTester(self)






      def 			_a						(		self)     ->       Dict:

       for model_class_name in self.all_model_classes:
        __snake_case         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(lowercase__			,							lowercase__			,							lowercase__			,							lowercase__)






      def 			_a						(		self)     ->       Optional[int]:

       for model_class_name in self.all_model_classes:
        __snake_case         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            lowercase__			,							lowercase__			,							lowercase__			,							lowercase__)






      @tooslow
      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			GPTaTokenizer.from_pretrained('gpt2'			,							pad_token='<|endoftext|>'			,							padding_side='left')
       __snake_case         =			tokenizer(['Hello this is a long string', 'Hey']			,							return_tensors='np'			,							padding=lowercase__			,							truncation=lowercase__)

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

       __snake_case         =			jax.jit(model.generate)

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

       __snake_case         =			tokenizer.batch_decode(lowercase__			,							skip_special_tokens=lowercase__)

       __snake_case         =			[
           """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 			_a						(		self)     ->       Any:
       __snake_case         =			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
         __snake_case         =			self._prepare_for_class(lowercase__			,							lowercase__)
         __snake_case         =			{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

         __snake_case         =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,							lowercase__)
         __snake_case         =			fx_state

         with torch.no_grad():
          __snake_case         =			pt_model(**lowercase__).to_tuple()

         __snake_case         =			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__)
          __snake_case         =			model_class.from_pretrained(lowercase__			,							from_pt=lowercase__)

         __snake_case         =			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 			_a						(		self)     ->       Any:
       __snake_case         =			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
         __snake_case         =			self._prepare_for_class(lowercase__			,							lowercase__)
         __snake_case         =			{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

         __snake_case         =			pt_model_class(lowercase__).eval()
         __snake_case         =			model_class(lowercase__			,							dtype=jnp.floataa)

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

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

         with torch.no_grad():
          __snake_case         =			pt_model(**lowercase__).to_tuple()

         __snake_case         =			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__)
          __snake_case         =			pt_model_class.from_pretrained(lowercase__			,							from_flax=lowercase__)

         with torch.no_grad():
          __snake_case         =			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 			_a						(		self)     ->       int:

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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_      :  Optional[Any]				     =  {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Any				     =  ['GPTSw3Tokenizer']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 lowerCAmelCase_      :  int				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
