
"""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 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=14     ,	UpperCamelCase_=7     ,	UpperCamelCase_=True     ,	UpperCamelCase_=True     ,	UpperCamelCase_=False     ,	UpperCamelCase_=True     ,	UpperCamelCase_=99     ,	UpperCamelCase_=32     ,	UpperCamelCase_=4     ,	UpperCamelCase_=4     ,	UpperCamelCase_=4     ,	UpperCamelCase_=37     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=512     ,	UpperCamelCase_=0.02     ,	):
          UpperCAmelCase__	:      Dict     							=					parent
          UpperCAmelCase__	:      str     							=					batch_size
          UpperCAmelCase__	:      Dict     							=					seq_length
          UpperCAmelCase__	:      Tuple     							=					is_training
          UpperCAmelCase__	:      Tuple     							=					use_input_mask
          UpperCAmelCase__	:      Dict     							=					use_token_type_ids
          UpperCAmelCase__	:      Tuple     							=					use_labels
          UpperCAmelCase__	:      List[str]     							=					vocab_size
          UpperCAmelCase__	:      List[str]     							=					hidden_size
          UpperCAmelCase__	:      str     							=					rotary_dim
          UpperCAmelCase__	:      Optional[int]     							=					num_hidden_layers
          UpperCAmelCase__	:      str     							=					num_attention_heads
          UpperCAmelCase__	:      Union[str, Any]     							=					intermediate_size
          UpperCAmelCase__	:      List[str]     							=					hidden_act
          UpperCAmelCase__	:      List[str]     							=					hidden_dropout_prob
          UpperCAmelCase__	:      Optional[int]     							=					attention_probs_dropout_prob
          UpperCAmelCase__	:      str     							=					max_position_embeddings
          UpperCAmelCase__	:      Optional[int]     							=					initializer_range
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Tuple     							=					vocab_size - 1
          UpperCAmelCase__	:      str     							=					vocab_size - 1
          UpperCAmelCase__	:      Dict     							=					vocab_size - 1



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

          UpperCAmelCase__	:      Optional[int]     							=					None
          if self.use_input_mask:
              UpperCAmelCase__	:      Optional[Any]     							=					random_attention_mask([self.batch_size, self.seq_length]      )

          UpperCAmelCase__	:      int     							=					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=A_     ,	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  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__	:      Optional[Any]     							=					config_and_inputs
          UpperCAmelCase__	:      Dict     							=					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					20
          UpperCAmelCase__	:      List[Any]     							=					model_class_name(A_      )

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

          UpperCAmelCase__	:      Union[str, Any]     							=					jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )
          UpperCAmelCase__	:      Tuple     							=					model(
              input_ids[:, :-1]     ,	attention_mask=A_     ,	past_key_values=A_     ,	position_ids=A_     ,	)

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

          UpperCAmelCase__	:      List[str]     							=					model(A_      )

          UpperCAmelCase__	:      Optional[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  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[Any]     							=					20
          UpperCAmelCase__	:      Dict     							=					model_class_name(A_      )

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

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

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

          UpperCAmelCase__	:      Union[str, Any]     							=					model(A_     ,	attention_mask=A_      )

          UpperCAmelCase__	:      List[str]     							=					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 				a			(					SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		unittest.TestCase      ):
      UpperCamelCase							:   str           =     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      UpperCamelCase							:   Optional[int]           =     (FlaxGPTJForCausalLM,) if is_flax_available() else ()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					FlaxGPTJModelTester(self      )



      def  __snake_case							(   self      ):




          for model_class_name in self.all_model_classes:
              UpperCAmelCase__	:      List[str]     							=					self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(A_     ,	A_     ,	A_     ,	A_      )



      def  __snake_case							(   self      ):




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



      @tooslow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					GPTaTokenizer.from_pretrained('gpt2'     ,	pad_token='<|endoftext|>'     ,	padding_side='left'      )
          UpperCAmelCase__	:      int     							=					tokenizer(['Hello this is a long string', 'Hey']     ,	return_tensors='np'     ,	padding=A_     ,	truncation=A_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
          UpperCAmelCase__	:      str     							=					False
          UpperCAmelCase__	:      Union[str, Any]     							=					model.config.eos_token_id

          UpperCAmelCase__	:      Dict     							=					jax.jit(model.generate      )

          UpperCAmelCase__	:      Optional[Any]     							=					jit_generate(
              inputs['input_ids']     ,	attention_mask=inputs['attention_mask']     ,	pad_token_id=tokenizer.pad_token_id      ).sequences

          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.batch_decode(A_     ,	skip_special_tokens=A_      )

          UpperCAmelCase__	:      List[str]     							=					[
              '''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(A_     ,	A_      )



      @is_pt_flax_cross_test
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					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__	:      Union[str, Any]     							=					self._prepare_for_class(A_     ,	A_      )
                  UpperCAmelCase__	:      Union[str, Any]     							=					{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

                  UpperCAmelCase__	:      str     							=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	A_      )
                  UpperCAmelCase__	:      List[Any]     							=					fx_state

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

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

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

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



      @is_pt_flax_cross_test
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					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__	:      Any     							=					self._prepare_for_class(A_     ,	A_      )
                  UpperCAmelCase__	:      Tuple     							=					{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

                  UpperCAmelCase__	:      int     							=					pt_model_class(A_      ).eval()
                  UpperCAmelCase__	:      Union[str, Any]     							=					model_class(A_     ,	dtype=jnp.floataa      )

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

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

                  with torch.no_grad():
                      UpperCAmelCase__	:      Union[str, Any]     							=					pt_model(**A_      ).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(A_      )
                      UpperCAmelCase__	:      Optional[int]     							=					pt_model_class.from_pretrained(A_     ,	from_flax=A_      )

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

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



      @tooslow
      def  __snake_case							(   self      ):




          for model_class_name in self.all_model_classes:
              UpperCAmelCase__	:      Union[str, Any]     							=					model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
              UpperCAmelCase__	:      Optional[int]     							=					model(np.ones((1, 1)      )      )
              self.assertIsNotNone(A_      )



"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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