






"""simple docstring"""






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  UpperCAmelCase_   : str   			= """0.12"""  # assumed parallelism: 8


if is_torch_available():
  import torch



def 					_A			(__a    ,				__a    ,				__a=None							) ->       str:
    """simple docstring"""






    if rng is None:
        SCREAMING_SNAKE_CASE_      :  Any    						=		random.Random()

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		1
    for dim in shape:
        total_dims *= dim

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
    for _ in range(__a							):
        values.append(rng.randint(0    ,				vocab_size - 1							)							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.array(__a    ,				dtype=jnp.intaa							).reshape(__a							)

    return output



def 					_A			(__a    ,				__a=None							) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ids_tensor(__a    ,				vocab_size=2    ,				rng=__a							)
    # make sure that at least one token is attended to for each batch
    SCREAMING_SNAKE_CASE_      :  str    						=		1
    return attn_mask




@require_flax
class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  None
      __UpperCamelCase	 =  ()
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


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

          # cut to half length & take max batch_size 3
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		inputs['''input_ids'''].shape[-1] // 2
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		inputs['''input_ids'''][:max_batch_size, :sequence_length]

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.ones_like(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		attention_mask[:max_batch_size, :sequence_length]

          # generate max 5 tokens
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		input_ids.shape[-1] + 5
          if config.eos_token_id is not None and config.pad_token_id is None:
              # hack to allow generate for models such as GPT2 as is done in `generate()`
              SCREAMING_SNAKE_CASE_      :  Any    						=		config.eos_token_id
          return config, input_ids, attention_mask, max_length
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		False
          SCREAMING_SNAKE_CASE_      :  int    						=		max_length
          SCREAMING_SNAKE_CASE_      :  str    						=		0

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Any    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  int    						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		getattr(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		pt_model_class(lowercase_).eval()
              SCREAMING_SNAKE_CASE_      :  int    						=		load_flax_weights_in_pytorch_model(lowercase_						,	flax_model.params)

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_model.generate(lowercase_).sequences
              SCREAMING_SNAKE_CASE_      :  Dict    						=		pt_model.generate(torch.tensor(lowercase_						,	dtype=torch.long))

              if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                  SCREAMING_SNAKE_CASE_      :  int    						=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

              self.assertListEqual(pt_generation_outputs.numpy().tolist()						,	flax_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Any    						=		False
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		max_length

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Any    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Any    						=		True
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		max_length

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Any    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Dict    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Dict    						=		False
          SCREAMING_SNAKE_CASE_      :  int    						=		max_length
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		2

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Any    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Dict    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Dict    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		False
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		max_length
          SCREAMING_SNAKE_CASE_      :  str    						=		2
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		2

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  int    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[0]						,	input_ids.shape[0] * config.num_return_sequences)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True
          SCREAMING_SNAKE_CASE_      :  str    						=		max_length
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		0.8
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		10
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		0.3
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		1
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		8
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		9

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  Any    						=		max_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		1
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		8
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		9

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  int    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._get_input_ids_and_config()
          SCREAMING_SNAKE_CASE_      :  int    						=		max_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		1
          SCREAMING_SNAKE_CASE_      :  Dict    						=		8
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		9

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model.generate(lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		jit_generate(lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		self._get_input_ids_and_config()

          # pad attention mask on the left
          SCREAMING_SNAKE_CASE_      :  str    						=		attention_mask.at[(0, 0)].set(0)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		False
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		max_length

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.generate(lowercase_						,	attention_mask=lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		jit_generate(lowercase_						,	attention_mask=lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		self._get_input_ids_and_config()

          # pad attention mask on the left
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		attention_mask.at[(0, 0)].set(0)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		max_length

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		model.generate(lowercase_						,	attention_mask=lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  int    						=		jit_generate(lowercase_						,	attention_mask=lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())



      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._get_input_ids_and_config()

          # pad attention mask on the left
          SCREAMING_SNAKE_CASE_      :  Any    						=		attention_mask.at[(0, 0)].set(0)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		2
          SCREAMING_SNAKE_CASE_      :  Any    						=		max_length

          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model.generate(lowercase_						,	attention_mask=lowercase_).sequences
              self.assertEqual(generation_outputs.shape[-1]						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		jit(model.generate)
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jit_generate(lowercase_						,	attention_mask=lowercase_).sequences

              self.assertListEqual(generation_outputs.tolist()						,	jit_generation_outputs.tolist())




@require_flax
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
          SCREAMING_SNAKE_CASE_      :  Any    						=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		'''Hello world'''
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tokenizer(lowercase_						,	return_tensors='''np''').input_ids

          # typos are quickly detected (the correct argument is `do_sample`)
          with self.assertRaisesRegex(lowercase_						,	'''do_samples'''):
              model.generate(lowercase_						,	do_samples=lowercase_)

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(lowercase_						,	'''foo'''):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''foo''': '''bar'''}
              model.generate(lowercase_						,	**lowercase_)










"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


