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

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


if is_torch_available():
  import torch



def   __A		(			_A							,				_A							,				_A=None					):



 """simple docstring"""


 if rng is None:
  __a       =  random.Random()

 __a       =  1
 for dim in shape:
  total_dims *= dim

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

 __a       =  np.array(_A							,				dtype=jnp.intaa					).reshape(_A					)

 return output



def   __A		(			_A							,				_A=None					):



 """simple docstring"""


 __a       =  ids_tensor(_A							,				vocab_size=2							,				rng=_A					)
 # make sure that at least one token is attended to for each batch
 __a       =  1
 return attn_mask
@require_flax
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			()

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   # cut to half length & take max batch_size 3
   __a       =  2
   __a       =  inputs["input_ids"].shape[-1] // 2
   __a       =  inputs["input_ids"][:max_batch_size, :sequence_length]

   __a       =  jnp.ones_like(__SCREAMING_SNAKE_CASE       )
   __a       =  attention_mask[:max_batch_size, :sequence_length]

   # generate max 5 tokens
   __a       =  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()`
    __a       =  config.eos_token_id
   return config, input_ids, attention_mask, max_length

  @is_pt_flax_cross_test
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  False
   __a       =  max_length
   __a       =  0

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
    __a       =  getattr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  pt_model_class(__SCREAMING_SNAKE_CASE       ).eval()
    __a       =  load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE     ,							flax_model.params       )

    __a       =  flax_model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    __a       =  pt_model.generate(torch.tensor(__SCREAMING_SNAKE_CASE     ,							dtype=torch.long       )       )

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

    self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,							flax_generation_outputs.tolist()       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  False
   __a       =  max_length

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  True
   __a       =  max_length

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  False
   __a       =  max_length
   __a       =  2

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  False
   __a       =  max_length
   __a       =  2
   __a       =  2

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[0]     ,							input_ids.shape[0] * config.num_return_sequences       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  True
   __a       =  max_length
   __a       =  0.8
   __a       =  10
   __a       =  0.3
   __a       =  1
   __a       =  8
   __a       =  9

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  max_length
   __a       =  1
   __a       =  8
   __a       =  9

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()
   __a       =  max_length
   __a       =  2
   __a       =  1
   __a       =  8
   __a       =  9

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()

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

   __a       =  False
   __a       =  max_length

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()

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

   __a       =  True
   __a       =  max_length

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences

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

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       ,     __a       ,     __a       ,     __a       =  self._get_input_ids_and_config()

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

   __a       =  2
   __a       =  max_length

   for model_class in self.all_generative_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )

    __a       =  model.generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences
    self.assertEqual(generation_outputs.shape[-1]     ,							__SCREAMING_SNAKE_CASE       )

    __a       =  jit(model.generate       )
    __a       =  jit_generate(__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       ).sequences

    self.assertListEqual(generation_outputs.tolist()     ,							jit_generation_outputs.tolist()       )
@require_flax
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"       )
   __a       =  FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"       )

   __a       =  "Hello world"
   __a       =  tokenizer(__SCREAMING_SNAKE_CASE     ,							return_tensors="np"       ).input_ids

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

   # arbitrary arguments that will not be used anywhere are also not accepted
   with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE     ,							"foo"       ):
    __a       =  {"foo": "bar"}
    model.generate(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


