



"""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

							lowerCAmelCase_   						=   '0.12'  # assumed parallelism: 8


if is_torch_available():
							import torch



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None	)							->      str:
		if rng is None:
				lowercase__   :    Optional[int]				=			random.Random()

		lowercase__   :    str				=			1
		for dim in shape:
				total_dims *= dim

		lowercase__   :    Union[str, Any]				=			[]
		for _ in range(__lowerCamelCase	):
				values.append(rng.randint(0						,     vocab_size - 1	)	)

		lowercase__   :    Any				=			np.array(__lowerCamelCase						,     dtype=jnp.intaa	).reshape(__lowerCamelCase	)

		return output



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=None	)							->      List[Any]:
		lowercase__   :    Optional[int]				=			ids_tensor(__lowerCamelCase						,     vocab_size=2						,     rng=__lowerCamelCase	)
		# make sure that at least one token is attended to for each batch
		lowercase__   :    Union[str, Any]				=			1
		return attn_mask
@require_flax
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				None
		lowerCAmelCase			:					int   	=				()







		def 					UpperCAmelCase    (	self						:  Any						)					->		Any:






				"""simple docstring"""



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

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

				lowercase__   :    Any				=			jnp.ones_like(_snake_case						)
				lowercase__   :    Any				=			attention_mask[:max_batch_size, :sequence_length]

				# generate max 5 tokens
				lowercase__   :    Tuple				=			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()`
						lowercase__   :    str				=			config.eos_token_id
				return config, input_ids, attention_mask, max_length







		@is_pt_flax_cross_test
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Dict				=			self._get_input_ids_and_config()
				lowercase__   :    Dict				=			False
				lowercase__   :    List[str]				=			max_length
				lowercase__   :    List[str]				=			0

				for model_class in self.all_generative_model_classes:
						lowercase__   :    List[str]				=			model_class(_snake_case						)

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

						lowercase__   :    Tuple				=			flax_model.generate(_snake_case						).sequences
						lowercase__   :    List[str]				=			pt_model.generate(torch.tensor(_snake_case			,dtype=torch.long						)						)

						if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
								lowercase__   :    List[Any]				=			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		List[str]:






				"""simple docstring"""



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

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Optional[int]				=			model_class(_snake_case						)

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

						lowercase__   :    Optional[Any]				=			jit(model.generate						)
						lowercase__   :    Optional[int]				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  str						)					->		str:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Optional[Any]				=			self._get_input_ids_and_config()
				lowercase__   :    str				=			True
				lowercase__   :    Optional[Any]				=			max_length

				for model_class in self.all_generative_model_classes:
						lowercase__   :    str				=			model_class(_snake_case						)

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

						lowercase__   :    Tuple				=			jit(model.generate						)
						lowercase__   :    Optional[Any]				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Any				=			self._get_input_ids_and_config()
				lowercase__   :    str				=			False
				lowercase__   :    Optional[int]				=			max_length
				lowercase__   :    int				=			2

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Dict				=			model_class(_snake_case						)

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

						lowercase__   :    Dict				=			jit(model.generate						)
						lowercase__   :    Optional[int]				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Tuple:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Tuple				=			self._get_input_ids_and_config()
				lowercase__   :    Dict				=			False
				lowercase__   :    Tuple				=			max_length
				lowercase__   :    Tuple				=			2
				lowercase__   :    List[Any]				=			2

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Optional[int]				=			model_class(_snake_case						)

						lowercase__   :    str				=			model.generate(_snake_case						).sequences
						self.assertEqual(generation_outputs.shape[0]			,input_ids.shape[0] * config.num_return_sequences						)







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



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

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Any				=			model_class(_snake_case						)

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

						lowercase__   :    Optional[int]				=			jit(model.generate						)
						lowercase__   :    Tuple				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Tuple:






				"""simple docstring"""



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

				for model_class in self.all_generative_model_classes:
						lowercase__   :    List[str]				=			model_class(_snake_case						)

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

						lowercase__   :    List[str]				=			jit(model.generate						)
						lowercase__   :    Dict				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Tuple:






				"""simple docstring"""



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

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

						lowercase__   :    str				=			model.generate(_snake_case						).sequences
						self.assertEqual(generation_outputs.shape[-1]			,_snake_case						)

						lowercase__   :    int				=			jit(model.generate						)
						lowercase__   :    Optional[Any]				=			jit_generate(_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Tuple:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Dict				=			self._get_input_ids_and_config()

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

				lowercase__   :    Union[str, Any]				=			False
				lowercase__   :    int				=			max_length

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Optional[int]				=			model_class(_snake_case						)

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

						lowercase__   :    Any				=			jit(model.generate						)
						lowercase__   :    List[Any]				=			jit_generate(_snake_case			,attention_mask=_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    str				=			self._get_input_ids_and_config()

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

				lowercase__   :    Any				=			True
				lowercase__   :    List[str]				=			max_length

				for model_class in self.all_generative_model_classes:
						lowercase__   :    List[str]				=			model_class(_snake_case						)

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

						lowercase__   :    Optional[Any]				=			jit(model.generate						)
						lowercase__   :    Union[str, Any]				=			jit_generate(_snake_case			,attention_mask=_snake_case						).sequences

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







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    int				=			self._get_input_ids_and_config()

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

				lowercase__   :    Optional[int]				=			2
				lowercase__   :    List[str]				=			max_length

				for model_class in self.all_generative_model_classes:
						lowercase__   :    Any				=			model_class(_snake_case						)

						lowercase__   :    int				=			model.generate(_snake_case			,attention_mask=_snake_case						).sequences
						self.assertEqual(generation_outputs.shape[-1]			,_snake_case						)

						lowercase__   :    List[Any]				=			jit(model.generate						)
						lowercase__   :    int				=			jit_generate(_snake_case			,attention_mask=_snake_case						).sequences

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





@require_flax
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Dict				=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''						)
				lowercase__   :    str				=			FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''						)

				lowercase__   :    Union[str, Any]				=			'''Hello world'''
				lowercase__   :    List[Any]				=			tokenizer(_snake_case			,return_tensors='''np'''						).input_ids

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

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

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


