
'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
   import torch







class 	__lowerCAmelCase    :



     def __init__(self						,	lowerCAmelCase__						,	lowerCAmelCase__=1_4						,	lowerCAmelCase__=7						,	lowerCAmelCase__=True						,	lowerCAmelCase__=True						,	lowerCAmelCase__=False						,	lowerCAmelCase__=True						,	lowerCAmelCase__=9_9						,	lowerCAmelCase__=3_2						,	lowerCAmelCase__=4						,	lowerCAmelCase__=4						,	lowerCAmelCase__=[[["global", "local"], 2]]						,	lowerCAmelCase__=3_7						,	lowerCAmelCase__="gelu"						,	lowerCAmelCase__=0.1						,	lowerCAmelCase__=0.1						,	lowerCAmelCase__=5_1_2						,	lowerCAmelCase__=7						,	lowerCAmelCase__=0.0_2						,	):
          _UpperCAmelCase					:  Any         =    parent
          _UpperCAmelCase					:  Tuple         =    batch_size
          _UpperCAmelCase					:  Optional[Any]         =    seq_length
          _UpperCAmelCase					:  Dict         =    is_training
          _UpperCAmelCase					:  Any         =    use_input_mask
          _UpperCAmelCase					:  int         =    use_token_type_ids
          _UpperCAmelCase					:  Dict         =    use_labels
          _UpperCAmelCase					:  Optional[int]         =    vocab_size
          _UpperCAmelCase					:  int         =    hidden_size
          _UpperCAmelCase					:  List[str]         =    num_hidden_layers
          _UpperCAmelCase					:  Any         =    num_attention_heads
          _UpperCAmelCase					:  Any         =    attention_types
          _UpperCAmelCase					:  str         =    intermediate_size
          _UpperCAmelCase					:  Dict         =    hidden_act
          _UpperCAmelCase					:  int         =    hidden_dropout_prob
          _UpperCAmelCase					:  str         =    attention_probs_dropout_prob
          _UpperCAmelCase					:  Tuple         =    max_position_embeddings
          _UpperCAmelCase					:  Dict         =    window_size
          _UpperCAmelCase					:  Optional[int]         =    initializer_range
          _UpperCAmelCase					:  int         =    None
          _UpperCAmelCase					:  Any         =    vocab_size - 1
          _UpperCAmelCase					:  int         =    vocab_size - 1
          _UpperCAmelCase					:  Union[str, Any]         =    vocab_size - 1



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size	)

          _UpperCAmelCase					:  Union[str, Any]         =    None
          if self.use_input_mask:
               _UpperCAmelCase					:  Any         =    random_attention_mask([self.batch_size, self.seq_length]	)

          _UpperCAmelCase					:  str         =    GPTNeoConfig(
              vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_layers=self.num_hidden_layers						,	num_heads=self.num_attention_heads						,	max_position_embeddings=self.max_position_embeddings						,	use_cache=lowerCAmelCase__						,	bos_token_id=self.bos_token_id						,	eos_token_id=self.eos_token_id						,	pad_token_id=self.pad_token_id						,	window_size=self.window_size						,	attention_types=self.attention_types						,	)

          return (config, input_ids, input_mask)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    self.prepare_config_and_inputs()
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Union[str, Any]         =    config_and_inputs
          _UpperCAmelCase					:  Union[str, Any]         =    {"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    2_0
          _UpperCAmelCase					:  Union[str, Any]         =    model_class_name(lowerCAmelCase__	)

          _UpperCAmelCase					:  Dict         =    model.init_cache(input_ids.shape[0]						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    jnp.ones((input_ids.shape[0], max_decoder_length)						,	dtype="""i4"""	)

          _UpperCAmelCase					:  List[Any]         =    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1	)[None, :]						,	(input_ids.shape[0], input_ids.shape[-1] - 1)	)
          _UpperCAmelCase					:  Optional[int]         =    model(
              input_ids[:, :-1]						,	attention_mask=lowerCAmelCase__						,	past_key_values=lowerCAmelCase__						,	position_ids=lowerCAmelCase__						,	)

          _UpperCAmelCase					:  Tuple         =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,	dtype="""i4"""	)
          _UpperCAmelCase					:  Optional[Any]         =    model(
              input_ids[:, -1:]						,	attention_mask=lowerCAmelCase__						,	past_key_values=outputs_cache.past_key_values						,	position_ids=lowerCAmelCase__						,	)

          _UpperCAmelCase					:  Dict         =    model(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    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						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  int         =    2_0
          _UpperCAmelCase					:  Union[str, Any]         =    model_class_name(lowerCAmelCase__	)

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

          _UpperCAmelCase					:  int         =    model.init_cache(input_ids.shape[0]						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    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=lowerCAmelCase__						,	past_key_values=lowerCAmelCase__						,	position_ids=lowerCAmelCase__						,	)
          _UpperCAmelCase					:  List[str]         =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,	dtype="""i4"""	)
          _UpperCAmelCase					:  int         =    model(
              input_ids[:, -1:]						,	past_key_values=outputs_cache.past_key_values						,	attention_mask=lowerCAmelCase__						,	position_ids=lowerCAmelCase__						,	)

          _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__						,	attention_mask=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    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 	__lowerCAmelCase    (  __a       ,					__a       ,					unittest.TestCase				):
     snake_case   :    List[Any]							      =      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     snake_case   :    int							      =      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    FlaxGPTNeoModelTester(self	)



     def     snake_case_ (self	):
          for model_class_name in self.all_model_classes:
               _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  List[str]         =    self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self	):
          for model_class_name in self.all_model_classes:
               _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     @slow
     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=lowerCAmelCase__						,	truncation=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[str]         =    FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""	)
          _UpperCAmelCase					:  Optional[int]         =    False
          _UpperCAmelCase					:  Tuple         =    model.config.eos_token_id

          _UpperCAmelCase					:  Any         =    jax.jit(model.generate	)

          _UpperCAmelCase					:  List[str]         =    jit_generate(
              inputs["""input_ids"""]						,	attention_mask=inputs["""attention_mask"""]						,	pad_token_id=tokenizer.pad_token_id	).sequences

          _UpperCAmelCase					:  str         =    tokenizer.batch_decode(lowerCAmelCase__						,	skip_special_tokens=lowerCAmelCase__	)

          _UpperCAmelCase					:  Any         =    [
              """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(lowerCAmelCase__						,	lowerCAmelCase__	)



     @is_pt_flax_cross_test
     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Union[str, 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					:  str         =    self._prepare_for_class(lowerCAmelCase__						,	lowerCAmelCase__	)
                    _UpperCAmelCase					:  int         =    {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCAmelCase					:  Optional[int]         =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCAmelCase					:  Optional[Any]         =    getattr(lowerCAmelCase__						,	lowerCAmelCase__	)

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

                    _UpperCAmelCase					:  List[Any]         =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,	lowerCAmelCase__	)
                    _UpperCAmelCase					:  Any         =    fx_state

                    with torch.no_grad():
                         _UpperCAmelCase					:  Dict         =    pt_model(**lowerCAmelCase__	).to_tuple()

                    _UpperCAmelCase					:  List[str]         =    fx_model(**lowerCAmelCase__	).to_tuple()
                    self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	)						,	"""Output lengths differ between Flax and PyTorch"""	)
                    for fx_output, pt_output in zip(lowerCAmelCase__						,	lowerCAmelCase__	):
                         self.assert_almost_equals(fx_output[:, -1]						,	pt_output[:, -1].numpy()						,	4e-2	)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                         pt_model.save_pretrained(lowerCAmelCase__	)
                         _UpperCAmelCase					:  List[str]         =    model_class.from_pretrained(lowerCAmelCase__						,	from_pt=lowerCAmelCase__	)

                    _UpperCAmelCase					:  int         =    fx_model_loaded(**lowerCAmelCase__	).to_tuple()
                    self.assertEqual(
                        len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	)						,	"""Output lengths differ between Flax and PyTorch"""	)
                    for fx_output_loaded, pt_output in zip(lowerCAmelCase__						,	lowerCAmelCase__	):
                         self.assert_almost_equals(fx_output_loaded[:, -1]						,	pt_output[:, -1].numpy()						,	4e-2	)



     @is_pt_flax_cross_test
     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Union[str, 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					:  List[str]         =    self._prepare_for_class(lowerCAmelCase__						,	lowerCAmelCase__	)
                    _UpperCAmelCase					:  List[str]         =    {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCAmelCase					:  Optional[Any]         =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCAmelCase					:  Union[str, Any]         =    getattr(lowerCAmelCase__						,	lowerCAmelCase__	)

                    _UpperCAmelCase					:  Tuple         =    pt_model_class(lowerCAmelCase__	).eval()
                    _UpperCAmelCase					:  List[str]         =    model_class(lowerCAmelCase__						,	dtype=jnp.floataa	)

                    _UpperCAmelCase					:  Tuple         =    load_flax_weights_in_pytorch_model(lowerCAmelCase__						,	fx_model.params	)
                    _UpperCAmelCase  ,     _UpperCAmelCase					:  Any         =    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(lowerCAmelCase__	):
                         _UpperCAmelCase					:  Dict         =    0
                         _UpperCAmelCase					:  List[str]         =    1
                         _UpperCAmelCase					:  List[Any]         =    0
                         _UpperCAmelCase					:  Dict         =    1

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

                    with torch.no_grad():
                         _UpperCAmelCase					:  str         =    pt_model(**lowerCAmelCase__	).to_tuple()

                    _UpperCAmelCase					:  Optional[Any]         =    fx_model(**lowerCAmelCase__	).to_tuple()
                    self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	)						,	"""Output lengths differ between Flax and PyTorch"""	)
                    for fx_output, pt_output in zip(lowerCAmelCase__						,	lowerCAmelCase__	):
                         self.assert_almost_equals(fx_output[:, -1]						,	pt_output[:, -1].numpy()						,	4e-2	)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                         fx_model.save_pretrained(lowerCAmelCase__	)
                         _UpperCAmelCase					:  Tuple         =    pt_model_class.from_pretrained(lowerCAmelCase__						,	from_flax=lowerCAmelCase__	)

                    with torch.no_grad():
                         _UpperCAmelCase					:  Union[str, Any]         =    pt_model_loaded(**lowerCAmelCase__	).to_tuple()

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



     @slow
     def     snake_case_ (self	):
          for model_class_name in self.all_model_classes:
               _UpperCAmelCase					:  Dict         =    model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""	)
               _UpperCAmelCase					:  int         =    model(np.ones((1, 1)	)	)
               self.assertIsNotNone(lowerCAmelCase__	)






'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		__UpperCAmelCase    				=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)