





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 			__A:

     def __init__(   self,							A,							A=14,							A=7,							A=True,							A=True,							A=False,							A=True,							A=99,							A=32,							A=4,							A=4,							A=[[["global", "local"], 2]],							A=37,							A="gelu",							A=0.1,							A=0.1,							A=512,							A=7,							A=0.02,							):





            """simple docstring"""

            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    seq_length
            _UpperCamelCase														=    is_training
            _UpperCamelCase														=    use_input_mask
            _UpperCamelCase														=    use_token_type_ids
            _UpperCamelCase														=    use_labels
            _UpperCamelCase														=    vocab_size
            _UpperCamelCase														=    hidden_size
            _UpperCamelCase														=    num_hidden_layers
            _UpperCamelCase														=    num_attention_heads
            _UpperCamelCase														=    attention_types
            _UpperCamelCase														=    intermediate_size
            _UpperCamelCase														=    hidden_act
            _UpperCamelCase														=    hidden_dropout_prob
            _UpperCamelCase														=    attention_probs_dropout_prob
            _UpperCamelCase														=    max_position_embeddings
            _UpperCamelCase														=    window_size
            _UpperCamelCase														=    initializer_range
            _UpperCamelCase														=    None
            _UpperCamelCase														=    vocab_size - 1
            _UpperCamelCase														=    vocab_size - 1
            _UpperCamelCase														=    vocab_size - 1

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ids_tensor([self.batch_size, self.seq_length],							self.vocab_size   )

            _UpperCamelCase														=    None
            if self.use_input_mask:
                   _UpperCamelCase														=    random_attention_mask([self.batch_size, self.seq_length]   )

            _UpperCamelCase														=    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=snake_case_,							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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.prepare_config_and_inputs()
            _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    config_and_inputs
            _UpperCamelCase														=    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    20
            _UpperCamelCase														=    model_class_name(snake_case_   )

            _UpperCamelCase														=    model.init_cache(input_ids.shape[0],							snake_case_   )
            _UpperCamelCase														=    jnp.ones((input_ids.shape[0], max_decoder_length),							dtype='''i4'''   )

            _UpperCamelCase														=    jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1   )[None, :],							(input_ids.shape[0], input_ids.shape[-1] - 1)   )
            _UpperCamelCase														=    model(
                input_ids[:, :-1],							attention_mask=snake_case_,							past_key_values=snake_case_,							position_ids=snake_case_,							)

            _UpperCamelCase														=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],							dtype='''i4'''   )
            _UpperCamelCase														=    model(
                input_ids[:, -1:],							attention_mask=snake_case_,							past_key_values=outputs_cache.past_key_values,							position_ids=snake_case_,							)

            _UpperCamelCase														=    model(snake_case_   )

            _UpperCamelCase														=    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 						_UpperCamelCase  (   self,							A,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    20
            _UpperCamelCase														=    model_class_name(snake_case_   )

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

            _UpperCamelCase														=    model.init_cache(input_ids.shape[0],							snake_case_   )
            _UpperCamelCase														=    jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1   )[None, :],							(input_ids.shape[0], input_ids.shape[-1] - 1)   )

            _UpperCamelCase														=    model(
                input_ids[:, :-1],							attention_mask=snake_case_,							past_key_values=snake_case_,							position_ids=snake_case_,							)
            _UpperCamelCase														=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],							dtype='''i4'''   )
            _UpperCamelCase														=    model(
                input_ids[:, -1:],							past_key_values=outputs_cache.past_key_values,							attention_mask=snake_case_,							position_ids=snake_case_,							)

            _UpperCamelCase														=    model(snake_case_,							attention_mask=snake_case_   )

            _UpperCamelCase														=    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(    _a    ,      _a    ,      unittest.TestCase			):
     __A							=							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     __A							=							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    FlaxGPTNeoModelTester(self   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            for model_class_name in self.all_model_classes:
                   _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(snake_case_,							snake_case_,							snake_case_,							snake_case_   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            for model_class_name in self.all_model_classes:
                   _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward_with_attn_mask(
                       snake_case_,							snake_case_,							snake_case_,							snake_case_   )

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTaTokenizer.from_pretrained('''gpt2''',							pad_token='''<|endoftext|>''',							padding_side='''left'''   )
            _UpperCamelCase														=    tokenizer(['''Hello this is a long string''', '''Hey'''],							return_tensors='''np''',							padding=snake_case_,							truncation=snake_case_   )

            _UpperCamelCase														=    FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''   )
            _UpperCamelCase														=    False
            _UpperCamelCase														=    model.config.eos_token_id

            _UpperCamelCase														=    jax.jit(model.generate   )

            _UpperCamelCase														=    jit_generate(
                inputs['''input_ids'''],							attention_mask=inputs['''attention_mask'''],							pad_token_id=tokenizer.pad_token_id   ).sequences

            _UpperCamelCase														=    tokenizer.batch_decode(snake_case_,							skip_special_tokens=snake_case_   )

            _UpperCamelCase														=    [
                '''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(snake_case_,							snake_case_   )

     @is_pt_flax_cross_test
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    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														=    self._prepare_for_class(snake_case_,							snake_case_   )
                          _UpperCamelCase														=    {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

                          # load corresponding PyTorch class
                          _UpperCamelCase														=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                          _UpperCamelCase														=    getattr(snake_case_,							snake_case_   )

                          _UpperCamelCase							,		_UpperCamelCase														=    pt_inputs['''input_ids'''].shape
                          _UpperCamelCase														=    np.random.randint(0,							seq_length - 1,							size=(batch_size,)   )
                          for batch_idx, start_index in enumerate(snake_case_   ):
                                 _UpperCamelCase														=    0
                                 _UpperCamelCase														=    1
                                 _UpperCamelCase														=    0
                                 _UpperCamelCase														=    1
                          _UpperCamelCase														=    pt_model_class(snake_case_   ).eval()
                          _UpperCamelCase														=    model_class(snake_case_,							dtype=jnp.floataa   )

                          _UpperCamelCase														=    convert_pytorch_state_dict_to_flax(pt_model.state_dict(),							snake_case_   )
                          _UpperCamelCase														=    fx_state

                          with torch.no_grad():
                                 _UpperCamelCase														=    pt_model(**snake_case_   ).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 pt_model.save_pretrained(snake_case_   )
                                 _UpperCamelCase														=    model_class.from_pretrained(snake_case_,							from_pt=snake_case_   )

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

     @is_pt_flax_cross_test
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    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														=    self._prepare_for_class(snake_case_,							snake_case_   )
                          _UpperCamelCase														=    {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

                          # load corresponding PyTorch class
                          _UpperCamelCase														=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                          _UpperCamelCase														=    getattr(snake_case_,							snake_case_   )

                          _UpperCamelCase														=    pt_model_class(snake_case_   ).eval()
                          _UpperCamelCase														=    model_class(snake_case_,							dtype=jnp.floataa   )

                          _UpperCamelCase														=    load_flax_weights_in_pytorch_model(snake_case_,							fx_model.params   )
                          _UpperCamelCase							,		_UpperCamelCase														=    pt_inputs['''input_ids'''].shape
                          _UpperCamelCase														=    np.random.randint(0,							seq_length - 1,							size=(batch_size,)   )
                          for batch_idx, start_index in enumerate(snake_case_   ):
                                 _UpperCamelCase														=    0
                                 _UpperCamelCase														=    1
                                 _UpperCamelCase														=    0
                                 _UpperCamelCase														=    1

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

                          with torch.no_grad():
                                 _UpperCamelCase														=    pt_model(**snake_case_   ).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 fx_model.save_pretrained(snake_case_   )
                                 _UpperCamelCase														=    pt_model_class.from_pretrained(snake_case_,							from_flax=snake_case_   )

                          with torch.no_grad():
                                 _UpperCamelCase														=    pt_model_loaded(**snake_case_   ).to_tuple()

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



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            for model_class_name in self.all_model_classes:
                   _UpperCamelCase														=    model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''   )
                   _UpperCamelCase														=    model(np.ones((1, 1)   )   )
                   self.assertIsNotNone(snake_case_   )














"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




