


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_		:

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase						, _UpperCAmelCase=14						, _UpperCAmelCase=7						, _UpperCAmelCase=True						, _UpperCAmelCase=True						, _UpperCAmelCase=False						, _UpperCAmelCase=True						, _UpperCAmelCase=99						, _UpperCAmelCase=32						, _UpperCAmelCase=4						, _UpperCAmelCase=4						, _UpperCAmelCase=[[["global", "local"], 2]]						, _UpperCAmelCase=37						, _UpperCAmelCase="gelu"						, _UpperCAmelCase=0.1						, _UpperCAmelCase=0.1						, _UpperCAmelCase=5_12						, _UpperCAmelCase=7						, _UpperCAmelCase=0.02						, ):
       snake_case_			= parent
       snake_case_			= batch_size
       snake_case_			= seq_length
       snake_case_			= is_training
       snake_case_			= use_input_mask
       snake_case_			= use_token_type_ids
       snake_case_			= use_labels
       snake_case_			= vocab_size
       snake_case_			= hidden_size
       snake_case_			= num_hidden_layers
       snake_case_			= num_attention_heads
       snake_case_			= attention_types
       snake_case_			= intermediate_size
       snake_case_			= hidden_act
       snake_case_			= hidden_dropout_prob
       snake_case_			= attention_probs_dropout_prob
       snake_case_			= max_position_embeddings
       snake_case_			= window_size
       snake_case_			= initializer_range
       snake_case_			= None
       snake_case_			= vocab_size - 1
       snake_case_			= vocab_size - 1
       snake_case_			= vocab_size - 1



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

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

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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       snake_case_			= 20
       snake_case_			= model_class_name(_UpperCAmelCase   )

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

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

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

       snake_case_			= model(_UpperCAmelCase   )

       snake_case_			= 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						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       snake_case_			= 20
       snake_case_			= model_class_name(_UpperCAmelCase   )

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

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

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

       snake_case_			= model(_UpperCAmelCase						, attention_mask=_UpperCAmelCase   )

       snake_case_			= 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_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 __snake_case				=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= FlaxGPTNeoModelTester(self   )



 def 		UpperCamelCase__	(      self   ):
       for model_class_name in self.all_model_classes:
             snake_case_			,       snake_case_			,       snake_case_			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )



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



 @slow
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= GPTaTokenizer.from_pretrained('''gpt2'''						, pad_token='''<|endoftext|>'''						, padding_side='''left'''   )
       snake_case_			= tokenizer(['''Hello this is a long string''', '''Hey''']						, return_tensors='''np'''						, padding=_UpperCAmelCase						, truncation=_UpperCAmelCase   )

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

       snake_case_			= jax.jit(model.generate   )

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

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

       snake_case_			= [
           '''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(_UpperCAmelCase						, _UpperCAmelCase   )



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

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

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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





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



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    ["input_ids", "attention_mask"]



 def __init__(      self						, _UpperCAmelCase="[PAD]"						, _UpperCAmelCase="[BOS]"						, _UpperCAmelCase="[EOS]"						, _UpperCAmelCase="[MASK]"						, _UpperCAmelCase="[CLS]"						, _UpperCAmelCase="[SEP]"						, _UpperCAmelCase=20_48						, **_UpperCAmelCase						, ):
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else pad_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else bos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else eos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else mask_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else cls_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else sep_token

       super().__init__(
           pad_token=_UpperCAmelCase						, bos_token=_UpperCAmelCase						, eos_token=_UpperCAmelCase						, mask_token=_UpperCAmelCase						, cls_token=_UpperCAmelCase						, sep_token=_UpperCAmelCase						, model_max_length=_UpperCAmelCase						, **_UpperCAmelCase						, )

       snake_case_			= 2**8  # utf is 8 bits

       # define special tokens dict
       snake_case_			= {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       snake_case_			= len(self.special_tokens_encoder   )
       snake_case_			= {v: k for k, v in self.special_tokens_encoder.items()}



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
             snake_case_			= chr(_UpperCAmelCase   )
             snake_case_			= i + len(self.special_tokens_encoder   )
       return vocab



 @property
 def 		UpperCamelCase__	(      self   ):
       return self._utf_vocab_size + self._num_special_tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None						, _UpperCAmelCase = False   ):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase						, token_ids_a=_UpperCAmelCase						, already_has_special_tokens=_UpperCAmelCase   )

       # normal case: some special tokens
       if token_ids_a is None:
             return [1] + [0] * len(_UpperCAmelCase   ) + [1]
       return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= [chr(_UpperCAmelCase   ) for i in text.encode('''utf-8'''   )]
       return tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if token in self.special_tokens_encoder:
             snake_case_			= self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             snake_case_			= self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase   ) != 1:
             snake_case_			= self.unk_token_id
       else:
             snake_case_			= ord(_UpperCAmelCase   ) + self._num_special_tokens
       return token_id



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if index in self.special_tokens_decoder:
             snake_case_			= self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
             snake_case_			= self.added_tokens_decoder[index]
       else:
             snake_case_			= chr(index - self._num_special_tokens   )
       return token



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= b''''''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.added_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.special_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             elif token in self.added_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             else:
                   snake_case_			= bytes([ord(_UpperCAmelCase   )]   )
             bstring += tok_string
       snake_case_			= bstring.decode('''utf-8'''						, errors='''replace'''   )
       return string





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       return ()