
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					,						__magic_name__					,						__magic_name__=1_4					,						__magic_name__=7					,						__magic_name__=True					,						__magic_name__=True					,						__magic_name__=False					,						__magic_name__=True					,						__magic_name__=9_9					,						__magic_name__=3_2					,						__magic_name__=4					,						__magic_name__=4					,						__magic_name__=[[["global", "local"], 2]]					,						__magic_name__=3_7					,						__magic_name__="gelu"					,						__magic_name__=0.1					,						__magic_name__=0.1					,						__magic_name__=5_1_2					,						__magic_name__=7					,						__magic_name__=0.02					,						):
       lowerCamelCase	:			Dict           =   parent
       lowerCamelCase	:			int           =   batch_size
       lowerCamelCase	:			Optional[Any]           =   seq_length
       lowerCamelCase	:			Union[str, Any]           =   is_training
       lowerCamelCase	:			str           =   use_input_mask
       lowerCamelCase	:			Dict           =   use_token_type_ids
       lowerCamelCase	:			Optional[int]           =   use_labels
       lowerCamelCase	:			Union[str, Any]           =   vocab_size
       lowerCamelCase	:			Union[str, Any]           =   hidden_size
       lowerCamelCase	:			List[str]           =   num_hidden_layers
       lowerCamelCase	:			List[Any]           =   num_attention_heads
       lowerCamelCase	:			Dict           =   attention_types
       lowerCamelCase	:			Union[str, Any]           =   intermediate_size
       lowerCamelCase	:			Dict           =   hidden_act
       lowerCamelCase	:			Dict           =   hidden_dropout_prob
       lowerCamelCase	:			List[str]           =   attention_probs_dropout_prob
       lowerCamelCase	:			Optional[int]           =   max_position_embeddings
       lowerCamelCase	:			List[Any]           =   window_size
       lowerCamelCase	:			List[Any]           =   initializer_range
       lowerCamelCase	:			str           =   None
       lowerCamelCase	:			Any           =   vocab_size - 1
       lowerCamelCase	:			int           =   vocab_size - 1
       lowerCamelCase	:			List[Any]           =   vocab_size - 1
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Optional[int]           =   ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size   )

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

       lowerCamelCase	:			List[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=__magic_name__					,						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   ):
       lowerCamelCase	:			List[str]           =   self.prepare_config_and_inputs()
       lowerCamelCase	:			Tuple           =   config_and_inputs
       lowerCamelCase	:			Union[str, Any]           =   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			str           =   2_0
       lowerCamelCase	:			Tuple           =   model_class_name(__magic_name__   )

       lowerCamelCase	:			Optional[int]           =   model.init_cache(input_ids.shape[0]					,						__magic_name__   )
       lowerCamelCase	:			Tuple           =   jnp.ones((input_ids.shape[0], max_decoder_length)					,						dtype="""i4"""   )

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

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

       lowerCamelCase	:			Tuple           =   model(__magic_name__   )

       lowerCamelCase	:			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 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			Optional[Any]           =   2_0
       lowerCamelCase	:			Tuple           =   model_class_name(__magic_name__   )

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

       lowerCamelCase	:			Any           =   model.init_cache(input_ids.shape[0]					,						__magic_name__   )
       lowerCamelCase	:			List[Any]           =   jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,						(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCamelCase	:			Optional[Any]           =   model(
           input_ids[:, :-1]					,						attention_mask=__magic_name__					,						past_key_values=__magic_name__					,						position_ids=__magic_name__					,						)
       lowerCamelCase	:			Union[str, Any]           =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,						dtype="""i4"""   )
       lowerCamelCase	:			str           =   model(
           input_ids[:, -1:]					,						past_key_values=outputs_cache.past_key_values					,						attention_mask=__magic_name__					,						position_ids=__magic_name__					,						)

       lowerCamelCase	:			Tuple           =   model(__magic_name__					,						attention_mask=__magic_name__   )

       lowerCamelCase	:			Optional[int]           =   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__	(			__SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     unittest.TestCase):
      _UpperCAmelCase       :					List[Any]					 =    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      _UpperCAmelCase       :					Optional[int]					 =    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   FlaxGPTNeoModelTester(self   )
      def 		UpperCamelCase__							(   self   ):
       for model_class_name in self.all_model_classes:
        lowerCamelCase	:			List[str]           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for model_class_name in self.all_model_classes:
        lowerCamelCase	:			List[str]           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   )
      @slow
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Tuple           =   GPTaTokenizer.from_pretrained("""gpt2"""					,						pad_token="""<|endoftext|>"""					,						padding_side="""left"""   )
       lowerCamelCase	:			Tuple           =   tokenizer(["""Hello this is a long string""", """Hey"""]					,						return_tensors="""np"""					,						padding=__magic_name__					,						truncation=__magic_name__   )

       lowerCamelCase	:			Optional[Any]           =   FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""   )
       lowerCamelCase	:			Union[str, Any]           =   False
       lowerCamelCase	:			Tuple           =   model.config.eos_token_id

       lowerCamelCase	:			List[str]           =   jax.jit(model.generate   )

       lowerCamelCase	:			Any           =   jit_generate(
           inputs["""input_ids"""]					,						attention_mask=inputs["""attention_mask"""]					,						pad_token_id=tokenizer.pad_token_id   ).sequences

       lowerCamelCase	:			Optional[int]           =   tokenizer.batch_decode(__magic_name__					,						skip_special_tokens=__magic_name__   )

       lowerCamelCase	:			int           =   [
           """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(__magic_name__					,						__magic_name__   )
      @is_pt_flax_cross_test
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			str           =   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
         lowerCamelCase	:			Dict           =   self._prepare_for_class(__magic_name__					,						__magic_name__   )
         lowerCamelCase	:			Optional[int]           =   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         lowerCamelCase	:			List[str]           =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase	:			Dict           =   getattr(__magic_name__					,						__magic_name__   )

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

         lowerCamelCase	:			str           =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,						__magic_name__   )
         lowerCamelCase	:			Tuple           =   fx_state

         with torch.no_grad():
          lowerCamelCase	:			Optional[int]           =   pt_model(**__magic_name__   ).to_tuple()

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

         with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(__magic_name__   )
          lowerCamelCase	:			Optional[int]           =   model_class.from_pretrained(__magic_name__					,						from_pt=__magic_name__   )

         lowerCamelCase	:			Optional[int]           =   fx_model_loaded(**__magic_name__   ).to_tuple()
         self.assertEqual(
             len(__magic_name__   )					,						len(__magic_name__   )					,						"""Output lengths differ between Flax and PyTorch"""   )
         for fx_output_loaded, pt_output in zip(__magic_name__					,						__magic_name__   ):
          self.assert_almost_equals(fx_output_loaded[:, -1]					,						pt_output[:, -1].numpy()					,						4e-2   )
      @is_pt_flax_cross_test
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   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
         lowerCamelCase	:			Tuple           =   self._prepare_for_class(__magic_name__					,						__magic_name__   )
         lowerCamelCase	:			List[str]           =   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

         lowerCamelCase	:			Dict           =   pt_model_class(__magic_name__   ).eval()
         lowerCamelCase	:			Union[str, Any]           =   model_class(__magic_name__					,						dtype=jnp.floataa   )

         lowerCamelCase	:			Optional[int]           =   load_flax_weights_in_pytorch_model(__magic_name__					,						fx_model.params   )
         lowerCamelCase	:			List[Any]           =   pt_inputs["""input_ids"""].shape
         lowerCamelCase	:			List[Any]           =   np.random.randint(0					,						seq_length - 1					,						size=(batch_size,)   )
         for batch_idx, start_index in enumerate(__magic_name__   ):
          lowerCamelCase	:			str           =   0
          lowerCamelCase	:			int           =   1
          lowerCamelCase	:			Dict           =   0
          lowerCamelCase	:			Union[str, Any]           =   1

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

         with torch.no_grad():
          lowerCamelCase	:			Optional[Any]           =   pt_model(**__magic_name__   ).to_tuple()

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

         with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(__magic_name__   )
          lowerCamelCase	:			List[Any]           =   pt_model_class.from_pretrained(__magic_name__					,						from_flax=__magic_name__   )

         with torch.no_grad():
          lowerCamelCase	:			List[Any]           =   pt_model_loaded(**__magic_name__   ).to_tuple()

         self.assertEqual(
             len(__magic_name__   )					,						len(__magic_name__   )					,						"""Output lengths differ between Flax and PyTorch"""   )
         for fx_output, pt_output in zip(__magic_name__					,						__magic_name__   ):
          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:
        lowerCamelCase	:			Dict           =   model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""   )
        lowerCamelCase	:			Optional[Any]           =   model(np.ones((1, 1)   )   )
        self.assertIsNotNone(__magic_name__   )






from typing import Dict, List, Optional, Tuple

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


_lowerCamelCase           =logging.get_logger(__name__)





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Tuple					 =    ["""input_ids""", """attention_mask"""]
      def __init__(   self					,						__magic_name__="[PAD]"					,						__magic_name__="[BOS]"					,						__magic_name__="[EOS]"					,						__magic_name__="[MASK]"					,						__magic_name__="[CLS]"					,						__magic_name__="[SEP]"					,						__magic_name__=2_0_4_8					,						**__magic_name__					,						):
       lowerCamelCase	:			List[str]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else pad_token
       lowerCamelCase	:			Dict           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else bos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else eos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else mask_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else cls_token
       lowerCamelCase	:			Optional[Any]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else sep_token

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

       lowerCamelCase	:			List[Any]           =   2**8  # utf is 8 bits

       # define special tokens dict
       lowerCamelCase	:			Dict[str, int]           =   {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       lowerCamelCase	:			Optional[int]           =   len(self.special_tokens_encoder   )
       lowerCamelCase	:			Dict[int, str]           =   {v: k for k, v in self.special_tokens_encoder.items()}
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
        lowerCamelCase	:			List[Any]           =   chr(__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   i + len(self.special_tokens_encoder   )
       return vocab
      @property
      def 		UpperCamelCase__							(   self   ):
       return self._utf_vocab_size + self._num_special_tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None					,						__magic_name__ = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__					,						token_ids_a=__magic_name__					,						already_has_special_tokens=__magic_name__   )

       # normal case: some special tokens
       if token_ids_a is None:
        return [1] + [0] * len(__magic_name__   ) + [1]
       return [1] + ([0] * len(__magic_name__   )) + [1] + ([0] * len(__magic_name__   )) + [1]
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = 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					,						__magic_name__   ):
       lowerCamelCase	:			Optional[int]           =   [chr(__magic_name__   ) for i in text.encode("""utf-8"""   )]
       return tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if token in self.special_tokens_encoder:
        lowerCamelCase	:			List[str]           =   self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
        lowerCamelCase	:			List[Any]           =   self.added_tokens_encoder[token]
       elif len(__magic_name__   ) != 1:
        lowerCamelCase	:			Union[str, Any]           =   self.unk_token_id
       else:
        lowerCamelCase	:			int           =   ord(__magic_name__   ) + self._num_special_tokens
       return token_id
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if index in self.special_tokens_decoder:
        lowerCamelCase	:			Union[str, Any]           =   self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
        lowerCamelCase	:			List[str]           =   self.added_tokens_decoder[index]
       else:
        lowerCamelCase	:			Optional[int]           =   chr(index - self._num_special_tokens   )
       return token
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			List[Any]           =   b""""""
       for token in tokens:
        if token in self.special_tokens_decoder:
         lowerCamelCase	:			List[str]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.added_tokens_decoder:
         lowerCamelCase	:			Optional[int]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.special_tokens_encoder:
         lowerCamelCase	:			Tuple           =   token.encode("""utf-8"""   )
        elif token in self.added_tokens_encoder:
         lowerCamelCase	:			Union[str, Any]           =   token.encode("""utf-8"""   )
        else:
         lowerCamelCase	:			Dict           =   bytes([ord(__magic_name__   )]   )
        bstring += tok_string
       lowerCamelCase	:			Any           =   bstring.decode("""utf-8"""					,						errors="""replace"""   )
       return string






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




