





'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch







class __a			:





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Tuple=14					,lowerCamelCase					:      Any=7					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Dict=True					,lowerCamelCase					:      Optional[Any]=False					,lowerCamelCase					:      List[Any]=True					,lowerCamelCase					:      Any=99					,lowerCamelCase					:      str=32					,lowerCamelCase					:      Optional[Any]=4					,lowerCamelCase					:      Tuple=4					,lowerCamelCase					:      List[str]=4					,lowerCamelCase					:      str=37					,lowerCamelCase					:      Any="gelu"					,lowerCamelCase					:      str=0.1					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      int=512					,lowerCamelCase					:      Optional[int]=0.02					,):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

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

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

      __SCREAMING_SNAKE_CASE   				=							GPTJConfig(
          vocab_size=self.vocab_size					,n_embd=self.hidden_size					,n_layer=self.num_hidden_layers					,n_head=self.num_attention_heads					,n_positions=self.max_position_embeddings					,use_cache=A_					,bos_token_id=self.bos_token_id					,eos_token_id=self.eos_token_id					,pad_token_id=self.pad_token_id					,rotary_dim=self.rotary_dim					,)

      return (config, input_ids, input_mask)





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Any					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							20
      __SCREAMING_SNAKE_CASE   				=							model_class_name(A_     )

      __SCREAMING_SNAKE_CASE   				=							model.init_cache(input_ids.shape[0]					,A_     )
      __SCREAMING_SNAKE_CASE   				=							jnp.ones((input_ids.shape[0], max_decoder_length)					,dtype="""i4"""     )

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

      __SCREAMING_SNAKE_CASE   				=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,dtype="""i4"""     )
      __SCREAMING_SNAKE_CASE   				=							model(
          input_ids[:, -1:]					,attention_mask=A_					,past_key_values=outputs_cache.past_key_values					,position_ids=A_					,)

      __SCREAMING_SNAKE_CASE   				=							model(A_     )

      __SCREAMING_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					:      Optional[int]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							20
      __SCREAMING_SNAKE_CASE   				=							model_class_name(A_     )

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

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

      __SCREAMING_SNAKE_CASE   				=							model(
          input_ids[:, :-1]					,attention_mask=A_					,past_key_values=A_					,position_ids=A_					,)
      __SCREAMING_SNAKE_CASE   				=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,dtype="""i4"""     )
      __SCREAMING_SNAKE_CASE   				=							model(
          input_ids[:, -1:]					,past_key_values=outputs_cache.past_key_values					,attention_mask=A_					,position_ids=A_					,)

      __SCREAMING_SNAKE_CASE   				=							model(A_					,attention_mask=A_     )

      __SCREAMING_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 __a			( __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, unittest.TestCase      ):
   __UpperCamelCase :			Optional[int]						   =						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   __UpperCamelCase :			Tuple						   =						(FlaxGPTJForCausalLM,) if is_flax_available() else ()





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							FlaxGPTJModelTester(self     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(A_					,A_					,A_					,A_     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             A_					,A_					,A_					,A_     )





   @tooslow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""     )
      __SCREAMING_SNAKE_CASE   				=							False
      __SCREAMING_SNAKE_CASE   				=							model.config.eos_token_id

      __SCREAMING_SNAKE_CASE   				=							jax.jit(model.generate     )

      __SCREAMING_SNAKE_CASE   				=							jit_generate(
          inputs["""input_ids"""]					,attention_mask=inputs["""attention_mask"""]					,pad_token_id=tokenizer.pad_token_id     ).sequences

      __SCREAMING_SNAKE_CASE   				=							tokenizer.batch_decode(A_					,skip_special_tokens=A_     )

      __SCREAMING_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(A_					,A_     )





   @is_pt_flax_cross_test
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_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
            __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(A_					,A_     )
            __SCREAMING_SNAKE_CASE   				=							{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

            __SCREAMING_SNAKE_CASE   				=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,A_     )
            __SCREAMING_SNAKE_CASE   				=							fx_state

            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							pt_model(**A_     ).to_tuple()

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

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

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





   @is_pt_flax_cross_test
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_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
            __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(A_					,A_     )
            __SCREAMING_SNAKE_CASE   				=							{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

            __SCREAMING_SNAKE_CASE   				=							pt_model_class(A_     ).eval()
            __SCREAMING_SNAKE_CASE   				=							model_class(A_					,dtype=jnp.floataa     )

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

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

            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							pt_model(**A_     ).to_tuple()

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

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

            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							pt_model_loaded(**A_     ).to_tuple()

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





   @tooslow
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""     )
         __SCREAMING_SNAKE_CASE   				=							model(np.ones((1, 1)     )     )
         self.assertIsNotNone(A_     )












import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_A					:				Dict				=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	BartphoTokenizer
    lowerCamelCase__    :		Tuple				=	False
    lowerCamelCase__    :		Dict				=	True



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        SCREAMING_SNAKE_CASE__											=			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			{'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''monolingual_vocab_file''']     )
        with open(self.monolingual_vocab_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            for token in vocab_tokens:
                fp.write(f'''{token} {vocab_tokens[token]}\n'''     )

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        tokenizer.save_pretrained(self.tmpdirname     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return BartphoTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''This is a<unk><unk> test'''
        return input_text, output_text




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''▁This ▁is ▁a ▁l à ▁t est'''.split()
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )
