




'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
   import torch

class      _a       :

     '''simple docstring'''





     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=14      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=False      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=0.0_2      ,)							->    List[str]:
          _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							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   initializer_range
          _snake_case							=   None
          _snake_case							=   vocab_size - 1
          _snake_case							=   vocab_size - 1
          _snake_case							=   vocab_size - 1






     def        _lowercase			(				self					)							->    List[str]:
          _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							=   GPTaConfig(
              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=_lowerCamelCase      ,bos_token_id=self.bos_token_id      ,eos_token_id=self.eos_token_id      ,pad_token_id=self.pad_token_id      ,)

          return (config, input_ids, input_mask)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							=   config_and_inputs
          _snake_case							=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()

          _snake_case							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,vocab_size=2					)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Dict:
          _snake_case							=   20
          _snake_case							=   model_class_name(_lowerCamelCase					)

          _snake_case							=   model.init_cache(input_ids.shape[0]      ,_lowerCamelCase					)
          _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=_lowerCamelCase      ,past_key_values=_lowerCamelCase      ,position_ids=_lowerCamelCase      ,)

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

          _snake_case							=   model(_lowerCamelCase					)

          _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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    List[Any]:
          _snake_case							=   20
          _snake_case							=   model_class_name(_lowerCamelCase					)

          _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]      ,_lowerCamelCase					)
          _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=_lowerCamelCase      ,past_key_values=_lowerCamelCase      ,position_ids=_lowerCamelCase      ,)
          _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=_lowerCamelCase      ,position_ids=_lowerCamelCase      ,)

          _snake_case							=   model(_lowerCamelCase      ,attention_mask=_lowerCamelCase					)

          _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       (		lowerCAmelCase__      ,      lowerCAmelCase__      ,      unittest.TestCase							):

     '''simple docstring'''





     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		(FlaxGPTaLMHeadModel,) if is_flax_available() else ()






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   FlaxGPTaModelTester(self					)






     def        _lowercase			(				self					)							->    int:

          for model_class_name in self.all_model_classes:
               _snake_case							=   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase					)






     def        _lowercase			(				self					)							->    Dict:

          for model_class_name in self.all_model_classes:
               _snake_case							=   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase					)






     @slow
     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   GPTaTokenizer.from_pretrained("gpt2"      ,pad_token="</s>"      ,padding_side="left"					)
          _snake_case							=   tokenizer(["Hello this is a long string", "Hey"]      ,return_tensors="np"      ,padding=_lowerCamelCase      ,truncation=_lowerCamelCase					)

          _snake_case							=   FlaxGPTaLMHeadModel.from_pretrained("gpt2"					)
          _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"]					).sequences

          _snake_case							=   tokenizer.batch_decode(_lowerCamelCase      ,skip_special_tokens=_lowerCamelCase					)

          _snake_case							=   [
              """Hello this is a long string of words. I'm going to start with the first one.\n""",
              """Hey, I'm not sure if I'm going to be able to do""",
          ]

          self.assertListEqual(_lowerCamelCase      ,_lowerCamelCase					)






     @is_pt_flax_cross_test
     def        _lowercase			(				self					)							->    Any:
          _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(_lowerCamelCase      ,_lowerCamelCase					)
                    _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(_lowerCamelCase      ,_lowerCamelCase					)

                    _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(_lowerCamelCase					):
                         _snake_case							=   0
                         _snake_case							=   1
                         _snake_case							=   0
                         _snake_case							=   1
                    _snake_case							=   pt_model_class(_lowerCamelCase					).eval()
                    _snake_case							=   model_class(_lowerCamelCase      ,dtype=jnp.floataa					)

                    _snake_case							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,_lowerCamelCase					)
                    _snake_case							=   fx_state

                    with torch.no_grad():
                         _snake_case							=   pt_model(**_lowerCamelCase					).to_tuple()

                    _snake_case							=   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					)
                         _snake_case							=   model_class.from_pretrained(_lowerCamelCase      ,from_pt=_lowerCamelCase					)

                    _snake_case							=   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        _lowercase			(				self					)							->    str:
          _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(_lowerCamelCase      ,_lowerCamelCase					)
                    _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(_lowerCamelCase      ,_lowerCamelCase					)

                    _snake_case							=   pt_model_class(_lowerCamelCase					).eval()
                    _snake_case							=   model_class(_lowerCamelCase      ,dtype=jnp.floataa					)

                    _snake_case							=   load_flax_weights_in_pytorch_model(_lowerCamelCase      ,fx_model.params					)
                    _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(_lowerCamelCase					):
                         _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(**_lowerCamelCase					).to_tuple()

                    _snake_case							=   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					)
                         _snake_case							=   pt_model_class.from_pretrained(_lowerCamelCase      ,from_flax=_lowerCamelCase					)

                    with torch.no_grad():
                         _snake_case							=   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        _lowercase			(				self					)							->    List[Any]:

          for model_class_name in self.all_model_classes:
               _snake_case							=   model_class_name.from_pretrained("gpt2"      ,from_pt=_lowerCamelCase					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_lowerCamelCase					)








'''simple docstring'''





import io
import json
import subprocess


UpperCamelCase_		:    Any									=		[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase_		:    Union[str, Any]									=		8





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case							=   {}
     for src, tgt in pairs:
          _snake_case							=   F"""{src}-{tgt}"""
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
     return text


UpperCamelCase_		:    Tuple									=		get_all_data(pairs, n_objs)
UpperCamelCase_		:    Optional[Any]									=		'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   UpperCamelCase_		:    List[str]									=		json.dump(text, f, indent=2, ensure_ascii=False)


