

"""simple docstring"""
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
 import numpy as np

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
 import torch

@require_flax
class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: int       ,       snake_case					: List[Any]       ,       snake_case					: Union[str, Any]=14       ,       snake_case					: Optional[Any]=7       ,       snake_case					: Dict=True       ,       snake_case					: Any=True       ,       snake_case					: Dict=True       ,       snake_case					: Tuple=99       ,       snake_case					: Tuple=32       ,       snake_case					: str=5       ,       snake_case					: Optional[int]=4       ,       snake_case					: Union[str, Any]=37       ,       snake_case					: List[Any]="gelu"       ,       snake_case					: Optional[Any]=0.1       ,       snake_case					: int=0.1       ,       snake_case					: List[str]=512       ,       snake_case					: str=0.02       ,       snake_case					: Tuple=None       ,       )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     parent
         __magic_name__			:		Optional[Any]        =     batch_size
         __magic_name__			:		Tuple        =     seq_length
         __magic_name__			:		Optional[Any]        =     is_training
         __magic_name__			:		Union[str, Any]        =     use_input_mask
         __magic_name__			:		int        =     use_labels
         __magic_name__			:		List[str]        =     vocab_size
         __magic_name__			:		List[Any]        =     d_model
         __magic_name__			:		List[Any]        =     num_hidden_layers
         __magic_name__			:		int        =     num_attention_heads
         __magic_name__			:		Optional[Any]        =     ffn_dim
         __magic_name__			:		str        =     activation_function
         __magic_name__			:		Any        =     activation_dropout
         __magic_name__			:		List[str]        =     attention_dropout
         __magic_name__			:		str        =     max_position_embeddings
         __magic_name__			:		Dict        =     initializer_range
         __magic_name__			:		str        =     None
         __magic_name__			:		Any        =     0
         __magic_name__			:		Tuple        =     2
         __magic_name__			:		Dict        =     1



  def 							_UpperCAmelCase      ( self					: Any      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     np.clip(ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size      )       ,       3       ,       self.vocab_size      )

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

         __magic_name__			:		Tuple        =     XGLMConfig(
             vocab_size=self.vocab_size       ,       d_model=self.hidden_size       ,       num_layers=self.num_hidden_layers       ,       attention_heads=self.num_attention_heads       ,       ffn_dim=self.ffn_dim       ,       activation_function=self.activation_function       ,       activation_dropout=self.activation_dropout       ,       attention_dropout=self.attention_dropout       ,       max_position_embeddings=self.max_position_embeddings       ,       initializer_range=self.initializer_range       ,       use_cache=lowercase_       ,       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 							_UpperCAmelCase      ( self					: int      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Dict        =     self.prepare_config_and_inputs()
         __magic_name__			:		Any        =     config_and_inputs
         __magic_name__			:		int        =     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
         return config, inputs_dict



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		str        =     self.prepare_config_and_inputs()

         __magic_name__			:		Union[str, Any]        =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
         __magic_name__			:		List[str]        =     ids_tensor([self.batch_size, self.seq_length]       ,       vocab_size=2      )

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



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: List[Any]       ,       snake_case					: Optional[int]       ,       snake_case					: str       ,       snake_case					: Tuple      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		str        =     20
         __magic_name__			:		List[str]        =     model_class_name(lowercase_      )

         __magic_name__			:		Any        =     model.init_cache(input_ids.shape[0]       ,       lowercase_      )
         __magic_name__			:		Optional[int]        =     jnp.ones((input_ids.shape[0], max_decoder_length)       ,       dtype='''i4'''      )

         __magic_name__			:		List[str]        =     jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1      )[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1)      )
         __magic_name__			:		Optional[int]        =     model(
             input_ids[:, :-1]       ,       attention_mask=lowercase_       ,       past_key_values=lowercase_       ,       position_ids=lowercase_       ,       )

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

         __magic_name__			:		List[str]        =     model(lowercase_      )

         __magic_name__			:		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}"""      )




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: Optional[Any]       ,       snake_case					: Union[str, Any]       ,       snake_case					: int       ,       snake_case					: List[Any]      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     20
         __magic_name__			:		List[Any]        =     model_class_name(lowercase_      )

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

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

         __magic_name__			:		Optional[int]        =     model(
             input_ids[:, :-1]       ,       attention_mask=lowercase_       ,       past_key_values=lowercase_       ,       position_ids=lowercase_       ,       )
         __magic_name__			:		Optional[int]        =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,       dtype='''i4'''      )
         __magic_name__			:		List[Any]        =     model(
             input_ids[:, -1:]       ,       past_key_values=outputs_cache.past_key_values       ,       attention_mask=lowercase_       ,       position_ids=lowercase_       ,       )

         __magic_name__			:		Union[str, Any]        =     model(lowercase_       ,       attention_mask=lowercase_      )
         __magic_name__			:		str        =     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_sentencepiece
@require_flax
class        _UpperCamelCase	(						snake_case_   ,				snake_case_   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  snake_case_        =    (FlaxXGLMForCausalLM,) if is_flax_available() else ()



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Any        =     FlaxXGLMModelTester(self      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			Tuple:


         '''simple docstring'''





         for model_class_name in self.all_model_classes:
                __magic_name__			:		Optional[int]        =     self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(lowercase_       ,       lowercase_       ,       lowercase_       ,       lowercase_      )



  def 							_UpperCAmelCase      ( self					: Any      )	->			Tuple:


         '''simple docstring'''





         for model_class_name in self.all_model_classes:
                __magic_name__			:		Optional[int]        =     self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    lowercase_       ,       lowercase_       ,       lowercase_       ,       lowercase_      )



  @slow
  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     XGLMTokenizer.from_pretrained('''XGLM'''       ,       padding_side='''left'''      )
         __magic_name__			:		int        =     tokenizer(['''Hello this is a long string''', '''Hey''']       ,       return_tensors='''np'''       ,       padding=lowercase_       ,       truncation=lowercase_      )

         __magic_name__			:		Optional[Any]        =     FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )
         __magic_name__			:		Union[str, Any]        =     1
         __magic_name__			:		Optional[Any]        =     False

         __magic_name__			:		int        =     jax.jit(model.generate      )

         __magic_name__			:		Optional[Any]        =     jit_generate(inputs['''input_ids''']       ,       attention_mask=inputs['''attention_mask''']      ).sequences

         __magic_name__			:		List[str]        =     tokenizer.batch_decode(lowercase_       ,       skip_special_tokens=lowercase_      )

         __magic_name__			:		Optional[Any]        =     [
             """Hello this is a long string of questions, but I'm not sure if I'm""",
             """Hey, I'm a newbie to the forum and I'""",
         ]

         self.assertListEqual(lowercase_       ,       lowercase_      )



  @is_pt_flax_cross_test
  def 							_UpperCAmelCase      ( self					: Any      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Any        =     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
                       __magic_name__			:		Union[str, Any]        =     self._prepare_for_class(lowercase_       ,       lowercase_      )
                       __magic_name__			:		List[Any]        =     {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

                       __magic_name__			:		Dict        =     pt_inputs["""input_ids"""].shape
                       __magic_name__			:		Tuple        =     np.random.randint(0       ,       seq_length - 1       ,       size=(batch_size,)      )
                       for batch_idx, start_index in enumerate(lowercase_      ):
                              __magic_name__			:		List[str]        =     0
                              __magic_name__			:		List[Any]        =     1
                              __magic_name__			:		str        =     0
                              __magic_name__			:		Union[str, Any]        =     1
                       __magic_name__			:		List[Any]        =     pt_model_class(lowercase_      ).eval()
                       # Flax models don't use the `use_cache` option and cache is not returned as a default.
                       # So we disable `use_cache` here for PyTorch model.
                       __magic_name__			:		Optional[int]        =     False
                       __magic_name__			:		Optional[Any]        =     model_class(lowercase_       ,       dtype=jnp.floataa      )
                       __magic_name__			:		Union[str, Any]        =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,       lowercase_      )
                       __magic_name__			:		List[str]        =     fx_state

                       with torch.no_grad():
                              __magic_name__			:		Dict        =     pt_model(**lowercase_      ).to_tuple()

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

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

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



  @is_pt_flax_cross_test
  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Dict        =     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
                       __magic_name__			:		List[Any]        =     self._prepare_for_class(lowercase_       ,       lowercase_      )
                       __magic_name__			:		Tuple        =     {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

                       __magic_name__			:		Optional[Any]        =     pt_model_class(lowercase_      ).eval()
                       __magic_name__			:		Any        =     False
                       __magic_name__			:		Optional[int]        =     model_class(lowercase_       ,       dtype=jnp.floataa      )

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

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

                       with torch.no_grad():
                              __magic_name__			:		Tuple        =     pt_model(**lowercase_      ).to_tuple()

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

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

                       with torch.no_grad():
                              __magic_name__			:		Tuple        =     pt_model_loaded(**lowercase_      ).to_tuple()

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




  @slow
  def 							_UpperCAmelCase      ( self					: str      )	->			str:


         '''simple docstring'''





         for model_class_name in self.all_model_classes:
                __magic_name__			:		Optional[Any]        =     model_class_name.from_pretrained('''facebook/xglm-564M'''      )
                __magic_name__			:		int        =     model(np.ones((1, 1)      )      )
                self.assertIsNotNone(lowercase_      )





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase					:				Union[str, Any]   	=2 << 20  # 2mb
lowerCAmelCase					:				Union[str, Any]   	=get_logger(__name__)






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					self._options.get_all_options().get("""runner""" )
									return runner in [None, "DirectRunner", "PortableRunner"]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Union[str, Any]					     =					FileSystems
						if fs.exists(SCREAMING_SNAKE_CASE__  ):
												if force_upload:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.create(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""rb"""  ) as local_file:
																		lowerCAmelCase	:   Any					     =					local_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								remote_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   List[str]					     =					local_file.read(SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					FileSystems
						if os.path.exists(SCREAMING_SNAKE_CASE__  ):
												if force_download:
																		logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.open(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""wb"""  ) as local_file:
																		lowerCAmelCase	:   Optional[int]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								local_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   Union[str, Any]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )

