




'''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   _snake_case:






 def __init__(self					:     Dict						,		a					:     str						,		a					:     Dict=14						,		a					:     Optional[int]=7						,		a					:     str=True						,		a					:     int=True						,		a					:     str=True						,		a					:     List[str]=99						,		a					:     List[str]=32						,		a					:     int=5						,		a					:     Optional[int]=4						,		a					:     Any=37						,		a					:     Dict="gelu"						,		a					:     Any=0.1						,		a					:     List[Any]=0.1						,		a					:     List[str]=5_12						,		a					:     Optional[int]=0.02						,		a					:     str=None						,		)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    parent
    A__         =    batch_size
    A__         =    seq_length
    A__         =    is_training
    A__         =    use_input_mask
    A__         =    use_labels
    A__         =    vocab_size
    A__         =    d_model
    A__         =    num_hidden_layers
    A__         =    num_attention_heads
    A__         =    ffn_dim
    A__         =    activation_function
    A__         =    activation_dropout
    A__         =    attention_dropout
    A__         =    max_position_embeddings
    A__         =    initializer_range
    A__         =    None
    A__         =    0
    A__         =    2
    A__         =    1






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






    """simple docstring"""


    A__         =    np.clip(ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size						)						,		3						,		self.vocab_size						)

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

    A__         =    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=a						,		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					:     List[Any]						)					->      Optional[Any]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Tuple:






    """simple docstring"""


    A__     , A__     , A__         =    self.prepare_config_and_inputs()

    A__         =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
    A__         =    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					:     List[str]						,		a					:     Optional[int]						,		a					:     int						,		a					:     Optional[Any]						,		a					:     Tuple						)					->      Tuple:






    """simple docstring"""


    A__         =    20
    A__         =    model_class_name(a						)

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

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

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

    A__         =    model(a						)

    A__         =    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					:     Dict						,		a					:     Dict						,		a					:     List[str]						,		a					:     str						,		a					:     Optional[Any]						)					->      Tuple:






    """simple docstring"""


    A__         =    20
    A__         =    model_class_name(a						)

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

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

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

    A__         =    model(a						,		attention_mask=a						)
    A__         =    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   _snake_case( UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       Any									=				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 __snake_case:       List[str]									=				(FlaxXGLMForCausalLM,) if is_flax_available() else ()






 def 	_UpperCamelCase   (self					:     str						)					->      int:






    """simple docstring"""


    A__         =    FlaxXGLMModelTester(self						)






 def 	_UpperCamelCase   (self					:     int						)					->      Union[str, Any]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__     , A__     , A__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(a						,		a						,		a						,		a						)






 def 	_UpperCamelCase   (self					:     List[str]						)					->      int:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__     , A__     , A__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           a						,		a						,		a						,		a						)






 @slow
 def 	_UpperCamelCase   (self					:     Union[str, Any]						)					->      Optional[int]:






    """simple docstring"""


    A__         =    XGLMTokenizer.from_pretrained('XGLM'						,		padding_side='left'						)
    A__         =    tokenizer(['Hello this is a long string', 'Hey']						,		return_tensors='np'						,		padding=a						,		truncation=a						)

    A__         =    FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'						)
    A__         =    1
    A__         =    False

    A__         =    jax.jit(model.generate						)

    A__         =    jit_generate(inputs['input_ids']						,		attention_mask=inputs['attention_mask']						).sequences

    A__         =    tokenizer.batch_decode(a						,		skip_special_tokens=a						)

    A__         =    [
        '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(a						,		a						)






 @is_pt_flax_cross_test
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      List[str]:






    """simple docstring"""


    A__     , A__         =    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
          A__         =    self._prepare_for_class(a						,		a						)
          A__         =    {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

          A__     , A__         =    pt_inputs['input_ids'].shape
          A__         =    np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)						)
          for batch_idx, start_index in enumerate(a						):
             A__         =    0
             A__         =    1
             A__         =    0
             A__         =    1
          A__         =    pt_model_class(a						).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.
          A__         =    False
          A__         =    model_class(a						,		dtype=jnp.floataa						)
          A__         =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		a						)
          A__         =    fx_state

          with torch.no_grad():
             A__         =    pt_model(**a						).to_tuple()

          A__         =    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						)
             A__         =    model_class.from_pretrained(a						,		from_pt=a						)

          A__         =    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					:     Dict						)					->      Any:






    """simple docstring"""


    A__     , A__         =    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
          A__         =    self._prepare_for_class(a						,		a						)
          A__         =    {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

          A__         =    pt_model_class(a						).eval()
          A__         =    False
          A__         =    model_class(a						,		dtype=jnp.floataa						)

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

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

          with torch.no_grad():
             A__         =    pt_model(**a						).to_tuple()

          A__         =    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						)
             A__         =    pt_model_class.from_pretrained(a						,		from_flax=a						)

          with torch.no_grad():
             A__         =    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						)






 @slow
 def 	_UpperCamelCase   (self					:     List[str]						)					->      Union[str, Any]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__         =    model_class_name.from_pretrained('facebook/xglm-564M'						)
       A__         =    model(np.ones((1, 1)						)						)
       self.assertIsNotNone(a						)










'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowerCAmelCase_    	=							TypeVar('''T''', bound='''Enum''')

@unique
class   _snake_case( UpperCAmelCase			):
 __snake_case:       List[Any]									=				80
 __snake_case:       Optional[int]									=				70
 __snake_case:       Optional[int]									=				60
 __snake_case:       int									=				50
 __snake_case:       Dict									=				40
 __snake_case:       Dict									=				30
 __snake_case:       str									=				20
 __snake_case:       Optional[int]									=				10




@unique
class   _snake_case( UpperCAmelCase			):
 __snake_case:       List[Any]									=				24
 __snake_case:       Union[str, Any]									=				21
 __snake_case:       Dict									=				18
 __snake_case:       List[str]									=				15
 __snake_case:       Union[str, Any]									=				12
 __snake_case:       Tuple									=				9
 __snake_case:       int									=				6
 __snake_case:       Tuple									=				3
 __snake_case:       List[Any]									=				2
 __snake_case:       int									=				1
 __snake_case:       List[str]									=				-1
 __snake_case:       Union[str, Any]									=				-2
 __snake_case:       List[str]									=				-3
 __snake_case:       Any									=				-6
 __snake_case:       Tuple									=				-9
 __snake_case:       List[str]									=				-12
 __snake_case:       Dict									=				-15
 __snake_case:       Optional[int]									=				-18
 __snake_case:       str									=				-21
 __snake_case:       Any									=				-24






 @classmethod
 def 	_UpperCamelCase   (cls					:     type[T]						)					->      dict:






    """simple docstring"""


    return {unit.name: unit.value for unit in cls if unit.value > 0}






 @classmethod
 def 	_UpperCamelCase   (cls					:     type[T]						)					->      dict:






    """simple docstring"""


    return {unit.name: unit.value for unit in cls if unit.value < 0}







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
   for name_prefix, value_prefix in prefixes.items():
      A__         =    value / (10**value_prefix)
      if numerical_part > 1:
         return F"""{numerical_part!s} {name_prefix}"""
   return str(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   for prefix in BinaryUnit:
      A__         =    value / (2**prefix.value)
      if numerical_part > 1:
         return F"""{numerical_part!s} {prefix.name}"""
   return str(UpperCAmelCase	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


