






"""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   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=14   ,    SCREAMING_SNAKE_CASE_=7   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=True   ,    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_=0.02   ,    SCREAMING_SNAKE_CASE_=None   ,    )    ->				int:
          UpperCamelCase	:List[Any]     							=       parent
          UpperCamelCase	:Dict     							=       batch_size
          UpperCamelCase	:str     							=       seq_length
          UpperCamelCase	:str     							=       is_training
          UpperCamelCase	:Dict     							=       use_input_mask
          UpperCamelCase	:List[str]     							=       use_labels
          UpperCamelCase	:List[str]     							=       vocab_size
          UpperCamelCase	:Dict     							=       d_model
          UpperCamelCase	:Union[str, Any]     							=       num_hidden_layers
          UpperCamelCase	:Any     							=       num_attention_heads
          UpperCamelCase	:Union[str, Any]     							=       ffn_dim
          UpperCamelCase	:int     							=       activation_function
          UpperCamelCase	:Dict     							=       activation_dropout
          UpperCamelCase	:Union[str, Any]     							=       attention_dropout
          UpperCamelCase	:Union[str, Any]     							=       max_position_embeddings
          UpperCamelCase	:List[Any]     							=       initializer_range
          UpperCamelCase	:Union[str, Any]     							=       None
          UpperCamelCase	:Dict     							=       0
          UpperCamelCase	:List[Any]     							=       2
          UpperCamelCase	:Optional[Any]     							=       1
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:Tuple     							=       np.clip(ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)   ,    3   ,    self.vocab_size			)

          UpperCamelCase	:Dict     							=       None
          if self.use_input_mask:
              UpperCamelCase	:str     							=       random_attention_mask([self.batch_size, self.seq_length]			)

          UpperCamelCase	:str     							=       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			)    ->				Optional[Any]:
          UpperCamelCase	:List[Any]     							=       self.prepare_config_and_inputs()
          UpperCamelCase	:Optional[int]     							=       config_and_inputs
          UpperCamelCase	:int     							=       {"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Any     							=       self.prepare_config_and_inputs()

          UpperCamelCase	:Dict     							=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
          UpperCamelCase	:Optional[int]     							=       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   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				str:
          UpperCamelCase	:Any     							=       20
          UpperCamelCase	:Optional[Any]     							=       model_class_name(__A			)

          UpperCamelCase	:List[Any]     							=       model.init_cache(input_ids.shape[0]   ,    __A			)
          UpperCamelCase	:Union[str, Any]     							=       jnp.ones((input_ids.shape[0], max_decoder_length)   ,    dtype='''i4'''			)

          UpperCamelCase	:Union[str, Any]     							=       jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)
          UpperCamelCase	:List[Any]     							=       model(
              input_ids[:, :-1]   ,    attention_mask=__A   ,    past_key_values=__A   ,    position_ids=__A   ,    )

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

          UpperCamelCase	:int     							=       model(__A			)

          UpperCamelCase	:List[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}'''			)




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          UpperCamelCase	:List[Any]     							=       20
          UpperCamelCase	:Optional[Any]     							=       model_class_name(__A			)

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

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

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

          UpperCamelCase	:Union[str, Any]     							=       model(__A   ,    attention_mask=__A			)
          UpperCamelCase	:Tuple     							=       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_						( lowercase,     lowercase,     unittest.TestCase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      UpperCamelCase_    : str									=(FlaxXGLMForCausalLM,) if is_flax_available() else ()
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:int     							=       FlaxXGLMModelTester(self			)
      def    UpperCAmelCase						(    self			)    ->				str:





          for model_class_name in self.all_model_classes:
              UpperCamelCase	:List[Any]     							=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__A   ,    __A   ,    __A   ,    __A			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:





          for model_class_name in self.all_model_classes:
              UpperCamelCase	:Optional[int]     							=       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			)    ->				str:
          UpperCamelCase	:Tuple     							=       XGLMTokenizer.from_pretrained('''XGLM'''   ,    padding_side='''left'''			)
          UpperCamelCase	:Any     							=       tokenizer(['''Hello this is a long string''', '''Hey''']   ,    return_tensors='''np'''   ,    padding=__A   ,    truncation=__A			)

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

          UpperCamelCase	:List[Any]     							=       jax.jit(model.generate			)

          UpperCamelCase	:str     							=       jit_generate(inputs['''input_ids''']   ,    attention_mask=inputs['''attention_mask''']			).sequences

          UpperCamelCase	:int     							=       tokenizer.batch_decode(__A   ,    skip_special_tokens=__A			)

          UpperCamelCase	: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(__A   ,    __A			)
      @is_pt_flax_cross_test
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:Tuple     							=       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
                  UpperCamelCase	:Union[str, Any]     							=       self._prepare_for_class(__A   ,    __A			)
                  UpperCamelCase	:Optional[int]     							=       {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

                  UpperCamelCase	:Union[str, Any]     							=       pt_inputs["input_ids"].shape
                  UpperCamelCase	:Optional[Any]     							=       np.random.randint(0   ,    seq_length - 1   ,    size=(batch_size,)			)
                  for batch_idx, start_index in enumerate(__A			):
                      UpperCamelCase	:Tuple     							=       0
                      UpperCamelCase	:List[Any]     							=       1
                      UpperCamelCase	:Dict     							=       0
                      UpperCamelCase	:Union[str, Any]     							=       1
                  UpperCamelCase	:int     							=       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.
                  UpperCamelCase	:Any     							=       False
                  UpperCamelCase	:Optional[Any]     							=       model_class(__A   ,    dtype=jnp.floataa			)
                  UpperCamelCase	:Union[str, Any]     							=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,    __A			)
                  UpperCamelCase	:List[Any]     							=       fx_state

                  with torch.no_grad():
                      UpperCamelCase	:Any     							=       pt_model(**__A			).to_tuple()

                  UpperCamelCase	:Tuple     							=       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			)
                      UpperCamelCase	:Union[str, Any]     							=       model_class.from_pretrained(__A   ,    from_pt=__A			)

                  UpperCamelCase	:Tuple     							=       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:
          UpperCamelCase	:Optional[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
                  UpperCamelCase	:Tuple     							=       self._prepare_for_class(__A   ,    __A			)
                  UpperCamelCase	:Tuple     							=       {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  UpperCamelCase	:Optional[Any]     							=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  UpperCamelCase	:Tuple     							=       getattr(__A   ,    __A			)

                  UpperCamelCase	:List[str]     							=       pt_model_class(__A			).eval()
                  UpperCamelCase	:Optional[Any]     							=       False
                  UpperCamelCase	:Optional[Any]     							=       model_class(__A   ,    dtype=jnp.floataa			)

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

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

                  with torch.no_grad():
                      UpperCamelCase	:Union[str, Any]     							=       pt_model(**__A			).to_tuple()

                  UpperCamelCase	:Tuple     							=       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			)
                      UpperCamelCase	:Union[str, Any]     							=       pt_model_class.from_pretrained(__A   ,    from_flax=__A			)

                  with torch.no_grad():
                      UpperCamelCase	:List[Any]     							=       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			)    ->				int:





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













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    if hue < 0 or hue > 360:
        raise Exception('''hue should be between 0 and 360'''	)

    if saturation < 0 or saturation > 1:
        raise Exception('''saturation should be between 0 and 1'''	)

    if value < 0 or value > 1:
        raise Exception('''value should be between 0 and 1'''	)

    UpperCamelCase	:Tuple     							=       value * saturation
    UpperCamelCase	:Any     							=       hue / 60
    UpperCamelCase	:Any     							=       chroma * (1 - abs(hue_section % 2 - 1	))
    UpperCamelCase	:Optional[int]     							=       value - chroma

    if hue_section >= 0 and hue_section <= 1:
        UpperCamelCase	:Any     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
    elif hue_section > 1 and hue_section <= 2:
        UpperCamelCase	:Dict     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:Dict     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
    elif hue_section > 2 and hue_section <= 3:
        UpperCamelCase	:Any     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
    elif hue_section > 3 and hue_section <= 4:
        UpperCamelCase	:Tuple     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (chroma + match_value)	)
    elif hue_section > 4 and hue_section <= 5:
        UpperCamelCase	:int     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (chroma + match_value)	)
    else:
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[int]     							=       round(255 * (second_largest_component + match_value)	)

    return [red, green, blue]


def   _A       (    SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int	):
    if red < 0 or red > 255:
        raise Exception('''red should be between 0 and 255'''	)

    if green < 0 or green > 255:
        raise Exception('''green should be between 0 and 255'''	)

    if blue < 0 or blue > 255:
        raise Exception('''blue should be between 0 and 255'''	)

    UpperCamelCase	:str     							=       red / 255
    UpperCamelCase	:List[str]     							=       green / 255
    UpperCamelCase	:int     							=       blue / 255
    UpperCamelCase	:Optional[int]     							=       max(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       value - min(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Dict     							=       0 if value == 0 else chroma / value

    if chroma == 0:
        UpperCamelCase	:Dict     							=       0.0
    elif value == float_red:
        UpperCamelCase	:Optional[Any]     							=       60 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        UpperCamelCase	:List[str]     							=       60 * (2 + (float_blue - float_red) / chroma)
    else:
        UpperCamelCase	:str     							=       60 * (4 + (float_red - float_green) / chroma)

    UpperCamelCase	:int     							=       (hue + 360) % 360

    return [hue, saturation, value]



def   _A       (    SCREAMING_SNAKE_CASE__  :     list[float]					,  SCREAMING_SNAKE_CASE__  :     list[float]	):
    UpperCamelCase	:str     							=       abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCamelCase	:List[str]     							=       abs(hsv_a[1] - hsv_a[1]	) < 0.0_02
    UpperCamelCase	:Any     							=       abs(hsv_a[2] - hsv_a[2]	) < 0.0_02

    return check_hue and check_saturation and check_value





