





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    _a  :

      """simple docstring"""


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


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






      def 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      np.clip(ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)					,  3					,  self.vocab_size			)

          UpperCamelCase__:					Any												=      None
          if self.use_input_mask:
              UpperCamelCase__:					Optional[Any]												=      random_attention_mask([self.batch_size, self.seq_length]			)

          UpperCamelCase__:					Union[str, Any]												=      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=__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 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      self.prepare_config_and_inputs()
          UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__:					Union[str, Any]												=      config_and_inputs
          UpperCamelCase__:					Tuple												=      {"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__:					str												=      self.prepare_config_and_inputs()

          UpperCamelCase__:					List[str]												=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
          UpperCamelCase__:					Optional[Any]												=      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]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	int			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      20
          UpperCamelCase__:					Optional[int]												=      model_class_name(__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      model.init_cache(input_ids.shape[0]					,  __lowerCamelCase			)
          UpperCamelCase__:					Any												=      jnp.ones((input_ids.shape[0], max_decoder_length)					,  dtype="i4"			)

          UpperCamelCase__:					List[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=__lowerCamelCase					,  past_key_values=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )

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

          UpperCamelCase__:					Optional[int]												=      model(__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      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:	Union[str, Any]					,  __lowerCamelCase:	Optional[int]					,  __lowerCamelCase:	Tuple					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      20
          UpperCamelCase__:					Optional[Any]												=      model_class_name(__lowerCamelCase			)

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

          UpperCamelCase__:					List[Any]												=      model.init_cache(input_ids.shape[0]					,  __lowerCamelCase			)
          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__:					Any												=      model(
              input_ids[:, :-1]					,  attention_mask=__lowerCamelCase					,  past_key_values=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )
          UpperCamelCase__:					Any												=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"			)
          UpperCamelCase__:					str												=      model(
              input_ids[:, -1:]					,  past_key_values=outputs_cache.past_key_values					,  attention_mask=__lowerCamelCase					,  position_ids=__lowerCamelCase					,  )

          UpperCamelCase__:					int												=      model(__lowerCamelCase					,  attention_mask=__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      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    _a  (  UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase):

      """simple docstring"""


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






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      FlaxXGLMModelTester(self			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__:					int												=      self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__:					List[Any]												=      self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)






      @slow
      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      XGLMTokenizer.from_pretrained("XGLM"					,  padding_side="left"			)
          UpperCamelCase__:					Optional[Any]												=      tokenizer(["Hello this is a long string", "Hey"]					,  return_tensors="np"					,  padding=__lowerCamelCase					,  truncation=__lowerCamelCase			)

          UpperCamelCase__:					List[Any]												=      FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)
          UpperCamelCase__:					Union[str, Any]												=      1
          UpperCamelCase__:					Union[str, Any]												=      False

          UpperCamelCase__:					Union[str, Any]												=      jax.jit(model.generate			)

          UpperCamelCase__:					int												=      jit_generate(inputs["input_ids"]					,  attention_mask=inputs["attention_mask"]			).sequences

          UpperCamelCase__:					str												=      tokenizer.batch_decode(__lowerCamelCase					,  skip_special_tokens=__lowerCamelCase			)

          UpperCamelCase__:					int												=      [
              "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(__lowerCamelCase					,  __lowerCamelCase			)






      @is_pt_flax_cross_test
      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					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
                  UpperCamelCase__:					Any												=      self._prepare_for_class(__lowerCamelCase					,  __lowerCamelCase			)
                  UpperCamelCase__:					Optional[int]												=      {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

                  with torch.no_grad():
                      UpperCamelCase__:					List[Any]												=      pt_model(**__lowerCamelCase			).to_tuple()

                  UpperCamelCase__:					Optional[int]												=      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			)
                      UpperCamelCase__:					Tuple												=      model_class.from_pretrained(__lowerCamelCase					,  from_pt=__lowerCamelCase			)

                  UpperCamelCase__:					int												=      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 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					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__:					Any												=      self._prepare_for_class(__lowerCamelCase					,  __lowerCamelCase			)
                  UpperCamelCase__:					Dict												=      {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

                  UpperCamelCase__:					Optional[Any]												=      pt_model_class(__lowerCamelCase			).eval()
                  UpperCamelCase__:					str												=      False
                  UpperCamelCase__:					Optional[int]												=      model_class(__lowerCamelCase					,  dtype=jnp.floataa			)

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

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

                  with torch.no_grad():
                      UpperCamelCase__:					Any												=      pt_model(**__lowerCamelCase			).to_tuple()

                  UpperCamelCase__:					List[Any]												=      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			)
                      UpperCamelCase__:					List[str]												=      pt_model_class.from_pretrained(__lowerCamelCase					,  from_flax=__lowerCamelCase			)

                  with torch.no_grad():
                      UpperCamelCase__:					Union[str, Any]												=      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 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              UpperCamelCase__:					Any												=      model_class_name.from_pretrained("facebook/xglm-564M"			)
              UpperCamelCase__:					Tuple												=      model(np.ones((1, 1)			)			)
              self.assertIsNotNone(__lowerCamelCase			)








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

