
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					,					_UpperCAmelCase					,					_UpperCAmelCase=14					,					_UpperCAmelCase=7					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase=99					,					_UpperCAmelCase=32					,					_UpperCAmelCase=5					,					_UpperCAmelCase=4					,					_UpperCAmelCase=37					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=512					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=None					,					):
            lowerCAmelCase_						=      parent
            lowerCAmelCase_						=      batch_size
            lowerCAmelCase_						=      seq_length
            lowerCAmelCase_						=      is_training
            lowerCAmelCase_						=      use_input_mask
            lowerCAmelCase_						=      use_labels
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      d_model
            lowerCAmelCase_						=      num_hidden_layers
            lowerCAmelCase_						=      num_attention_heads
            lowerCAmelCase_						=      ffn_dim
            lowerCAmelCase_						=      activation_function
            lowerCAmelCase_						=      activation_dropout
            lowerCAmelCase_						=      attention_dropout
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      initializer_range
            lowerCAmelCase_						=      None
            lowerCAmelCase_						=      0
            lowerCAmelCase_						=      2
            lowerCAmelCase_						=      1







      def lowercase__		( self):
            lowerCAmelCase_						=      np.clip(ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size)					,					3					,					self.vocab_size)

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

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







      def lowercase__		( self):
            lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      self.prepare_config_and_inputs()

            lowerCAmelCase_						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
            lowerCAmelCase_						=      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					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      20
            lowerCAmelCase_						=      model_class_name(_UpperCAmelCase)

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

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

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

            lowerCAmelCase_						=      model(_UpperCAmelCase)

            lowerCAmelCase_						=      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					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      20
            lowerCAmelCase_						=      model_class_name(_UpperCAmelCase)

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

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

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

            lowerCAmelCase_						=      model(_UpperCAmelCase					,					attention_mask=_UpperCAmelCase)
            lowerCAmelCase_						=      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_				(  A					,       A					,       unittest.TestCase     ):




      '''simple docstring'''







      a		:Optional[Any]						 =      (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      a		:Optional[int]						 =      (FlaxXGLMForCausalLM,) if is_flax_available() else ()







      def lowercase__		( self):
            lowerCAmelCase_						=      FlaxXGLMModelTester(self)







      def lowercase__		( self):
            for model_class_name in self.all_model_classes:
                  lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self):
            for model_class_name in self.all_model_classes:
                  lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      @slow
      def lowercase__		( self):
            lowerCAmelCase_						=      XGLMTokenizer.from_pretrained('''XGLM'''					,					padding_side='''left''')
            lowerCAmelCase_						=      tokenizer(['''Hello this is a long string''', '''Hey''']					,					return_tensors='''np'''					,					padding=_UpperCAmelCase					,					truncation=_UpperCAmelCase)

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

            lowerCAmelCase_						=      jax.jit(model.generate)

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

            lowerCAmelCase_						=      tokenizer.batch_decode(_UpperCAmelCase					,					skip_special_tokens=_UpperCAmelCase)

            lowerCAmelCase_						=      [
                '''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(_UpperCAmelCase					,					_UpperCAmelCase)







      @is_pt_flax_cross_test
      def lowercase__		( self):
            lowerCAmelCase_			,		lowerCAmelCase_						=      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
                        lowerCAmelCase_						=      self._prepare_for_class(_UpperCAmelCase					,					_UpperCAmelCase)
                        lowerCAmelCase_						=      {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                        with torch.no_grad():
                              lowerCAmelCase_						=      pt_model(**_UpperCAmelCase).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                              pt_model.save_pretrained(_UpperCAmelCase)
                              lowerCAmelCase_						=      model_class.from_pretrained(_UpperCAmelCase					,					from_pt=_UpperCAmelCase)

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







      @is_pt_flax_cross_test
      def lowercase__		( self):
            lowerCAmelCase_			,		lowerCAmelCase_						=      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
                        lowerCAmelCase_						=      self._prepare_for_class(_UpperCAmelCase					,					_UpperCAmelCase)
                        lowerCAmelCase_						=      {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                        lowerCAmelCase_						=      pt_model_class(_UpperCAmelCase).eval()
                        lowerCAmelCase_						=      False
                        lowerCAmelCase_						=      model_class(_UpperCAmelCase					,					dtype=jnp.floataa)

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

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

                        with torch.no_grad():
                              lowerCAmelCase_						=      pt_model(**_UpperCAmelCase).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                              fx_model.save_pretrained(_UpperCAmelCase)
                              lowerCAmelCase_						=      pt_model_class.from_pretrained(_UpperCAmelCase					,					from_flax=_UpperCAmelCase)

                        with torch.no_grad():
                              lowerCAmelCase_						=      pt_model_loaded(**_UpperCAmelCase).to_tuple()

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







      @slow
      def lowercase__		( self):
            for model_class_name in self.all_model_classes:
                  lowerCAmelCase_						=      model_class_name.from_pretrained('''facebook/xglm-564M''')
                  lowerCAmelCase_						=      model(np.ones((1, 1)))
                  self.assertIsNotNone(_UpperCAmelCase)





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



