




'''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       lowerCAmelCase				:


       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:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




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

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

            __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					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  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case   ,      __snake_case   ,      __snake_case         =					self.prepare_config_and_inputs()

            __snake_case         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            __snake_case         =					ids_tensor([self.batch_size, self.seq_length]      ,					vocab_size=2					)

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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					20
            __snake_case         =					model_class_name(__SCREAMING_SNAKE_CASE					)

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

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

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

            __snake_case         =					model(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




            __snake_case         =					20
            __snake_case         =					model_class_name(__SCREAMING_SNAKE_CASE					)

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

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

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

            __snake_case         =					model(__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE					)
            __snake_case         =					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       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: str	    =     (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       __lowercase					: List[str]	    =     (FlaxXGLMForCausalLM,) if is_flax_available() else ()


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					FlaxXGLMModelTester(self					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case   ,      __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case   ,      __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     __SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       @slow
       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




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

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

            __snake_case         =					jax.jit(model.generate					)

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

            __snake_case         =					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE      ,					skip_special_tokens=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					[
                '''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(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       @is_pt_flax_cross_test
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




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

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

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

                      with torch.no_grad():
                           __snake_case         =					pt_model(**__SCREAMING_SNAKE_CASE					).to_tuple()

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

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

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


       @is_pt_flax_cross_test
       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




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

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

                      __snake_case         =					pt_model_class(__SCREAMING_SNAKE_CASE					).eval()
                      __snake_case         =					False
                      __snake_case         =					model_class(__SCREAMING_SNAKE_CASE      ,					dtype=jnp.floataa					)

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

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

                      with torch.no_grad():
                           __snake_case         =					pt_model(**__SCREAMING_SNAKE_CASE					).to_tuple()

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

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

                      with torch.no_grad():
                           __snake_case         =					pt_model_loaded(**__SCREAMING_SNAKE_CASE					).to_tuple()

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




       @slow
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




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












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






