
"""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      ,	__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=0.0_2      ,	__UpperCAmelCase=None      ,	):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

      __UpperCamelCase							    =			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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			self.prepare_config_and_inputs()

      __UpperCamelCase							    =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      __UpperCamelCase							    =			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      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			20
      __UpperCamelCase							    =			model_class_name(__UpperCAmelCase )

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

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

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

      __UpperCamelCase							    =			model(__UpperCAmelCase )

      __UpperCamelCase							    =			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      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			20
      __UpperCamelCase							    =			model_class_name(__UpperCAmelCase )

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

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

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

      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	attention_mask=__UpperCAmelCase )
      __UpperCamelCase							    =			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						( __A     ,    __A     ,    unittest.TestCase     ):
     lowercase		  =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     lowercase		  =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			FlaxXGLMModelTester(self )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for model_class_name in self.all_model_classes:
       __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for model_class_name in self.all_model_classes:
       __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )





     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

      __UpperCamelCase							    =			jax.jit(model.generate )

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

      __UpperCamelCase							    =			tokenizer.batch_decode(__UpperCAmelCase      ,	skip_special_tokens=__UpperCAmelCase )

      __UpperCamelCase							    =			[
          '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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

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

        with torch.no_grad():
         __UpperCamelCase							    =			pt_model(**__UpperCAmelCase ).to_tuple()

        __UpperCamelCase							    =			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 )
         __UpperCamelCase							    =			model_class.from_pretrained(__UpperCAmelCase      ,	from_pt=__UpperCAmelCase )

        __UpperCamelCase							    =			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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

        __UpperCamelCase							    =			pt_model_class(__UpperCAmelCase ).eval()
        __UpperCamelCase							    =			False
        __UpperCamelCase							    =			model_class(__UpperCAmelCase      ,	dtype=jnp.floataa )

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

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

        with torch.no_grad():
         __UpperCamelCase							    =			pt_model(**__UpperCAmelCase ).to_tuple()

        __UpperCamelCase							    =			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 )
         __UpperCamelCase							    =			pt_model_class.from_pretrained(__UpperCAmelCase      ,	from_flax=__UpperCAmelCase )

        with torch.no_grad():
         __UpperCamelCase							    =			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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





