





"""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       :


     def __init__( self      :Union[str, Any]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :int=14					,	__UpperCamelCase      :Optional[int]=7					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :Optional[int]=True					,	__UpperCamelCase      :List[str]=True					,	__UpperCamelCase      :Any=99					,	__UpperCamelCase      :Any=32					,	__UpperCamelCase      :str=5					,	__UpperCamelCase      :Union[str, Any]=4					,	__UpperCamelCase      :Optional[int]=37					,	__UpperCamelCase      :Dict="gelu"					,	__UpperCamelCase      :str=0.1					,	__UpperCamelCase      :List[Any]=0.1					,	__UpperCamelCase      :List[Any]=5_12					,	__UpperCamelCase      :int=0.02					,	__UpperCamelCase      :Dict=None					,	):
            A		=  parent
            A		=  batch_size
            A		=  seq_length
            A		=  is_training
            A		=  use_input_mask
            A		=  use_labels
            A		=  vocab_size
            A		=  d_model
            A		=  num_hidden_layers
            A		=  num_attention_heads
            A		=  ffn_dim
            A		=  activation_function
            A		=  activation_dropout
            A		=  attention_dropout
            A		=  max_position_embeddings
            A		=  initializer_range
            A		=  None
            A		=  0
            A		=  2
            A		=  1


     def      lowerCamelCase	( self      :Any		):
            A		=  np.clip(ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)					,	3					,	self.vocab_size		)

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

            A		=  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      :Any		):
            A		=  self.prepare_config_and_inputs()
            A,     A,     A		=  config_and_inputs
            A		=  {"input_ids": input_ids, "attention_mask": attention_mask}
            return config, inputs_dict


     def      lowerCamelCase	( self      :Any		):
            A,     A,     A		=  self.prepare_config_and_inputs()

            A		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            A		=  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      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :Dict		):
            A		=  20
            A		=  model_class_name(__SCREAMING_SNAKE_CASE		)

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

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

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

            A		=  model(__SCREAMING_SNAKE_CASE		)

            A		=  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      :Tuple					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Optional[Any]		):
            A		=  20
            A		=  model_class_name(__SCREAMING_SNAKE_CASE		)

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

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

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

            A		=  model(__SCREAMING_SNAKE_CASE					,	attention_mask=__SCREAMING_SNAKE_CASE		)
            A		=  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       (     __lowerCAmelCase					,							__lowerCAmelCase					,							unittest.TestCase			):
     UpperCamelCase								=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     UpperCamelCase								=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()


     def      lowerCamelCase	( self      :Optional[int]		):
            A		=  FlaxXGLMModelTester(self		)


     def      lowerCamelCase	( self      :Optional[Any]		):




            for model_class_name in self.all_model_classes:
                   A,     A,     A		=  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      :List[Any]		):




            for model_class_name in self.all_model_classes:
                   A,     A,     A		=  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[int]		):
            A		=  XGLMTokenizer.from_pretrained("XGLM"					,	padding_side="left"		)
            A		=  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=__SCREAMING_SNAKE_CASE					,	truncation=__SCREAMING_SNAKE_CASE		)

            A		=  FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
            A		=  1
            A		=  False

            A		=  jax.jit(model.generate		)

            A		=  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]		).sequences

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

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

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

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

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

                          A		=  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		)
                                 A		=  model_class.from_pretrained(__SCREAMING_SNAKE_CASE					,	from_pt=__SCREAMING_SNAKE_CASE		)

                          A		=  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      :List[str]		):
            A,     A		=  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
                          A		=  self._prepare_for_class(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
                          A		=  {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

                          A		=  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		)
                                 A		=  pt_model_class.from_pretrained(__SCREAMING_SNAKE_CASE					,	from_flax=__SCREAMING_SNAKE_CASE		)

                          with torch.no_grad():
                                 A		=  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      :int		):




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









"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


