
'''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 			__A					(			snake_case_      ):
 lowerCamelCase												=["image_processor", "tokenizer"]
 lowerCamelCase												="LayoutLMv2ImageProcessor"
 lowerCamelCase												=("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
 def __init__( self      :     Optional[Any]					, lowerCamelCase      :     Any=None					, lowerCamelCase      :     List[Any]=None					, **lowerCamelCase      :     List[Any]							):



   """simple docstring"""





   __A		:  int       				=		None
   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""					, lowercase_					, )
     __A		:  str       				=		kwargs.pop("""feature_extractor"""							)

   __A		:  str       				=		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__(lowercase_					, lowercase_							)
 def __call__( self      :     Any					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     List[Any] = None					, lowerCamelCase      :     Optional[int] = None					, lowerCamelCase      :     int = None					, lowerCamelCase      :     Optional[int] = None					, lowerCamelCase      :     Tuple = True					, lowerCamelCase      :     Optional[int] = False					, lowerCamelCase      :     Optional[Any] = False					, lowerCamelCase      :     Dict = None					, lowerCamelCase      :     int = 0					, lowerCamelCase      :     int = None					, lowerCamelCase      :     Tuple = None					, lowerCamelCase      :     Optional[Any] = None					, lowerCamelCase      :     str = False					, lowerCamelCase      :     Dict = False					, lowerCamelCase      :     Any = False					, lowerCamelCase      :     Union[str, Any] = False					, lowerCamelCase      :     Any = True					, lowerCamelCase      :     int = None					, **lowerCamelCase      :     List[Any]					, ):



   """simple docstring"""





   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		:  Any       				=		self.image_processor(images=lowercase_					, return_tensors=lowercase_							)

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

   __A		:  Any       				=		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=lowercase_					, add_special_tokens=lowercase_					, padding=lowercase_					, truncation=lowercase_					, max_length=lowercase_					, stride=lowercase_					, pad_to_multiple_of=lowercase_					, return_token_type_ids=lowercase_					, return_attention_mask=lowercase_					, return_overflowing_tokens=lowercase_					, return_special_tokens_mask=lowercase_					, return_offsets_mapping=lowercase_					, return_length=lowercase_					, verbose=lowercase_					, return_tensors=lowercase_					, **lowercase_					, )

   # add pixel values
   __A		:  Union[str, Any]       				=		features.pop("""pixel_values"""							)
   if return_overflowing_tokens is True:
     __A		:  Optional[int]       				=		self.get_overflowing_images(lowercase_					, encoded_inputs["""overflow_to_sample_mapping"""]							)
   __A		:  List[str]       				=		images

   return encoded_inputs
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     Any					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Dict       				=		[]
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]							)

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

   return images_with_overflow
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[str]					, **lowerCamelCase      :     Any							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowercase_					, **lowercase_							)
 def 		lowercase_( self      :     Optional[int]					, *lowerCamelCase      :     Any					, **lowerCamelCase      :     int							):



   """simple docstring"""





   return self.tokenizer.decode(*lowercase_					, **lowercase_							)
 @property
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





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



   """simple docstring"""





   warnings.warn(
       """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""					, lowercase_					, )
   return self.image_processor_class






 @property
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   warnings.warn(
       """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""					, lowercase_					, )
   return self.image_processor








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			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=14    ,  lowercase_=7    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=99    ,  lowercase_=32    ,  lowercase_=5    ,  lowercase_=4    ,  lowercase_=37    ,  lowercase_="gelu"    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=512    ,  lowercase_=0.0_2    ,  lowercase_=None    ,  )	->					Tuple:
									lowerCAmelCase	:   Union[str, Any]					     =					parent
									lowerCAmelCase	:   Optional[Any]					     =					batch_size
									lowerCAmelCase	:   Tuple					     =					seq_length
									lowerCAmelCase	:   Optional[Any]					     =					is_training
									lowerCAmelCase	:   Union[str, Any]					     =					use_input_mask
									lowerCAmelCase	:   int					     =					use_labels
									lowerCAmelCase	:   List[str]					     =					vocab_size
									lowerCAmelCase	:   List[Any]					     =					d_model
									lowerCAmelCase	:   List[Any]					     =					num_hidden_layers
									lowerCAmelCase	:   int					     =					num_attention_heads
									lowerCAmelCase	:   Optional[Any]					     =					ffn_dim
									lowerCAmelCase	:   str					     =					activation_function
									lowerCAmelCase	:   Any					     =					activation_dropout
									lowerCAmelCase	:   List[str]					     =					attention_dropout
									lowerCAmelCase	:   str					     =					max_position_embeddings
									lowerCAmelCase	:   Dict					     =					initializer_range
									lowerCAmelCase	:   str					     =					None
									lowerCAmelCase	:   Any					     =					0
									lowerCAmelCase	:   Tuple					     =					2
									lowerCAmelCase	:   Dict					     =					1






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Tuple					     =					np.clip(ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )    ,  3    ,  self.vocab_size )

									lowerCAmelCase	:   List[Any]					     =					None
									if self.use_input_mask:
															lowerCAmelCase	:   Dict					     =					random_attention_mask([self.batch_size, self.seq_length] )

									lowerCAmelCase	:   Tuple					     =					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=lowercase_    ,  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 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					config_and_inputs
									lowerCAmelCase	:   int					     =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					self.prepare_config_and_inputs()

									lowerCAmelCase	:   Union[str, Any]					     =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									lowerCAmelCase	:   List[str]					     =					ids_tensor([self.batch_size, self.seq_length]    ,  vocab_size=2 )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   str					     =					20
									lowerCAmelCase	:   List[str]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   Any					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,  dtype="""i4""" )

									lowerCAmelCase	:   List[str]					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )

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

									lowerCAmelCase	:   List[str]					     =					model(lowercase_ )

									lowerCAmelCase	:   Optional[int]					     =					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 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Union[str, Any]					     =					20
									lowerCAmelCase	:   List[Any]					     =					model_class_name(lowercase_ )

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

									lowerCAmelCase	:   int					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   int					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )
									lowerCAmelCase	:   Optional[int]					     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,  dtype="""i4""" )
									lowerCAmelCase	:   List[Any]					     =					model(
									    input_ids[:, -1:]    ,  past_key_values=outputs_cache.past_key_values    ,  attention_mask=lowercase_    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase_    ,  attention_mask=lowercase_ )
									lowerCAmelCase	:   str					     =					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			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   List[str]	     =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			_UpperCamelCase:   str	     =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Any					     =					FlaxXGLMModelTester(self )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					XGLMTokenizer.from_pretrained("""XGLM"""    ,  padding_side="""left""" )
									lowerCAmelCase	:   int					     =					tokenizer(["""Hello this is a long string""", """Hey"""]    ,  return_tensors="""np"""    ,  padding=lowercase_    ,  truncation=lowercase_ )

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

									lowerCAmelCase	:   int					     =					jax.jit(model.generate )

									lowerCAmelCase	:   Optional[Any]					     =					jit_generate(inputs["""input_ids"""]    ,  attention_mask=inputs["""attention_mask"""] ).sequences

									lowerCAmelCase	:   List[str]					     =					tokenizer.batch_decode(lowercase_    ,  skip_special_tokens=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					[
									    """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(lowercase_    ,  lowercase_ )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase     ,	lowerCAmelCase	:   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
																					lowerCAmelCase	:   Union[str, Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   List[Any]					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

																					with torch.no_grad():
																											lowerCAmelCase	:   Dict					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Tuple					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   Optional[int]					     =					model_class.from_pretrained(lowercase_    ,  from_pt=lowercase_ )

																					lowerCAmelCase	:   Optional[int]					     =					fx_model_loaded(**lowercase_ ).to_tuple()
																					self.assertEqual(
																					    len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output_loaded, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output_loaded[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					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	:   List[Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   Tuple					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

																					lowerCAmelCase	:   Optional[Any]					     =					pt_model_class(lowercase_ ).eval()
																					lowerCAmelCase	:   Any					     =					False
																					lowerCAmelCase	:   Optional[int]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )

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

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

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Optional[Any]					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   List[str]					     =					pt_model_class.from_pretrained(lowercase_    ,  from_flax=lowercase_ )

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model_loaded(**lowercase_ ).to_tuple()

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






			@slow
			def 							_snake_case   (    self )	->					str:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase	:   Optional[Any]					     =					model_class_name.from_pretrained("""facebook/xglm-564M""" )
															lowerCAmelCase	:   int					     =					model(np.ones((1, 1) ) )
															self.assertIsNotNone(lowercase_ )

