

'''simple docstring'''






from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def 						_A	(       A__	,							A__	,							A__=None	,							A__=None ):






 """simple docstring"""



 if attention_mask is None:
  __lowercase				    =					tf.cast(tf.math.not_equal(A__	,							config.pad_token_id )	,							tf.inta )
 return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class      lowercase_      :




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Tuple							     =     OPTConfig
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     {}
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     'gelu'






  def __init__( self  :	int   ,lowercase__  :	Any   ,lowercase__  :	Tuple=1_3   ,lowercase__  :	int=7   ,lowercase__  :	str=True   ,lowercase__  :	Any=False   ,lowercase__  :	Optional[Any]=9_9   ,lowercase__  :	List[Any]=1_6   ,lowercase__  :	List[str]=2   ,lowercase__  :	Tuple=4   ,lowercase__  :	Tuple=4   ,lowercase__  :	Any="gelu"   ,lowercase__  :	str=0.1   ,lowercase__  :	str=0.1   ,lowercase__  :	Union[str, Any]=2_0   ,lowercase__  :	Union[str, Any]=2   ,lowercase__  :	int=1   ,lowercase__  :	List[Any]=0   ,lowercase__  :	Optional[Any]=1_6   ,lowercase__  :	Optional[int]=1_6   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					seq_length
   __lowercase				    =					is_training
   __lowercase				    =					use_labels
   __lowercase				    =					vocab_size
   __lowercase				    =					hidden_size
   __lowercase				    =					num_hidden_layers
   __lowercase				    =					num_attention_heads
   __lowercase				    =					intermediate_size
   __lowercase				    =					hidden_act
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					max_position_embeddings
   __lowercase				    =					eos_token_id
   __lowercase				    =					pad_token_id
   __lowercase				    =					bos_token_id
   __lowercase				    =					embed_dim
   __lowercase				    =					word_embed_proj_dim
   __lowercase				    =					False






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size     )
   __lowercase				    =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )   ,1     )
   __lowercase				    =					tf.concat([input_ids, eos_tensor]   ,axis=1     )

   __lowercase				    =					self.config_cls(
       vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,ffn_dim=self.intermediate_size   ,dropout=self.hidden_dropout_prob   ,attention_dropout=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,eos_token_id=self.eos_token_id   ,bos_token_id=self.bos_token_id   ,pad_token_id=self.pad_token_id   ,embed_dim=self.embed_dim   ,word_embed_proj_dim=self.word_embed_proj_dim   ,is_encoder_decoder=lowercase__   ,**self.config_updates   ,)
   __lowercase				    =					prepare_opt_inputs_dict(lowercase__   ,lowercase__     )
   return config, inputs_dict






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	List[Any]     ):
   __lowercase				    =					TFOPTModel(config=lowercase__     )
   __lowercase				    =					inputs_dict['''input_ids''']

   __lowercase				    =					input_ids[:1, :]
   __lowercase				    =					inputs_dict['''attention_mask'''][:1, :]
   __lowercase				    =					1

   # first forward pass
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,use_cache=lowercase__     )

   __lowercase    ,      __lowercase				    =					outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   __lowercase				    =					ids_tensor((self.batch_size, 3)   ,config.vocab_size     )
   __lowercase				    =					tf.cast(ids_tensor((self.batch_size, 3)   ,2     )   ,tf.inta     )

   # append to next input_ids and
   __lowercase				    =					tf.concat([input_ids, next_tokens]   ,axis=-1     )
   __lowercase				    =					tf.concat([attention_mask, next_attn_mask]   ,axis=-1     )

   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__     )[0]
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,past_key_values=lowercase__     )[0]

   self.parent.assertEqual(next_tokens.shape[1]   ,output_from_past.shape[1]     )

   # select random slice
   __lowercase				    =					int(ids_tensor((1,)   ,output_from_past.shape[-1]     )     )
   __lowercase				    =					output_from_no_past[:, -3:, random_slice_idx]
   __lowercase				    =					output_from_past[:, :, random_slice_idx]

   # test that outputs are equal for slice
   tf.debugging.assert_near(lowercase__   ,lowercase__   ,rtol=1e-3     )






@require_tf
class      lowercase_      (lowerCamelCase__							,			lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    List[str]							     =     (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     (TFOPTForCausalLM,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE       :    List[str]							     =     (
      {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
  )
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     False
  SCREAMING_SNAKE_CASE       :    str							     =     False
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     False
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     1_0






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					TFOPTModelTester(self     )
   __lowercase				    =					ConfigTester(self   ,config_class=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   self.config_tester.run_common_tests()






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   def _get_word_embedding_weight(lowercase__  :	Optional[int]   ,lowercase__  :	List[str]     ):
    if hasattr(lowercase__   ,'''weight'''     ):
     return embedding_layer.weight
    else:
     # Here we build the word embeddings weights if not exists.
     # And then we retry to get the attribute once built.
     model.build()
     if hasattr(lowercase__   ,'''weight'''     ):
      return embedding_layer.weight
     else:
      return None

   for model_class in self.all_model_classes:
    for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
     # build the embeddings
     __lowercase				    =					model_class(config=lowercase__     )
     __lowercase				    =					_get_word_embedding_weight(lowercase__   ,model.get_input_embeddings()     )
     __lowercase				    =					_get_word_embedding_weight(lowercase__   ,model.get_output_embeddings()     )

     # reshape the embeddings
     model.resize_token_embeddings(lowercase__     )
     __lowercase				    =					_get_word_embedding_weight(lowercase__   ,model.get_input_embeddings()     )
     __lowercase				    =					_get_word_embedding_weight(lowercase__   ,model.get_output_embeddings()     )

     # check that the resized embeddings size matches the desired size.
     __lowercase				    =					size if size is not None else config.vocab_size

     self.assertEqual(new_input_embeddings.shape[0]   ,lowercase__     )

     # check that weights remain the same after resizing
     __lowercase				    =					True
     for pa, pa in zip(old_input_embeddings.value()   ,new_input_embeddings.value()     ):
      if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
       __lowercase				    =					False
     self.assertTrue(lowercase__     )

     if old_output_embeddings is not None and new_output_embeddings is not None:
      self.assertEqual(new_output_embeddings.shape[0]   ,lowercase__     )

      __lowercase				    =					True
      for pa, pa in zip(old_output_embeddings.value()   ,new_output_embeddings.value()     ):
       if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
        __lowercase				    =					False
      self.assertTrue(lowercase__     )


def 						_A	(       A__ ):






 """simple docstring"""



 return tf.constant(A__	,							dtype=tf.intaa )






@require_tf
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     9_9






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					tf.ones((4, 1)   ,dtype=tf.intaa     ) * 2
   __lowercase				    =					tf.concat([ids_tensor((4, 6)   ,self.vocab_size - 3     ) + 3, eos_column_vector]   ,axis=1     )
   __lowercase				    =					input_ids.shape[0]
   __lowercase				    =					OPTConfig(
       vocab_size=self.vocab_size   ,hidden_size=2_4   ,num_hidden_layers=2   ,num_attention_heads=2   ,ffn_dim=3_2   ,max_position_embeddings=4_8   ,eos_token_id=2   ,pad_token_id=1   ,bos_token_id=0   ,)
   return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					TFOPTModel.from_pretrained('''facebook/opt-350m'''     )
   __lowercase				    =					_long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]     )
   __lowercase				    =					tf.not_equal(lowercase__   ,model.config.pad_token_id     )
   with tf.GradientTape():
    __lowercase				    =					model(input_ids=lowercase__   ,attention_mask=lowercase__     ).last_hidden_state
   __lowercase				    =					(1, 1_1, 5_1_2)
   self.assertEqual(output.shape   ,lowercase__     )
   __lowercase				    =					tf.constant(
       [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]     )
   self.assertTrue(np.allclose(output[:, :3, :3]   ,lowercase__   ,atol=4e-3     )     )

   __lowercase				    =					tf.function(lowercase__   ,jit_compile=lowercase__     )
   __lowercase				    =					xla_generate(lowercase__   ,lowercase__     )[0]
   self.assertTrue(np.allclose(output[:, :3, :3]   ,lowercase__   ,atol=4e-2     )     )






@require_tf
@slow
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   super().setUp()
   __lowercase				    =					'''facebook/opt-350m'''






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					TFOPTForCausalLM.from_pretrained(self.path_model     )
   __lowercase				    =					GPTaTokenizer.from_pretrained(self.path_model     )

   __lowercase				    =					[
       '''Today is a beautiful day and I want to''',
       '''In the city of''',
       '''Paris is the capital of France and''',
       '''Computers and mobile phones have taken''',
   ]
   # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
   __lowercase				    =					tokenizer(lowercase__   ,return_tensors='''tf'''   ,padding=lowercase__   ,add_special_tokens=lowercase__     )
   __lowercase				    =					tf.math.reduce_mean(model(inputs.input_ids   ,attention_mask=inputs.attention_mask     )[0]   ,axis=-1     )
   __lowercase				    =					tf.constant(
       [
           [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
           [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
           [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
           [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
       ]     )
   self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-4     )     )

   __lowercase				    =					tf.function(lowercase__   ,jit_compile=lowercase__     )
   __lowercase				    =					tf.math.reduce_mean(xla_generate(inputs.input_ids   ,attention_mask=inputs.attention_mask     )[0]   ,axis=-1     )
   self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-4     )     )






@require_tf
@slow
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return [
       "Today is a beautiful day and I want",
       "In the city of",
       "Paris is the capital of France and",
       "Computers and mobile phones have taken",
   ]






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					'''facebook/opt-125m'''

   __lowercase				    =					[
       '''Today is a beautiful day and I want to''',
       '''In the city of New York, the city''',
       '''Paris is the capital of France and the capital''',
       '''Computers and mobile phones have taken over the''',
   ]

   __lowercase				    =					[]
   __lowercase				    =					GPTaTokenizer.from_pretrained(lowercase__     )
   __lowercase				    =					TFOPTForCausalLM.from_pretrained(lowercase__     )

   for prompt in self.prompts:
    __lowercase				    =					tokenizer(lowercase__   ,return_tensors='''tf'''     ).input_ids

    __lowercase				    =					model.generate(lowercase__   ,max_length=1_0     )

    __lowercase				    =					tokenizer.batch_decode(lowercase__   ,skip_special_tokens=lowercase__     )
    predicted_outputs += generated_string

   self.assertListEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					'''facebook/opt-350m'''

   __lowercase				    =					GPTaTokenizer.from_pretrained(lowercase__     )
   __lowercase				    =					TFOPTForCausalLM.from_pretrained(lowercase__     )

   __lowercase				    =					'''left'''

   # use different length sentences to test batching
   __lowercase				    =					[
       '''Hello, my dog is a little''',
       '''Today, I''',
   ]

   __lowercase				    =					tokenizer(lowercase__   ,return_tensors='''tf'''   ,padding=lowercase__     )
   __lowercase				    =					inputs['''input_ids''']

   __lowercase				    =					model.generate(input_ids=lowercase__   ,attention_mask=inputs['''attention_mask''']     )

   __lowercase				    =					tokenizer(sentences[0]   ,return_tensors='''tf'''     ).input_ids
   __lowercase				    =					model.generate(input_ids=lowercase__     )

   __lowercase				    =					inputs_non_padded.shape[-1] - tf.math.reduce_sum(
       tf.cast(inputs['''attention_mask'''][-1]   ,tf.intaa     )     )
   __lowercase				    =					tokenizer(sentences[1]   ,return_tensors='''tf'''     ).input_ids
   __lowercase				    =					model.generate(input_ids=lowercase__   ,max_length=model.config.max_length - num_paddings     )

   __lowercase				    =					tokenizer.batch_decode(lowercase__   ,skip_special_tokens=lowercase__     )
   __lowercase				    =					tokenizer.decode(output_non_padded[0]   ,skip_special_tokens=lowercase__     )
   __lowercase				    =					tokenizer.decode(output_padded[0]   ,skip_special_tokens=lowercase__     )

   __lowercase				    =					[
       '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
       '''Today, I was in the middle of a conversation with a friend about the''',
   ]
   self.assertListEqual(lowercase__   ,lowercase__     )
   self.assertListEqual(lowercase__   ,[non_padded_sentence, padded_sentence]     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					'''facebook/opt-350m'''

   __lowercase				    =					[
       '''Today is a beautiful day and I want to''',
       '''In the city of San Francisco, the city''',
       '''Paris is the capital of France and the capital''',
       '''Computers and mobile phones have taken over the''',
   ]

   __lowercase				    =					[]
   __lowercase				    =					GPTaTokenizer.from_pretrained(lowercase__     )
   __lowercase				    =					TFOPTForCausalLM.from_pretrained(lowercase__     )

   for prompt in self.prompts:
    __lowercase				    =					tokenizer(lowercase__   ,return_tensors='''tf'''     ).input_ids

    __lowercase				    =					model.generate(lowercase__   ,max_length=1_0     )

    __lowercase				    =					tokenizer.batch_decode(lowercase__   ,skip_special_tokens=lowercase__     )
    predicted_outputs += generated_string

   self.assertListEqual(lowercase__   ,lowercase__     )







'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= (3, 32, 128)
								A_									= tempfile.mkdtemp()

								# fmt: off
								A_									= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')

								A_									= {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 128},
								}
								A_									= os.path.join(self.tmpdirname      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

								A_									= self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)')
								A_									= self.get_image_processor(do_normalize=_lowercase      ,       padding_value=1.0)

								A_									= MgpstrProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=_lowercase      ,       padding_value=1.0)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= processor(images=_lowercase      ,       return_tensors='np')

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2)



					def     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

								A_									= tokenizer(_lowercase)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key])



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       ['pixel_values', 'labels'])

								# test if it raises when no input is passed
								with pytest.raises(_lowercase):
											processor()



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								A_									= processor.char_decode(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       processor.model_input_names)



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

								A_									= processor.batch_decode([char_input, bpe_input, wp_input])

								self.assertListEqual(list(results.keys())      ,       ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])




