





"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   a__   :       int   						=			False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_vision
@require_torch
class        lowercase_			(       unittest.TestCase							):

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			tempfile.mkdtemp()

        snake_case__      							=			PixaStructImageProcessor()
        snake_case__      							=			TaTokenizer.from_pretrained("t5-small"       )

        snake_case__      							=			PixaStructProcessor(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

        processor.save_pretrained(self.tmpdirname       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       ).tokenizer

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       ).image_processor

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			[np.random.randint(255				,   size=(3, 30, 400)				,   dtype=np.uinta       )]

        snake_case__      							=			[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE				,   0				,   -1       )       ) for x in image_inputs]

        return image_inputs

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			PixaStructProcessor(tokenizer=self.get_tokenizer()				,   image_processor=self.get_image_processor()       )
        processor.save_pretrained(self.tmpdirname       )

        snake_case__      							=			self.get_tokenizer(bos_token="(BOS)"				,   eos_token="(EOS)"       )
        snake_case__      							=			self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE				,   padding_value=1.0       )

        snake_case__      							=			PixaStructProcessor.from_pretrained(
            self.tmpdirname				,   bos_token="(BOS)"				,   eos_token="(EOS)"				,   do_normalize=__SCREAMING_SNAKE_CASE				,   padding_value=1.0       )

        self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer_add_kwargs.get_vocab()       )
        self.assertIsInstance(processor.tokenizer				,   __SCREAMING_SNAKE_CASE       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.prepare_image_inputs()

        snake_case__      							=			image_processor(__SCREAMING_SNAKE_CASE				,   return_tensors="np"       )
        snake_case__      							=			processor(images=__SCREAMING_SNAKE_CASE				,   return_tensors="np"       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"lower newer"

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			tokenizer(__SCREAMING_SNAKE_CASE				,   return_token_type_ids=__SCREAMING_SNAKE_CASE				,   add_special_tokens=__SCREAMING_SNAKE_CASE       )

        for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"lower newer"
        snake_case__      							=			self.prepare_image_inputs()

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE				,   images=__SCREAMING_SNAKE_CASE       )

        self.assertListEqual(
            list(inputs.keys()       )				,   ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"lower newer"
        snake_case__      							=			self.prepare_image_inputs()

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE				,   images=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			[512, 1024, 2048, 4096]
        snake_case__      							=			[770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE				,   images=__SCREAMING_SNAKE_CASE				,   max_patches=__SCREAMING_SNAKE_CASE       )
         self.assertEqual(inputs["flattened_patches"][0].shape[0]				,   __SCREAMING_SNAKE_CASE       )
         self.assertEqual(inputs["flattened_patches"][0].shape[1]				,   expected_hidden_size[i]       )

        # without text input
        for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			processor(images=__SCREAMING_SNAKE_CASE				,   max_patches=__SCREAMING_SNAKE_CASE       )
         self.assertEqual(inputs["flattened_patches"][0].shape[0]				,   __SCREAMING_SNAKE_CASE       )
         self.assertEqual(inputs["flattened_patches"][0].shape[1]				,   expected_hidden_size[i]       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			processor.batch_decode(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )

        self.assertListEqual(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"lower newer"
        snake_case__      							=			self.prepare_image_inputs()

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE				,   images=__SCREAMING_SNAKE_CASE       )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()       )				,   ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE       )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()       )				,   ["input_ids", "attention_mask"]       )








"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__   :       Optional[Any]   						=			logging.get_logger(__name__)

a__   :       List[str]   						=			{}



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """fsmt_decoder"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

        super().__init__()
        snake_case__      							=			vocab_size
        snake_case__      							=			bos_token_id



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							str										=       """fsmt"""
       snake_case__			:							List[Any]										=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=["en", "de"]				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE="relu"				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=200				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=1.0				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=2				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			langs
        snake_case__      							=			src_vocab_size
        snake_case__      							=			tgt_vocab_size
        snake_case__      							=			d_model  # encoder_embed_dim and decoder_embed_dim

        snake_case__      							=			encoder_ffn_dim
        snake_case__      							=			snake_case__      							=			encoder_layers
        snake_case__      							=			encoder_attention_heads
        snake_case__      							=			encoder_layerdrop
        snake_case__      							=			decoder_layerdrop
        snake_case__      							=			decoder_ffn_dim
        snake_case__      							=			decoder_layers
        snake_case__      							=			decoder_attention_heads
        snake_case__      							=			max_position_embeddings
        snake_case__      							=			init_std  # Normal(0, this parameter)
        snake_case__      							=			activation_function

        snake_case__      							=			DecoderConfig(vocab_size=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE       )
        if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

        snake_case__      							=			scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        snake_case__      							=			attention_dropout
        snake_case__      							=			activation_dropout
        snake_case__      							=			dropout

        snake_case__      							=			use_cache
        super().__init__(
            pad_token_id=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   decoder_start_token_id=__SCREAMING_SNAKE_CASE				,   is_encoder_decoder=__SCREAMING_SNAKE_CASE				,   tie_word_embeddings=__SCREAMING_SNAKE_CASE				,   forced_eos_token_id=__SCREAMING_SNAKE_CASE				,   max_length=__SCREAMING_SNAKE_CASE				,   num_beams=__SCREAMING_SNAKE_CASE				,   length_penalty=__SCREAMING_SNAKE_CASE				,   early_stopping=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )
        snake_case__      							=			self.decoder.to_dict()
        snake_case__      							=			self.__class__.model_type
        return output

