
'''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:
     UpperCamelCase_            =    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 					a_					(unittest.TestCase      ):




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   tempfile.mkdtemp()

         _lowerCAmelCase    :					str							=   PixaStructImageProcessor()
         _lowerCAmelCase    :					Optional[int]							=   TaTokenizer.from_pretrained("""t5-small"""					)

         _lowerCAmelCase    :					Any							=   PixaStructProcessor(snake_case_				,				snake_case_					)

         processor.save_pretrained(self.tmpdirname					)




       def 							__UpperCamelCase    (       self				,				**snake_case_					):
         return AutoProcessor.from_pretrained(self.tmpdirname				,				**snake_case_					).tokenizer




       def 							__UpperCamelCase    (       self				,				**snake_case_					):
         return AutoProcessor.from_pretrained(self.tmpdirname				,				**snake_case_					).image_processor




       def 							__UpperCamelCase    (       self					):
         shutil.rmtree(self.tmpdirname					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   [np.random.randint(2_5_5				,				size=(3, 3_0, 4_0_0)				,				dtype=np.uinta					)]

         _lowerCAmelCase    :					Union[str, Any]							=   [Image.fromarray(np.moveaxis(snake_case_				,				0				,				-1					)					) for x in image_inputs]

         return image_inputs




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   PixaStructProcessor(tokenizer=self.get_tokenizer()				,				image_processor=self.get_image_processor()					)
         processor.save_pretrained(self.tmpdirname					)

         _lowerCAmelCase    :					str							=   self.get_tokenizer(bos_token="""(BOS)"""				,				eos_token="""(EOS)"""					)
         _lowerCAmelCase    :					int							=   self.get_image_processor(do_normalize=snake_case_				,				padding_value=1.0					)

         _lowerCAmelCase    :					Union[str, Any]							=   PixaStructProcessor.from_pretrained(
             self.tmpdirname				,				bos_token="""(BOS)"""				,				eos_token="""(EOS)"""				,				do_normalize=snake_case_				,				padding_value=1.0					)

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

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   self.get_image_processor()
         _lowerCAmelCase    :					str							=   self.get_tokenizer()

         _lowerCAmelCase    :					Union[str, Any]							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					int							=   self.prepare_image_inputs()

         _lowerCAmelCase    :					str							=   image_processor(snake_case_				,				return_tensors="""np"""					)
         _lowerCAmelCase    :					str							=   processor(images=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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   self.get_image_processor()
         _lowerCAmelCase    :					Any							=   self.get_tokenizer()

         _lowerCAmelCase    :					Dict							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   """lower newer"""

         _lowerCAmelCase    :					str							=   processor(text=snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   tokenizer(snake_case_				,				return_token_type_ids=snake_case_				,				add_special_tokens=snake_case_					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   self.get_image_processor()
         _lowerCAmelCase    :					Any							=   self.get_tokenizer()

         _lowerCAmelCase    :					Dict							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					int							=   """lower newer"""
         _lowerCAmelCase    :					Union[str, Any]							=   self.prepare_image_inputs()

         _lowerCAmelCase    :					Dict							=   processor(text=snake_case_				,				images=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(snake_case_					):
           processor()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   self.get_image_processor()
         _lowerCAmelCase    :					int							=   self.get_tokenizer()

         _lowerCAmelCase    :					List[Any]							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					Tuple							=   """lower newer"""
         _lowerCAmelCase    :					Tuple							=   self.prepare_image_inputs()

         _lowerCAmelCase    :					Optional[Any]							=   processor(text=snake_case_				,				images=snake_case_					)

         _lowerCAmelCase    :					Optional[int]							=   [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
         _lowerCAmelCase    :					Optional[int]							=   [7_7_0, 7_7_0, 7_7_0, 7_7_0]
         # with text
         for i, max_patch in enumerate(snake_case_					):
           _lowerCAmelCase    :					int							=   processor(text=snake_case_				,				images=snake_case_				,				max_patches=snake_case_					)
           self.assertEqual(inputs["""flattened_patches"""][0].shape[0]				,				snake_case_					)
           self.assertEqual(inputs["""flattened_patches"""][0].shape[1]				,				expected_hidden_size[i]					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   self.get_image_processor()
         _lowerCAmelCase    :					Optional[Any]							=   self.get_tokenizer()

         _lowerCAmelCase    :					List[Any]							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					List[str]							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase    :					Tuple							=   processor.batch_decode(snake_case_					)
         _lowerCAmelCase    :					Optional[Any]							=   tokenizer.batch_decode(snake_case_					)

         self.assertListEqual(snake_case_				,				snake_case_					)







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   self.get_image_processor()
         _lowerCAmelCase    :					int							=   self.get_tokenizer()

         _lowerCAmelCase    :					Any							=   PixaStructProcessor(tokenizer=snake_case_				,				image_processor=snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   """lower newer"""
         _lowerCAmelCase    :					Any							=   self.prepare_image_inputs()

         _lowerCAmelCase    :					int							=   processor(text=snake_case_				,				images=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"""]					)

         _lowerCAmelCase    :					Optional[Any]							=   processor(text=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'''

from typing import TYPE_CHECKING

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


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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


