






import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@parameterized.expand([(None,), ('foo.json',)]    )
	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig(
							    do_sample=UpperCAmelCase       ,    temperature=0.7       ,    length_penalty=1.0       ,    bad_words_ids=[[1, 2, 3], [4, 5]]       ,    )
							with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(UpperCAmelCase       ,    config_name=UpperCAmelCase    )
													_UpperCAmelCase									=			GenerationConfig.from_pretrained(UpperCAmelCase       ,    config_name=UpperCAmelCase    )

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample       ,    UpperCAmelCase    )
							self.assertEqual(loaded_config.temperature       ,    0.7    )
							self.assertEqual(loaded_config.length_penalty       ,    1.0    )
							self.assertEqual(loaded_config.bad_words_ids       ,    [[1, 2, 3], [4, 5]]    )

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k       ,    50    )
							self.assertEqual(loaded_config.max_length       ,    20    )
							self.assertEqual(loaded_config.max_time       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AutoConfig.from_pretrained('gpt2'    )
							_UpperCAmelCase									=			GenerationConfig.from_model_config(UpperCAmelCase    )
							_UpperCAmelCase									=			GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id       ,    default_generation_config.eos_token_id    )
							self.assertEqual(generation_config_from_model.eos_token_id       ,    model_config.eos_token_id    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig()
							_UpperCAmelCase									=			{
							    'max_new_tokens': 1024,
							    'foo': 'bar',
							}
							_UpperCAmelCase									=			copy.deepcopy(UpperCAmelCase    )
							_UpperCAmelCase									=			generation_config.update(**UpperCAmelCase    )

							# update_kwargs was not modified (no side effects)
							self.assertEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens       ,    1024    )

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(UpperCAmelCase       ,    {'foo': 'bar'}    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig()
							_UpperCAmelCase									=			'bar'

							with tempfile.TemporaryDirectory('test-generation-config'    ) as tmp_dir:
													generation_config.save_pretrained(UpperCAmelCase    )

													_UpperCAmelCase									=			GenerationConfig.from_pretrained(UpperCAmelCase    )
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo       ,    'bar'    )

							_UpperCAmelCase									=			GenerationConfig.from_model_config(UpperCAmelCase    )
							assert not hasattr(UpperCAmelCase       ,    'foo'    )  # no new kwargs should be initialized if from config

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig()
							self.assertEqual(default_config.temperature       ,    1.0    )
							self.assertEqual(default_config.do_sample       ,    UpperCAmelCase    )
							self.assertEqual(default_config.num_beams       ,    1    )

							_UpperCAmelCase									=			GenerationConfig(
							    do_sample=UpperCAmelCase       ,    temperature=0.7       ,    length_penalty=1.0       ,    bad_words_ids=[[1, 2, 3], [4, 5]]       ,    )
							self.assertEqual(config.temperature       ,    0.7    )
							self.assertEqual(config.do_sample       ,    UpperCAmelCase    )
							self.assertEqual(config.num_beams       ,    1    )

							with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(UpperCAmelCase    )
													_UpperCAmelCase									=			GenerationConfig.from_pretrained(UpperCAmelCase       ,    temperature=1.0    )

							self.assertEqual(loaded_config.temperature       ,    1.0    )
							self.assertEqual(loaded_config.do_sample       ,    UpperCAmelCase    )
							self.assertEqual(loaded_config.num_beams       ,    1    )  # default value





@is_staging_test
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@classmethod
	def        UpperCamelCase    (							cls    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TOKEN
							HfFolder.save_token(UpperCAmelCase    )

	@classmethod
	def        UpperCamelCase    (							cls    ):
							"""simple docstring"""







							try:
													delete_repo(token=cls._token       ,    repo_id='test-generation-config'    )
							except HTTPError:
													pass

							try:
													delete_repo(token=cls._token       ,    repo_id='valid_org/test-generation-config-org'    )
							except HTTPError:
													pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig(
							    do_sample=UpperCAmelCase       ,    temperature=0.7       ,    length_penalty=1.0       ,    )
							config.push_to_hub('test-generation-config'       ,    use_auth_token=self._token    )

							_UpperCAmelCase									=			GenerationConfig.from_pretrained(F"""{USER}/test-generation-config"""    )
							for k, v in config.to_dict().items():
													if k != "transformers_version":
																			self.assertEqual(UpperCAmelCase       ,    getattr(UpperCAmelCase       ,    UpperCAmelCase    )    )

        # Reset repo
							delete_repo(token=self._token       ,    repo_id='test-generation-config'    )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(
													    UpperCAmelCase       ,    repo_id='test-generation-config'       ,    push_to_hub=UpperCAmelCase       ,    use_auth_token=self._token    )

							_UpperCAmelCase									=			GenerationConfig.from_pretrained(F"""{USER}/test-generation-config"""    )
							for k, v in config.to_dict().items():
													if k != "transformers_version":
																			self.assertEqual(UpperCAmelCase       ,    getattr(UpperCAmelCase       ,    UpperCAmelCase    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			GenerationConfig(
							    do_sample=UpperCAmelCase       ,    temperature=0.7       ,    length_penalty=1.0       ,    )
							config.push_to_hub('valid_org/test-generation-config-org'       ,    use_auth_token=self._token    )

							_UpperCAmelCase									=			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'    )
							for k, v in config.to_dict().items():
													if k != "transformers_version":
																			self.assertEqual(UpperCAmelCase       ,    getattr(UpperCAmelCase       ,    UpperCAmelCase    )    )

        # Reset repo
							delete_repo(token=self._token       ,    repo_id='valid_org/test-generation-config-org'    )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
													config.save_pretrained(
													    UpperCAmelCase       ,    repo_id='valid_org/test-generation-config-org'       ,    push_to_hub=UpperCAmelCase       ,    use_auth_token=self._token    )

							_UpperCAmelCase									=			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'    )
							for k, v in config.to_dict().items():
													if k != "transformers_version":
																			self.assertEqual(UpperCAmelCase       ,    getattr(UpperCAmelCase       ,    UpperCAmelCase    )    )








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 __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

							if text_encoding is not None:
													encoding_image_processor.update(UpperCAmelCase    )

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
