

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 				a  (	unittest.TestCase				):
						_lowercase    						=			ViTImageProcessor if is_vision_available() else None


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    (3, 32, 128)
										_UpperCAmelCase       :		str 		=    tempfile.mkdtemp()

										# fmt: off
										_UpperCAmelCase       :		Optional[int] 		=    ["[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
										_UpperCAmelCase       :		int 		=    dict(zip(A_						,					range(len(A_	)	)	)	)

										_UpperCAmelCase       :		Optional[int] 		=    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(A_	) + "\n"	)

										_UpperCAmelCase       :		Tuple 		=    {
										    "do_normalize": False,
										    "do_resize": True,
										    "image_processor_type": "ViTImageProcessor",
										    "resample": 3,
										    "size": {"height": 32, "width": 128},
										}
										_UpperCAmelCase       :		Union[str, Any] 		=    os.path.join(self.tmpdirname						,					A_	)
										with open(self.image_processor_file						,					"w"						,					encoding="utf-8"	) as fp:
														json.dump(A_						,					A_	)


						def        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										return MgpstrTokenizer.from_pretrained(self.tmpdirname						,					**A_	)


						def        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										return ViTImageProcessor.from_pretrained(self.tmpdirname						,					**A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    np.random.randint(255						,					size=(3, 30, 400)						,					dtype=np.uinta	)

										_UpperCAmelCase       :		Union[str, Any] 		=    Image.fromarray(np.moveaxis(A_						,					0						,					-1	)	)

										return image_input


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    self.get_tokenizer()
										_UpperCAmelCase       :		Any 		=    self.get_image_processor()

										_UpperCAmelCase       :		Any 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)
										processor.save_pretrained(self.tmpdirname	)
										_UpperCAmelCase       :		int 		=    MgpstrProcessor.from_pretrained(self.tmpdirname						,					use_fast=A_	)

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.get_tokenizer()
										_UpperCAmelCase       :		str 		=    self.get_image_processor()

										_UpperCAmelCase       :		Any 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)
										processor.save_pretrained(self.tmpdirname	)

										_UpperCAmelCase       :		List[str] 		=    self.get_tokenizer(bos_token="(BOS)"						,					eos_token="(EOS)"	)
										_UpperCAmelCase       :		int 		=    self.get_image_processor(do_normalize=A_						,					padding_value=1.0	)

										_UpperCAmelCase       :		Optional[int] 		=    MgpstrProcessor.from_pretrained(
										    self.tmpdirname						,					bos_token="(BOS)"						,					eos_token="(EOS)"						,					do_normalize=A_						,					padding_value=1.0	)

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.get_image_processor()
										_UpperCAmelCase       :		Union[str, Any] 		=    self.get_tokenizer()

										_UpperCAmelCase       :		int 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		Optional[int] 		=    self.prepare_image_inputs()

										_UpperCAmelCase       :		List[str] 		=    image_processor(A_						,					return_tensors="np"	)
										_UpperCAmelCase       :		Optional[int] 		=    processor(images=A_						,					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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.get_image_processor()
										_UpperCAmelCase       :		Any 		=    self.get_tokenizer()

										_UpperCAmelCase       :		Optional[Any] 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		int 		=    "test"

										_UpperCAmelCase       :		Tuple 		=    processor(text=A_	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.get_image_processor()
										_UpperCAmelCase       :		Optional[int] 		=    self.get_tokenizer()

										_UpperCAmelCase       :		Any 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		List[str] 		=    "test"
										_UpperCAmelCase       :		Dict 		=    self.prepare_image_inputs()

										_UpperCAmelCase       :		int 		=    processor(text=A_						,					images=A_	)

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.get_image_processor()
										_UpperCAmelCase       :		List[str] 		=    self.get_tokenizer()

										_UpperCAmelCase       :		List[str] 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		Dict 		=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCAmelCase       :		Tuple 		=    processor.char_decode(A_	)
										_UpperCAmelCase       :		Any 		=    tokenizer.batch_decode(A_	)
										_UpperCAmelCase       :		List[Any] 		=    [seq.replace(" "						,					""	) for seq in decoded_tok]

										self.assertListEqual(A_						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.get_image_processor()
										_UpperCAmelCase       :		str 		=    self.get_tokenizer()

										_UpperCAmelCase       :		Optional[int] 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		str 		=    None
										_UpperCAmelCase       :		str 		=    self.prepare_image_inputs()

										_UpperCAmelCase       :		Optional[Any] 		=    processor(text=A_						,					images=A_	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.get_image_processor()
										_UpperCAmelCase       :		List[Any] 		=    self.get_tokenizer()

										_UpperCAmelCase       :		int 		=    MgpstrProcessor(tokenizer=A_						,					image_processor=A_	)

										_UpperCAmelCase       :		str 		=    torch.randn(1						,					27						,					38	)
										_UpperCAmelCase       :		Dict 		=    torch.randn(1						,					27						,					50257	)
										_UpperCAmelCase       :		List[Any] 		=    torch.randn(1						,					27						,					30522	)

										_UpperCAmelCase       :		Any 		=    processor.batch_decode([char_input, bpe_input, wp_input]	)

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bloom"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "num_hidden_layers": "n_layer",
						    "num_attention_heads": "n_head",
						}


						def __init__(       self						,					A_=250880						,					A_=64						,					A_=2						,					A_=8						,					A_=1e-5						,					A_=0.02						,					A_=True						,					A_=1						,					A_=2						,					A_=False						,					A_=0.0						,					A_=0.0						,					A_=1						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab_size
										# Backward compatibility with n_embed kwarg
										_UpperCAmelCase       :		str 		=    kwargs.pop("n_embed"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    hidden_size if n_embed is None else n_embed
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Optional[Any] 		=    n_head
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		List[str] 		=    pretraining_tp
										_UpperCAmelCase       :		List[Any] 		=    apply_residual_connection_post_layernorm
										_UpperCAmelCase       :		int 		=    hidden_dropout
										_UpperCAmelCase       :		List[str] 		=    attention_dropout

										_UpperCAmelCase       :		int 		=    bos_token_id
										_UpperCAmelCase       :		str 		=    eos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    slow_but_exact

										super().__init__(bos_token_id=A_						,					eos_token_id=A_						,					**A_	)







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.12"				)


						def __init__(       self						,					A_						,					A_ = "default"						,					A_ = None						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__(A_						,					task=A_						,					patching_specs=A_						,					use_past=A_	)
										if not getattr(self._config						,					"pad_token_id"						,					A_	):
														# TODO: how to do that better?
														_UpperCAmelCase       :		str 		=    0


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
										if self.use_past:
														# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
														self.fill_with_past_key_values_(A_						,					direction="inputs"						,					inverted_values_shape=A_	)
														_UpperCAmelCase       :		Optional[int] 		=    {0: "batch", 1: "past_sequence + sequence"}
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    {0: "batch", 1: "sequence"}

										return common_inputs


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_layer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_head


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-3


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = -1						,					A_ = -1						,					A_ = False						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    super(A_						,					self	).generate_dummy_inputs(
										    A_						,					batch_size=A_						,					seq_length=A_						,					is_pair=A_						,					framework=A_	)

										# We need to order the input in the way they appears in the forward()
										_UpperCAmelCase       :		Tuple 		=    OrderedDict({"input_ids": common_inputs["input_ids"]}	)

										# Need to add the past_keys
										if self.use_past:
														if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
														else:
																		import torch

																		_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    common_inputs["input_ids"].shape
																		# Not using the same length for past_key_values
																		_UpperCAmelCase       :		List[Any] 		=    seqlen + 2
																		_UpperCAmelCase       :		Dict 		=    self._config.hidden_size // self.num_attention_heads
																		_UpperCAmelCase       :		List[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    head_dim,
																		    past_key_values_length,
																		)
																		_UpperCAmelCase       :		Optional[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    past_key_values_length,
																		    head_dim,
																		)
																		_UpperCAmelCase       :		Tuple 		=    [
																		    (torch.zeros(A_	), torch.zeros(A_	)) for _ in range(self.num_layers	)
																		]

										_UpperCAmelCase       :		int 		=    common_inputs["attention_mask"]
										if self.use_past:
														_UpperCAmelCase       :		str 		=    ordered_inputs["attention_mask"].dtype
														_UpperCAmelCase       :		Optional[int] 		=    torch.cat(
														    [ordered_inputs["attention_mask"], torch.ones(A_						,					A_						,					dtype=A_	)]						,					dim=1	)

										return ordered_inputs





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 13


