
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   lowerCamelCase	(						unittest.TestCase						):
     UpperCAmelCase__       :							Tuple     =	ViTImageProcessor if is_vision_available() else None





     @property
     def 	UpperCAmelCase(self   :					Dict     )		->		Union[str, Any]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      (3, 3_2, 1_2_8)
         snake_case				  =      tempfile.mkdtemp()

         # fmt: off
         snake_case				  =      ['''[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
         snake_case				  =      dict(zip(__lowerCamelCase       ,					range(len(__lowerCamelCase     )     )     )     )

         snake_case				  =      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(__lowerCamelCase     ) + "\n"     )

         snake_case				  =      {
             '''do_normalize''': False,
             '''do_resize''': True,
             '''image_processor_type''': '''ViTImageProcessor''',
             '''resample''': 3,
             '''size''': {'''height''': 3_2, '''width''': 1_2_8},
         }
         snake_case				  =      os.path.join(self.tmpdirname       ,					__lowerCamelCase     )
         with open(self.image_processor_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             json.dump(__lowerCamelCase       ,					__lowerCamelCase     )





     def 	UpperCAmelCase(self   :					str       ,					**_A   :					Union[str, Any]     )		->		Union[str, Any]:
         return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,					**__lowerCamelCase     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					**_A   :					List[str]     )		->		List[Any]:
         return ViTImageProcessor.from_pretrained(self.tmpdirname       ,					**__lowerCamelCase     )





     def 	UpperCAmelCase(self   :					List[Any]     )		->		List[str]:
         shutil.rmtree(self.tmpdirname     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         snake_case				  =      np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta     )

         snake_case				  =      Image.fromarray(np.moveaxis(__lowerCamelCase       ,					0       ,					-1     )     )

         return image_input





     def 	UpperCAmelCase(self   :					Any     )		->		Dict:
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_image_processor()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )
         processor.save_pretrained(self.tmpdirname     )
         snake_case				  =      MgpstrProcessor.from_pretrained(self.tmpdirname       ,					use_fast=__lowerCamelCase     )

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

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





     def 	UpperCAmelCase(self   :					List[str]     )		->		Tuple:
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_image_processor()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )
         processor.save_pretrained(self.tmpdirname     )

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

         snake_case				  =      MgpstrProcessor.from_pretrained(
             self.tmpdirname       ,					bos_token="(BOS)"       ,					eos_token="(EOS)"       ,					do_normalize=__lowerCamelCase       ,					padding_value=1.0     )

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

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





     def 	UpperCAmelCase(self   :					int     )		->		Any:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

         snake_case				  =      self.prepare_image_inputs()

         snake_case				  =      image_processor(__lowerCamelCase       ,					return_tensors="np"     )
         snake_case				  =      processor(images=__lowerCamelCase       ,					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   :					Dict     )		->		Dict:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

         snake_case				  =      '''test'''

         snake_case				  =      processor(text=__lowerCamelCase     )

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





     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

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

         snake_case				  =      processor(text=__lowerCamelCase       ,					images=__lowerCamelCase     )

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

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

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

         snake_case				  =      processor.char_decode(__lowerCamelCase     )
         snake_case				  =      tokenizer.batch_decode(__lowerCamelCase     )
         snake_case				  =      [seq.replace(" "       ,					""     ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase       ,					__lowerCamelCase     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		Union[str, Any]:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

         snake_case				  =      None
         snake_case				  =      self.prepare_image_inputs()

         snake_case				  =      processor(text=__lowerCamelCase       ,					images=__lowerCamelCase     )

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





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()

         snake_case				  =      MgpstrProcessor(tokenizer=__lowerCamelCase       ,					image_processor=__lowerCamelCase     )

         snake_case				  =      torch.randn(1       ,					2_7       ,					3_8     )
         snake_case				  =      torch.randn(1       ,					2_7       ,					5_0_2_5_7     )
         snake_case				  =      torch.randn(1       ,					2_7       ,					3_0_5_2_2     )

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

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



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Dict     )		->		Dict:
         snake_case				  =      inspect.getfile(accelerate.test_utils     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
         snake_case				  =      os.path.sep.join(
             mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
         print(f'Command: {cmd}'     )
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,					cuda_visible_devices="0,1"     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )


if __name__ == "__main__":
   _A						      =				Accelerator()
   _A						      =				(accelerator.state.process_index + 2, 10)
   _A						      =				torch.randint(0, 10, shape).to(accelerator.device)

   _A						      =				""

   _A						      =				accelerator.pad_across_processes(tensor)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
      error_msg += "Padding was not done with the right value (0)."

   _A						      =				accelerator.pad_across_processes(tensor, pad_first=True)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   _A						      =				accelerator.state.num_processes - accelerator.state.process_index - 1
   if not torch.equal(tensora[index:], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[:index] == 0):
      error_msg += "Padding was not done with the right value (0)."

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)

