
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 		UpperCamelCase__  (       unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      ViTImageProcessor if is_vision_available() else None
   @property
   def       lowerCAmelCase			(self :					Optional[int]   ):
    return self.image_processor_tester.prepare_image_processor_dict()
   def       lowerCAmelCase			(self :					List[str]   ):
    __a :  Optional[int]						=  (3, 3_2, 1_2_8)
    __a :  Optional[Any]						=  tempfile.mkdtemp()

    # fmt: off
    __a :  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
    __a :  int						=  dict(zip(snake_case_					,				range(len(snake_case_   )   )   )   )

    __a :  Tuple						=  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(snake_case_   ) + '''\n'''   )

    __a :  Dict						=  {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    __a :  List[str]						=  os.path.join(self.tmpdirname					,				snake_case_   )
    with open(self.image_processor_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
     json.dump(snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				**snake_case_ :					Any   ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				**snake_case_ :					Tuple   ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Dict   ):
    shutil.rmtree(self.tmpdirname   )
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  np.random.randint(2_5_5					,				size=(3, 3_0, 4_0_0)					,				dtype=np.uinta   )

    __a :  Dict						=  Image.fromarray(np.moveaxis(snake_case_					,				0					,				-1   )   )

    return image_input
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.get_tokenizer()
    __a :  int						=  self.get_image_processor()

    __a :  Optional[int]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )
    processor.save_pretrained(self.tmpdirname   )
    __a :  Dict						=  MgpstrProcessor.from_pretrained(self.tmpdirname					,				use_fast=snake_case_   )

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

    self.assertEqual(processor.image_processor.to_json_string()					,				image_processor.to_json_string()   )
    self.assertIsInstance(processor.image_processor					,				snake_case_   )
   def       lowerCAmelCase			(self :					List[str]   ):
    __a :  Optional[Any]						=  self.get_tokenizer()
    __a :  str						=  self.get_image_processor()

    __a :  Union[str, Any]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )
    processor.save_pretrained(self.tmpdirname   )

    __a :  Tuple						=  self.get_tokenizer(bos_token='''(BOS)'''					,				eos_token='''(EOS)'''   )
    __a :  Optional[Any]						=  self.get_image_processor(do_normalize=snake_case_					,				padding_value=1.0   )

    __a :  str						=  MgpstrProcessor.from_pretrained(
        self.tmpdirname					,				bos_token='''(BOS)'''					,				eos_token='''(EOS)'''					,				do_normalize=snake_case_					,				padding_value=1.0   )

    self.assertEqual(processor.char_tokenizer.get_vocab()					,				tokenizer_add_kwargs.get_vocab()   )
    self.assertIsInstance(processor.char_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       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[int]						=  self.get_image_processor()
    __a :  int						=  self.get_tokenizer()

    __a :  Union[str, Any]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  Optional[Any]						=  self.prepare_image_inputs()

    __a :  Any						=  image_processor(snake_case_					,				return_tensors='''np'''   )
    __a :  str						=  processor(images=snake_case_					,				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       lowerCAmelCase			(self :					List[str]   ):
    __a :  Any						=  self.get_image_processor()
    __a :  Dict						=  self.get_tokenizer()

    __a :  List[Any]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  Optional[int]						=  '''test'''

    __a :  str						=  processor(text=snake_case_   )

    __a :  Any						=  tokenizer(snake_case_   )
    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]					,				encoded_processor[key]   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[Any]						=  self.get_image_processor()
    __a :  Any						=  self.get_tokenizer()

    __a :  Dict						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  Dict						=  '''test'''
    __a :  Union[str, Any]						=  self.prepare_image_inputs()

    __a :  List[str]						=  processor(text=snake_case_					,				images=snake_case_   )

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

    # test if it raises when no input is passed
    with pytest.raises(snake_case_   ):
     processor()
   def       lowerCAmelCase			(self :					str   ):
    __a :  Optional[int]						=  self.get_image_processor()
    __a :  List[str]						=  self.get_tokenizer()

    __a :  Union[str, Any]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  List[Any]						=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    __a :  str						=  processor.char_decode(snake_case_   )
    __a :  List[str]						=  tokenizer.batch_decode(snake_case_   )
    __a :  Dict						=  [seq.replace(''' '''					,				''''''   ) for seq in decoded_tok]

    self.assertListEqual(snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  List[str]						=  self.get_image_processor()
    __a :  Union[str, Any]						=  self.get_tokenizer()

    __a :  Tuple						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  Optional[Any]						=  None
    __a :  int						=  self.prepare_image_inputs()

    __a :  Any						=  processor(text=snake_case_					,				images=snake_case_   )

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







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  str						=  self.get_image_processor()
    __a :  List[Any]						=  self.get_tokenizer()

    __a :  List[str]						=  MgpstrProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

    __a :  int						=  torch.randn(1					,				2_7					,				3_8   )
    __a :  Any						=  torch.randn(1					,				2_7					,				5_0_2_5_7   )
    __a :  Optional[Any]						=  torch.randn(1					,				2_7					,				3_0_5_2_2   )

    __a :  List[str]						=  processor.batch_decode([char_input, bpe_input, wp_input]   )

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


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

 for input_type in input_types:
  if input_type == "text":
   inputs.append('''Text input'''  )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''  )  ) / '''000000039769.png'''  ).resize((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

 for output in outputs:
  if isinstance(lowerCAmelCase__  ,   (str, AgentText)  ):
   output_types.append('''text'''  )
  elif isinstance(lowerCAmelCase__  ,   (Image.Image, AgentImage)  ):
   output_types.append('''image'''  )
  elif isinstance(lowerCAmelCase__  ,   (torch.Tensor, AgentAudio)  ):
   output_types.append('''audio'''  )
  else:
   raise ValueError(f"Invalid output: {output}"  )

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

    # There is a single output
    if len(self.tool.outputs   ) == 1:
     __a :  Tuple						=  [outputs]

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    self.assertTrue(hasattr(self.tool					,				'''description'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''default_checkpoint'''   )   )
    self.assertTrue(self.tool.description.startswith('''This is a tool that'''   )   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				snake_case_   ):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )
