'''simple docstring'''

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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					ViTImageProcessor if is_vision_available() else None
 @property
 def a					(      self   :	Optional[int]							)  ->    Optional[Any]:
  return self.image_processor_tester.prepare_image_processor_dict()
 def a					(      self   :	List[str]							)  ->    Optional[int]:
  __lowerCAmelCase											=						(3, 32, 1_28)
  __lowerCAmelCase											=						tempfile.mkdtemp()

  # fmt: off
  __lowerCAmelCase											=						['''[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
  __lowerCAmelCase											=						dict(zip(snake_case_      ,       range(len(snake_case_							)							)							)							)

  __lowerCAmelCase											=						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"""							)

  __lowerCAmelCase											=						{
      '''do_normalize''': False,
      '''do_resize''': True,
      '''image_processor_type''': '''ViTImageProcessor''',
      '''resample''': 3,
      '''size''': {'''height''': 32, '''width''': 1_28},
  }
  __lowerCAmelCase											=						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 a					(      self   :	Any      ,       **SCREAMING_SNAKE_CASE__   :	Any							)  ->    str:
  return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **snake_case_							)
 def a					(      self   :	Union[str, Any]      ,       **SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Any:
  return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **snake_case_							)
 def a					(      self   :	Dict							)  ->    List[Any]:
  shutil.rmtree(self.tmpdirname							)
 def a					(      self   :	Optional[Any]							)  ->    str:
  __lowerCAmelCase											=						np.random.randint(2_55      ,       size=(3, 30, 4_00)      ,       dtype=np.uinta							)

  __lowerCAmelCase											=						Image.fromarray(np.moveaxis(snake_case_      ,       0      ,       -1							)							)

  return image_input
 def a					(      self   :	Optional[Any]							)  ->    str:
  __lowerCAmelCase											=						self.get_tokenizer()
  __lowerCAmelCase											=						self.get_image_processor()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)
  processor.save_pretrained(self.tmpdirname							)
  __lowerCAmelCase											=						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 a					(      self   :	List[str]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.get_tokenizer()
  __lowerCAmelCase											=						self.get_image_processor()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)
  processor.save_pretrained(self.tmpdirname							)

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

  __lowerCAmelCase											=						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 a					(      self   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

  __lowerCAmelCase											=						self.prepare_image_inputs()

  __lowerCAmelCase											=						image_processor(snake_case_      ,       return_tensors="""np"""							)
  __lowerCAmelCase											=						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 a					(      self   :	List[str]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

  __lowerCAmelCase											=						'''test'''

  __lowerCAmelCase											=						processor(text=snake_case_							)

  __lowerCAmelCase											=						tokenizer(snake_case_							)
  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key]							)
 def a					(      self   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

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

  __lowerCAmelCase											=						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 a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

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

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

  self.assertListEqual(snake_case_      ,       snake_case_							)
 def a					(      self   :	List[Any]							)  ->    Dict:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

  __lowerCAmelCase											=						None
  __lowerCAmelCase											=						self.prepare_image_inputs()

  __lowerCAmelCase											=						processor(text=snake_case_      ,       images=snake_case_							)

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





 def a					(      self   :	Optional[int]							)  ->    int:
  __lowerCAmelCase											=						self.get_image_processor()
  __lowerCAmelCase											=						self.get_tokenizer()

  __lowerCAmelCase											=						MgpstrProcessor(tokenizer=snake_case_      ,       image_processor=snake_case_							)

  __lowerCAmelCase											=						torch.randn(1      ,       27      ,       38							)
  __lowerCAmelCase											=						torch.randn(1      ,       27      ,       5_02_57							)
  __lowerCAmelCase											=						torch.randn(1      ,       27      ,       3_05_22							)

  __lowerCAmelCase											=						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   )   )
