"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      tempfile.mkdtemp()

      # fmt: off
      _UpperCAmelCase						 =      ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
      # fmt: on
      _UpperCAmelCase						 =      dict(zip(__lowerCAmelCase   ,				range(len(__lowerCAmelCase					)					)					)					)
      _UpperCAmelCase						 =      ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
      _UpperCAmelCase						 =      {'''unk_token''': '''<unk>'''}

      _UpperCAmelCase						 =      os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""vocab_file"""]					)
      _UpperCAmelCase						 =      os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""merges_file"""]					)
      with open(self.vocab_file   ,				"""w"""   ,				encoding="""utf-8"""					) as fp:
        fp.write(json.dumps(__lowerCAmelCase					) + """\n"""					)
      with open(self.merges_file   ,				"""w"""   ,				encoding="""utf-8"""					) as fp:
        fp.write("""\n""".join(__lowerCAmelCase					)					)

      _UpperCAmelCase						 =      {
          '''do_resize''': True,
          '''size''': 20,
          '''do_center_crop''': True,
          '''crop_size''': 18,
          '''do_normalize''': True,
          '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
          '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
      }
      _UpperCAmelCase						 =      os.path.join(self.tmpdirname   ,				__lowerCAmelCase					)
      with open(self.image_processor_file   ,				"""w"""   ,				encoding="""utf-8"""					) as fp:
        json.dump(__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				**__lowerCAmelCase	:  Tuple					):
      return CLIPTokenizer.from_pretrained(self.tmpdirname   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple   ,				**__lowerCAmelCase	:  Dict					):
      return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple   ,				**__lowerCAmelCase	:  List[Any]					):
      return CLIPImageProcessor.from_pretrained(self.tmpdirname   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str					):
      shutil.rmtree(self.tmpdirname					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      [np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta					)]

      _UpperCAmelCase						 =      [Image.fromarray(np.moveaxis(__lowerCAmelCase   ,				0   ,				-1					)					) for x in image_inputs]

      return image_inputs


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

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)
      processor_slow.save_pretrained(self.tmpdirname					)
      _UpperCAmelCase						 =      CLIPProcessor.from_pretrained(self.tmpdirname   ,				use_fast=__lowerCAmelCase					)

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)
      processor_fast.save_pretrained(self.tmpdirname					)
      _UpperCAmelCase						 =      CLIPProcessor.from_pretrained(self.tmpdirname					)

      self.assertEqual(processor_slow.tokenizer.get_vocab()   ,				tokenizer_slow.get_vocab()					)
      self.assertEqual(processor_fast.tokenizer.get_vocab()   ,				tokenizer_fast.get_vocab()					)
      self.assertEqual(tokenizer_slow.get_vocab()   ,				tokenizer_fast.get_vocab()					)
      self.assertIsInstance(processor_slow.tokenizer   ,				__lowerCAmelCase					)
      self.assertIsInstance(processor_fast.tokenizer   ,				__lowerCAmelCase					)

      self.assertEqual(processor_slow.image_processor.to_json_string()   ,				image_processor.to_json_string()					)
      self.assertEqual(processor_fast.image_processor.to_json_string()   ,				image_processor.to_json_string()					)
      self.assertIsInstance(processor_slow.image_processor   ,				__lowerCAmelCase					)
      self.assertIsInstance(processor_fast.image_processor   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)

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

      _UpperCAmelCase						 =      CLIPProcessor.from_pretrained(
          self.tmpdirname   ,				bos_token="""(BOS)"""   ,				eos_token="""(EOS)"""   ,				do_normalize=__lowerCAmelCase   ,				padding_value=1.0					)

      self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer   ,				__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      self.get_image_processor()
      _UpperCAmelCase						 =      self.get_tokenizer()

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)

      _UpperCAmelCase						 =      self.prepare_image_inputs()

      _UpperCAmelCase						 =      image_processor(__lowerCAmelCase   ,				return_tensors="""np"""					)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.get_image_processor()
      _UpperCAmelCase						 =      self.get_tokenizer()

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)

      _UpperCAmelCase						 =      '''lower newer'''

      _UpperCAmelCase						 =      processor(text=__lowerCAmelCase					)

      _UpperCAmelCase						 =      tokenizer(__lowerCAmelCase					)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]					)


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

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)

      _UpperCAmelCase						 =      '''lower newer'''
      _UpperCAmelCase						 =      self.prepare_image_inputs()

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

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

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


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

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)

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

      _UpperCAmelCase						 =      processor.batch_decode(__lowerCAmelCase					)
      _UpperCAmelCase						 =      tokenizer.batch_decode(__lowerCAmelCase					)

      self.assertListEqual(__lowerCAmelCase   ,				__lowerCAmelCase					)


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

      _UpperCAmelCase						 =      CLIPProcessor(tokenizer=__lowerCAmelCase   ,				image_processor=__lowerCAmelCase					)

      _UpperCAmelCase						 =      '''lower newer'''
      _UpperCAmelCase						 =      self.prepare_image_inputs()

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

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







"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  uuid.UUID = None   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Tuple=None					):
      if not conversation_id:
        _UpperCAmelCase						 =      uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase						 =      []
      if generated_responses is None:
        _UpperCAmelCase						 =      []

      _UpperCAmelCase						 =      conversation_id
      _UpperCAmelCase						 =      past_user_inputs
      _UpperCAmelCase						 =      generated_responses
      _UpperCAmelCase						 =      text


    def __eq__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  bool = False					):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''					)
          _UpperCAmelCase						 =      text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
      else:
        _UpperCAmelCase						 =      text


    def      lowerCAmelCase_  (					self	:  int					):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      self.generated_responses.append(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses					):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(					self	:  Optional[int]					):
      _UpperCAmelCase						 =      f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase						 =      """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_	,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,							)
class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  List[Any]   ,				**__lowerCAmelCase	:  Tuple					):
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase						 =      self.tokenizer.eos_token


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}

      if min_length_for_response is not None:
        _UpperCAmelCase						 =      min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase						 =      minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase						 =      generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase						 =      clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase					)
      return preprocess_params, forward_params, postprocess_params


    def __call__(					self	:  Tuple   ,				__lowerCAmelCase	:  Union[Conversation, List[Conversation]]   ,				__lowerCAmelCase	:  int=0   ,				**__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      super().__call__(__lowerCAmelCase   ,				num_workers=__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
        return outputs[0]
      return outputs


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Conversation   ,				__lowerCAmelCase	:  Optional[int]=32					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""					)
      if hasattr(self.tokenizer   ,				"""_build_conversation_input_ids"""					):
        _UpperCAmelCase						 =      self.tokenizer._build_conversation_input_ids(__lowerCAmelCase					)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase						 =      self._legacy_parse_and_tokenize(__lowerCAmelCase					)

      if self.framework == "pt":
        _UpperCAmelCase						 =      torch.LongTensor([input_ids]					)
      elif self.framework == "tf":
        _UpperCAmelCase						 =      tf.constant([input_ids]					)
      return {"input_ids": input_ids, "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]=10   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      generate_kwargs.get("""max_length"""   ,				self.model.config.max_length					)

      _UpperCAmelCase						 =      model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
        _UpperCAmelCase						 =      max_length - minimum_tokens
        _UpperCAmelCase						 =      model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase						 =      model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase						 =      model_inputs.pop("""conversation"""					)
      _UpperCAmelCase						 =      max_length
      _UpperCAmelCase						 =      self.model.generate(**__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase						 =      1
      else:
        _UpperCAmelCase						 =      n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]=True					):
      _UpperCAmelCase						 =      model_outputs["""output_ids"""]
      _UpperCAmelCase						 =      self.tokenizer.decode(
          output_ids[0]   ,				skip_special_tokens=__lowerCAmelCase   ,				clean_up_tokenization_spaces=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase					)
      return conversation


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Conversation					):
      _UpperCAmelCase						 =      self.tokenizer.eos_token_id
      _UpperCAmelCase						 =      []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [eos_token_id]					)
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					)					)

      if len(__lowerCAmelCase					) > self.tokenizer.model_max_length:
        _UpperCAmelCase						 =      input_ids[-self.tokenizer.model_max_length :]
      return input_ids
