



'''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


snake_case_     =	logging.get_logger(__name__)


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if not conversation_id:
           A       :       int							=						uuid.uuida()
       if past_user_inputs is None:
           A       :       str							=						[]
       if generated_responses is None:
           A       :       int							=						[]

       A       :       uuid.UUID							=						conversation_id
       A       :       List[str]							=						past_user_inputs
       A       :       List[str]							=						generated_responses
       A       :       Optional[str]							=						text


   def __eq__( self    ,   _UpperCAmelCase					):
       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           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 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 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}".'''					)
               A       :       Optional[Any]							=						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:
           A       :       Union[str, Any]							=						text


   def 					snake_case				( self					):
       if self.new_user_input:
           self.past_user_inputs.append(self.new_user_input					)
       A       :       Any							=						None


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       self.generated_responses.append(_UpperCAmelCase					)


   def 					snake_case				( self					):
       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					):
       A       :       Any							=						f'''Conversation id: {self.uuid} \n'''
       for is_user, text in self.iter_texts():
           A       :       int							=						'''user''' if is_user else '''bot'''
           output += f'''{name} >> {text} \n'''
       return output







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


   def __init__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       super().__init__(*_UpperCAmelCase    ,   **_UpperCAmelCase					)
       if self.tokenizer.pad_token_id is None:
           A       :       Tuple							=						self.tokenizer.eos_token


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Union[str, Any]							=						{}
       A       :       Optional[int]							=						{}
       A       :       int							=						{}

       if min_length_for_response is not None:
           A       :       List[str]							=						min_length_for_response
       if minimum_tokens is not None:
           A       :       Any							=						minimum_tokens

       if "max_length" in generate_kwargs:
           A       :       str							=						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:
           A       :       Optional[Any]							=						clean_up_tokenization_spaces

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


   def __call__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0    ,   **_UpperCAmelCase					):
       A       :       Any							=						super().__call__(_UpperCAmelCase    ,   num_workers=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) and len(_UpperCAmelCase					) == 1:
           return outputs[0]
       return outputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=32					):
       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           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'''					):
           A       :       Any							=						self.tokenizer._build_conversation_input_ids(_UpperCAmelCase					)
       else:
           # If the tokenizer cannot handle conversations, we default to only the old version
           A       :       Optional[int]							=						self._legacy_parse_and_tokenize(_UpperCAmelCase					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=10    ,   **_UpperCAmelCase					):
       A       :       Optional[Any]							=						generate_kwargs.get('''max_length'''    ,   self.model.config.max_length					)

       A       :       str							=						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})'''					)
           A       :       int							=						max_length - minimum_tokens
           A       :       Optional[int]							=						model_inputs['''input_ids'''][:, -trim:]
           if "attention_mask" in model_inputs:
               A       :       str							=						model_inputs['''attention_mask'''][:, -trim:]
       A       :       Tuple							=						model_inputs.pop('''conversation'''					)
       A       :       Optional[int]							=						max_length
       A       :       Dict							=						self.model.generate(**_UpperCAmelCase    ,   **_UpperCAmelCase					)
       if self.model.config.is_encoder_decoder:
           A       :       int							=						1
       else:
           A       :       Any							=						n
       return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=True					):
       A       :       Union[str, Any]							=						model_outputs['''output_ids''']
       A       :       Tuple							=						self.tokenizer.decode(
           output_ids[0]    ,   skip_special_tokens=_UpperCAmelCase    ,   clean_up_tokenization_spaces=_UpperCAmelCase    ,   )
       A       :       List[str]							=						model_outputs['''conversation''']
       conversation.mark_processed()
       conversation.append_response(_UpperCAmelCase					)
       return conversation


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

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







'''simple docstring'''






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


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       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((512, 512)		)		)
        elif input_type == "audio":
            inputs.append(torch.ones(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

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

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

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

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       # There is a single output
       if len(self.tool.outputs					) == 1:
           A       :       Optional[int]							=						[outputs]

       self.assertListEqual(output_types(_UpperCAmelCase					)    ,   self.tool.outputs					)


   def 					snake_case				( self					):
       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 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

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

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       for _input, input_type in zip(_UpperCAmelCase    ,   self.tool.inputs					):
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               _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       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

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