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 UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase = None		,     lowercase = None		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if not conversation_id:
								A_		:    str         =				uuid.uuida()
						if past_user_inputs is None:
								A_		:    int         =				[]
						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		,     lowercase ):



						"""simple docstring"""





						if not isinstance(lowercase		,     lowercase ):
								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		,     lowercase		,     lowercase = False ):



						"""simple docstring"""





						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_		:    List[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_		:    Any         =				text
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						if self.new_user_input:
								self.past_user_inputs.append(self.new_user_input )
						A_		:    List[str]         =				None
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						self.generated_responses.append(lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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 ):



						"""simple docstring"""





						A_		:    str         =				F'''Conversation id: {self.uuid} \n'''
						for is_user, text in self.iter_texts():
								A_		:    Dict         =				'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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						super().__init__(*lowercase		,     **lowercase )
						if self.tokenizer.pad_token_id is None:
								A_		:    Tuple         =				self.tokenizer.eos_token
				def   lowerCAmelCase_       (						self		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     **lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				{}
						A_		:    Dict         =				{}
						A_		:    List[str]         =				{}

						if min_length_for_response is not None:
								A_		:    int         =				min_length_for_response
						if minimum_tokens is not None:
								A_		:    List[str]         =				minimum_tokens

						if "max_length" in generate_kwargs:
								A_		:    Union[str, Any]         =				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_		:    int         =				clean_up_tokenization_spaces

						if generate_kwargs:
								forward_params.update(lowercase )
						return preprocess_params, forward_params, postprocess_params
				def __call__(						self		,     lowercase		,     lowercase=0		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				super().__call__(lowercase		,     num_workers=lowercase		,     **lowercase )
						if isinstance(lowercase		,     lowercase ) and len(lowercase ) == 1:
								return outputs[0]
						return outputs
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=3_2 ):



						"""simple docstring"""





						if not isinstance(lowercase		,     lowercase ):
								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_		:    int         =				self.tokenizer._build_conversation_input_ids(lowercase )
						else:
								# If the tokenizer cannot handle conversations, we default to only the old version
								A_		:    int         =				self._legacy_parse_and_tokenize(lowercase )

						if self.framework == "pt":
								A_		:    int         =				torch.LongTensor([input_ids] )
						elif self.framework == "tf":
								A_		:    Dict         =				tf.constant([input_ids] )
						return {"input_ids": input_ids, "conversation": conversation}
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=1_0		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				generate_kwargs.get('max_length'		,     self.model.config.max_length )

						A_		:    Union[str, Any]         =				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_		:    List[Any]         =				max_length - minimum_tokens
								A_		:    int         =				model_inputs['input_ids'][:, -trim:]
								if "attention_mask" in model_inputs:
										A_		:    List[str]         =				model_inputs['attention_mask'][:, -trim:]
						A_		:    Union[str, Any]         =				model_inputs.pop('conversation' )
						A_		:    Union[str, Any]         =				max_length
						A_		:    Union[str, Any]         =				self.model.generate(**lowercase		,     **lowercase )
						if self.model.config.is_encoder_decoder:
								A_		:    str         =				1
						else:
								A_		:    Optional[Any]         =				n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=True ):



						"""simple docstring"""





						A_		:    Tuple         =				model_outputs['output_ids']
						A_		:    Optional[int]         =				self.tokenizer.decode(
						    output_ids[0]		,     skip_special_tokens=lowercase		,     clean_up_tokenization_spaces=lowercase		,     )
						A_		:    List[Any]         =				model_outputs['conversation']
						conversation.mark_processed()
						conversation.append_response(lowercase )
						return conversation







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				self.tokenizer.eos_token_id
						A_		:    str         =				[]
						for is_user, text in conversation.iter_texts():
								if eos_token_id is not None:
										input_ids.extend(self.tokenizer.encode(lowercase		,     add_special_tokens=lowercase ) + [eos_token_id] )
								else:
										input_ids.extend(self.tokenizer.encode(lowercase		,     add_special_tokens=lowercase ) )

						if len(lowercase ) > self.tokenizer.model_max_length:
								A_		:    str         =				input_ids[-self.tokenizer.model_max_length :]
						return input_ids







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase            =		{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		["""BridgeTowerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BridgeTowerForContrastiveLearning""",
	    """BridgeTowerForImageAndTextRetrieval""",
	    """BridgeTowerForMaskedLM""",
	    """BridgeTowerModel""",
	    """BridgeTowerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






