"""simple docstring"""

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
		from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image







class      UpperCAmelCase_       :






							def __init__(		self :      Optional[Any]     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      List[str]=3     ,						__UpperCamelCase :      Union[str, Any]=32     ,						__UpperCamelCase :      Optional[Any]=3     ,						__UpperCamelCase :      Any=10     ,						__UpperCamelCase :      Dict=[8, 16, 32, 64]     ,						__UpperCamelCase :      Optional[Any]=[1, 1, 2, 1]     ,						__UpperCamelCase :      int=True     ,						__UpperCamelCase :      Tuple=True     ,						__UpperCamelCase :      Any="relu"     ,						__UpperCamelCase :      List[Any]=3     ,						__UpperCamelCase :      Union[str, Any]=None     ,						__UpperCamelCase :      Optional[Any]=["stage2", "stage3", "stage4"]     ,						__UpperCamelCase :      int=[2, 3, 4]     ,						__UpperCamelCase :      List[str]=1     ,						)       ->       Tuple:
												_UpperCamelCase      						=	parent
												_UpperCamelCase      						=	batch_size
												_UpperCamelCase      						=	image_size
												_UpperCamelCase      						=	num_channels
												_UpperCamelCase      						=	embeddings_size
												_UpperCamelCase      						=	hidden_sizes
												_UpperCamelCase      						=	depths
												_UpperCamelCase      						=	is_training
												_UpperCamelCase      						=	use_labels
												_UpperCamelCase      						=	hidden_act
												_UpperCamelCase      						=	num_labels
												_UpperCamelCase      						=	scope
												_UpperCamelCase      						=	len(__UpperCamelCase  )
												_UpperCamelCase      						=	out_features
												_UpperCamelCase      						=	out_indices
												_UpperCamelCase      						=	num_groups






							def       _UpperCamelCase			(		self :      str  )       ->       Tuple:
												_UpperCamelCase      						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

												_UpperCamelCase      						=	None
												if self.use_labels:
																	_UpperCamelCase      						=	ids_tensor([self.batch_size]     ,						self.num_labels  )

												_UpperCamelCase      						=	self.get_config()

												return config, pixel_values, labels






							def       _UpperCamelCase			(		self :      List[str]  )       ->       Optional[Any]:
												return BitConfig(
												    num_channels=self.num_channels     ,						embeddings_size=self.embeddings_size     ,						hidden_sizes=self.hidden_sizes     ,						depths=self.depths     ,						hidden_act=self.hidden_act     ,						num_labels=self.num_labels     ,						out_features=self.out_features     ,						out_indices=self.out_indices     ,						num_groups=self.num_groups     ,						)






							def       _UpperCamelCase			(		self :      Any     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Union[str, Any]  )       ->       Any:
												_UpperCamelCase      						=	BitModel(config=__UpperCamelCase  )
												model.to(__UpperCamelCase  )
												model.eval()
												_UpperCamelCase      						=	model(__UpperCamelCase  )
												self.parent.assertEqual(
												    result.last_hidden_state.shape     ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,						)






							def       _UpperCamelCase			(		self :      Dict     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Dict  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	self.num_labels
												_UpperCamelCase      						=	BitForImageClassification(__UpperCamelCase  )
												model.to(__UpperCamelCase  )
												model.eval()
												_UpperCamelCase      						=	model(__UpperCamelCase     ,						labels=__UpperCamelCase  )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)  )






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      int  )       ->       Optional[Any]:
												_UpperCamelCase      						=	BitBackbone(config=__UpperCamelCase  )
												model.to(__UpperCamelCase  )
												model.eval()
												_UpperCamelCase      						=	model(__UpperCamelCase  )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps  )     ,						len(config.out_features  )  )
												self.parent.assertListEqual(list(result.feature_maps[0].shape  )     ,						[self.batch_size, self.hidden_sizes[1], 4, 4]  )

												# verify channels
												self.parent.assertEqual(len(model.channels  )     ,						len(config.out_features  )  )
												self.parent.assertListEqual(model.channels     ,						config.hidden_sizes[1:]  )

												# verify backbone works with out_features=None
												_UpperCamelCase      						=	None
												_UpperCamelCase      						=	BitBackbone(config=__UpperCamelCase  )
												model.to(__UpperCamelCase  )
												model.eval()
												_UpperCamelCase      						=	model(__UpperCamelCase  )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps  )     ,						1  )
												self.parent.assertListEqual(list(result.feature_maps[0].shape  )     ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]  )

												# verify channels
												self.parent.assertEqual(len(model.channels  )     ,						1  )
												self.parent.assertListEqual(model.channels     ,						[config.hidden_sizes[-1]]  )






							def       _UpperCamelCase			(		self :      Dict  )       ->       str:
												_UpperCamelCase      						=	self.prepare_config_and_inputs()
												_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase      						=	config_and_inputs
												_UpperCamelCase      						=	{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class      UpperCAmelCase_       (						lowercase_			,							lowercase_			,							unittest.TestCase):
							snake_case__        =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
							snake_case__        =      (
							    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
							    if is_torch_available()
							    else {}
							)

							snake_case__        =      False
							snake_case__        =      False
							snake_case__        =      False
							snake_case__        =      False
							snake_case__        =      False






							def       _UpperCamelCase			(		self :      List[str]  )       ->       str:
												_UpperCamelCase      						=	BitModelTester(self  )
												_UpperCamelCase      						=	ConfigTester(self     ,						config_class=__UpperCamelCase     ,						has_text_modality=__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Union[str, Any]:
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()






							def       _UpperCamelCase			(		self :      List[Any]  )       ->       Tuple:
												return






							@unittest.skip(reason='''Bit does not output attentions'''  )
							def       _UpperCamelCase			(		self :      Any  )       ->       Optional[Any]:
												pass






							@unittest.skip(reason='''Bit does not use inputs_embeds'''  )
							def       _UpperCamelCase			(		self :      Dict  )       ->       List[str]:
												pass






							@unittest.skip(reason='''Bit does not support input and output embeddings'''  )
							def       _UpperCamelCase			(		self :      Any  )       ->       Tuple:
												pass






							def       _UpperCamelCase			(		self :      List[Any]  )       ->       Union[str, Any]:
												_UpperCamelCase						,							_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase      						=	model_class(__UpperCamelCase  )
																	_UpperCamelCase      						=	inspect.signature(model.forward  )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	_UpperCamelCase      						=	[*signature.parameters.keys()]

																	_UpperCamelCase      						=	['''pixel_values''']
																	self.assertListEqual(arg_names[:1]     ,						__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      List[Any]  )       ->       str:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      Optional[int]  )       ->       str:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      Optional[int]  )       ->       Tuple:
												_UpperCamelCase						,							_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase      						=	model_class(config=__UpperCamelCase  )
																	for name, module in model.named_modules():
																						if isinstance(__UpperCamelCase     ,						(nn.BatchNormad, nn.GroupNorm)  ):
																											self.assertTrue(
																											    torch.all(module.weight == 1  )     ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,						)
																											self.assertTrue(
																											    torch.all(module.bias == 0  )     ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,						)






							def       _UpperCamelCase			(		self :      Tuple  )       ->       Optional[int]:
												def check_hidden_states_output(__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Union[str, Any]  ):
																	_UpperCamelCase      						=	model_class(__UpperCamelCase  )
																	model.to(__UpperCamelCase  )
																	model.eval()

																	with torch.no_grad():
																						_UpperCamelCase      						=	model(**self._prepare_for_class(__UpperCamelCase     ,						__UpperCamelCase  )  )

																	_UpperCamelCase      						=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	_UpperCamelCase      						=	self.model_tester.num_stages
																	self.assertEqual(len(__UpperCamelCase  )     ,						expected_num_stages + 1  )

																	# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]  )     ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,						)

												_UpperCamelCase						,							_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs_for_common()
												_UpperCamelCase      						=	['''preactivation''', '''bottleneck''']
												for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																						_UpperCamelCase      						=	layer_type
																						_UpperCamelCase      						=	True
																						check_hidden_states_output(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase  )

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						_UpperCamelCase      						=	True

																						check_hidden_states_output(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase  )






							@unittest.skip(reason='''Bit does not use feedforward chunking'''  )
							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Union[str, Any]:
												pass






							def       _UpperCamelCase			(		self :      Dict  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase  )






							@slow
							def       _UpperCamelCase			(		self :      Tuple  )       ->       Any:
												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase      						=	BitModel.from_pretrained(__UpperCamelCase  )
																	self.assertIsNotNone(__UpperCamelCase  )






def      lowercase		(						)						->   Dict:
					_UpperCamelCase      						=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
					return image







@require_torch
@require_vision
class      UpperCAmelCase_       (						unittest.TestCase):






							@cached_property
							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Optional[int]:
												return (
												    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ) if is_vision_available() else None
												)






							@slow
							def       _UpperCamelCase			(		self :      List[str]  )       ->       Tuple:
												_UpperCamelCase      						=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__UpperCamelCase  )

												_UpperCamelCase      						=	self.default_image_processor
												_UpperCamelCase      						=	prepare_img()
												_UpperCamelCase      						=	image_processor(images=__UpperCamelCase     ,						return_tensors='''pt'''  ).to(__UpperCamelCase  )

												# forward pass
												with torch.no_grad():
																	_UpperCamelCase      						=	model(**__UpperCamelCase  )

												# verify the logits
												_UpperCamelCase      						=	torch.Size((1, 1000)  )
												self.assertEqual(outputs.logits.shape     ,						__UpperCamelCase  )

												_UpperCamelCase      						=	torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]  ).to(__UpperCamelCase  )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,						__UpperCamelCase     ,						atol=1E-4  )  )







@require_torch
class      UpperCAmelCase_       (						lowercase_			,							unittest.TestCase):
							snake_case__        =      (BitBackbone,) if is_torch_available() else ()
							snake_case__        =      BitConfig

							snake_case__        =      False






							def       _UpperCamelCase			(		self :      List[str]  )       ->       Dict:
												_UpperCamelCase      						=	BitModelTester(self  )



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		:   Dict										=       logging.get_logger(__name__)







class  lowercase				:




				def __init__(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   snake_case=None       ):
											if not conversation_id:
																		snake_case_							=    uuid.uuida()
											if past_user_inputs is None:
																		snake_case_							=    []
											if generated_responses is None:
																		snake_case_							=    []

											snake_case_							=    conversation_id
											snake_case_							=    past_user_inputs
											snake_case_							=    generated_responses
											snake_case_							=    text




				def __eq__(					self      ,   snake_case       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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     a							(					self      ,   snake_case      ,   snake_case = 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}".'''       )
																									snake_case_							=    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:
																		snake_case_							=    text




				def     a							(					self       ):
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											snake_case_							=    None




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




				def     a							(					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       ):
											snake_case_							=    F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		snake_case_							=    'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output







@add_end_docstrings(
    lowercase_	,  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				(       lowercase_			):




				def __init__(					self      ,   *snake_case      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )
											if self.tokenizer.pad_token_id is None:
																		snake_case_							=    self.tokenizer.eos_token




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    {}
											snake_case_							=    {}

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

											if "max_length" in generate_kwargs:
																		snake_case_							=    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:
																		snake_case_							=    clean_up_tokenization_spaces

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




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




				def     a							(					self      ,   snake_case      ,   snake_case=32       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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'       ):
																		snake_case_							=    self.tokenizer._build_conversation_input_ids(snake_case       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		snake_case_							=    self._legacy_parse_and_tokenize(snake_case       )

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




				def     a							(					self      ,   snake_case      ,   snake_case=10      ,   **snake_case       ):
											snake_case_							=    generate_kwargs.get('max_length'      ,   self.model.config.max_length       )

											snake_case_							=    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})'''       )
																		snake_case_							=    max_length - minimum_tokens
																		snake_case_							=    model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									snake_case_							=    model_inputs['attention_mask'][:, -trim:]
											snake_case_							=    model_inputs.pop('conversation'       )
											snake_case_							=    max_length
											snake_case_							=    self.model.generate(**snake_case      ,   **snake_case       )
											if self.model.config.is_encoder_decoder:
																		snake_case_							=    1
											else:
																		snake_case_							=    n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=True       ):
											snake_case_							=    model_outputs['output_ids']
											snake_case_							=    self.tokenizer.decode(
											    output_ids[0]      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   )
											snake_case_							=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(snake_case       )
											return conversation




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

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




