


"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch



@is_pipeline_test
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		lowerCamelCase__				=TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				TextaTextGenerationPipeline(model=a_				,       tokenizer=a_					)
			return generator, ["Something to write", "Something else"]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				generator('''Something there'''					)
			self.assertEqual(a_				,       [{'''generated_text''': ANY(a_					)}]					)
			# These are encoder decoder, they don't just append to incoming string
			self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''					)					)

			__snake_case	:						List[Any]						=				generator(['''This is great !''', '''Something else''']				,       num_return_sequences=2				,       do_sample=a_					)
			self.assertEqual(
			    a_				,       [
			        [{'''generated_text''': ANY(a_					)}, {'''generated_text''': ANY(a_					)}],
			        [{'''generated_text''': ANY(a_					)}, {'''generated_text''': ANY(a_					)}],
			    ]				,       )

			__snake_case	:						Optional[int]						=				generator(
			    ['''This is great !''', '''Something else''']				,       num_return_sequences=2				,       batch_size=2				,       do_sample=a_					)
			self.assertEqual(
			    a_				,       [
			        [{'''generated_text''': ANY(a_					)}, {'''generated_text''': ANY(a_					)}],
			        [{'''generated_text''': ANY(a_					)}, {'''generated_text''': ANY(a_					)}],
			    ]				,       )

			with self.assertRaises(a_					):
				generator(4					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				pipeline('''text2text-generation'''				,       model='''patrickvonplaten/t5-tiny-random'''				,       framework='''pt'''					)
			# do_sample=False necessary for reproducibility
			__snake_case	:						int						=				generator('''Something there'''				,       do_sample=a_					)
			self.assertEqual(a_				,       [{'''generated_text''': ''''''}]					)

			__snake_case	:						Optional[int]						=				3
			__snake_case	:						int						=				generator(
			    '''Something there'''				,       num_return_sequences=a_				,       num_beams=a_				,       )
			__snake_case	:						Dict						=				[
			    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
			    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
			    {'''generated_text''': ''''''},
			]
			self.assertEqual(a_				,       a_					)

			__snake_case	:						List[Any]						=				generator('''This is a test'''				,       do_sample=a_				,       num_return_sequences=2				,       return_tensors=a_					)
			self.assertEqual(
			    a_				,       [
			        {'''generated_token_ids''': ANY(torch.Tensor					)},
			        {'''generated_token_ids''': ANY(torch.Tensor					)},
			    ]				,       )
			__snake_case	:						Dict						=				generator.model.config.eos_token_id
			__snake_case	:						Any						=				'''<pad>'''
			__snake_case	:						Tuple						=				generator(
			    ['''This is a test''', '''This is a second test''']				,       do_sample=a_				,       num_return_sequences=2				,       batch_size=2				,       return_tensors=a_				,       )
			self.assertEqual(
			    a_				,       [
			        [
			            {'''generated_token_ids''': ANY(torch.Tensor					)},
			            {'''generated_token_ids''': ANY(torch.Tensor					)},
			        ],
			        [
			            {'''generated_token_ids''': ANY(torch.Tensor					)},
			            {'''generated_token_ids''': ANY(torch.Tensor					)},
			        ],
			    ]				,       )



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				pipeline('''text2text-generation'''				,       model='''patrickvonplaten/t5-tiny-random'''				,       framework='''tf'''					)
			# do_sample=False necessary for reproducibility
			__snake_case	:						Union[str, Any]						=				generator('''Something there'''				,       do_sample=a_					)
			self.assertEqual(a_				,       [{'''generated_text''': ''''''}]					)










'''simple docstring'''




# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCamelCase							:str				    =					TypeVar('''T''')







class 	_lowerCAmelCase							(			Generic[T] ):


	def __init__(self       ,      lowercase = True							):
				A_	:						dict[T, list[T]]	=     {}  # dictionary of lists
				A_	:						Any	=     directed


	def     _a	(self       ,      lowercase       ,      lowercase							):

				if not self.directed:  # For undirected graphs
							# if both source vertex and destination vertex are both present in the
							# adjacency list, add destination vertex to source vertex list of adjacent
							# vertices and add source vertex to destination vertex list of adjacent
							# vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										self.adj_list[destination_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination vertex
							# to source vertex list of adjacent vertices, then create a new vertex with
							# destination vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Dict	=     [source_vertex]
							# if only destination vertex is present in adjacency list, add source vertex
							# to destination vertex list of adjacent vertices, then create a new vertex
							# with source vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif destination_vertex in self.adj_list:
										self.adj_list[destination_vertex].append(lowercase							)
										A_	:						int	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and assign a list
							# containing the destination vertex as it's first adjacent vertex also
							# create a new vertex with destination vertex as key and assign a list
							# containing the source vertex as it's first adjacent vertex.
							else:
										A_	:						Optional[Any]	=     [destination_vertex]
										A_	:						Tuple	=     [source_vertex]
				else:  # For directed graphs
							# if both source vertex and destination vertex are present in adjacency
							# list, add destination vertex to source vertex list of adjacent vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination
							# vertex to source vertex list of adjacent vertices and create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Tuple	=     []
							# if only destination vertex is present in adjacency list, create a new
							# vertex with source vertex as key and assign a list containing destination
							# vertex as first adjacent vertex
							elif destination_vertex in self.adj_list:
										A_	:						Tuple	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and a list containing
							# destination vertex as it's first adjacent vertex. Then create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							else:
										A_	:						int	=     [destination_vertex]
										A_	:						List[str]	=     []

				return self


	def __repr__(self							):
				return pformat(self.adj_list							)